*How can we verify whether copyrighted content was used to train a large vision-language model (VLM) without direct access to its training data?* Motivated by the hypothesis that a VLM is able to recognize images from its training corpus, we propose DIS-CO, a novel approach to infer the inclusion of copyrighted content during the model's development. By repeatedly querying a VLM with specific frames from targeted copyrighted material, DIS-CO extracts the content's identity through free-form text completions. To assess its effectiveness, we introduce MovieTection, a benchmark comprising 14,000 frames paired with detailed captions, drawn from films released both before and after a model’s training cutoff. Our results show that DIS-CO significantly improves detection performance, nearly doubling the average AUC of the best prior method on models with logits available. Our findings also highlight a broader concern: all tested models appear to have been exposed to some extent to copyrighted content. We provide the code in the supplementary materials.

Convolutional neural networks (CNNs) trained on object recognition achieve high task performance but continue to exhibit vulnerability under a range of visual perturbations and out-of-domain images, when compared with biological vision. Prior work has demonstrated that coupling a standard CNN with a front-end (VOneBlock) that mimics the primate primary visual cortex (V1) can improve overall model robustness. Expanding on this, we introduce Early Vision Networks (EVNets), a new class of hybrid CNNs that combine the VOneBlock with a novel SubcorticalBlock, whose architecture draws from computational models in neuroscience and is parameterized to maximize alignment with subcortical responses reported across multiple experimental studies. Without being optimized to do so, the assembly of the SubcorticalBlock with the VOneBlock improved V1 alignment across most standard V1 benchmarks, and better modeled extra-classical receptive field phenomena. In addition, EVNets exhibit stronger emergent shape bias and outperform the base CNN architecture by 9.3\% on an aggregate benchmark of robustness evaluations, including adversarial perturbations, common corruptions, and domain shifts. Finally, we show that EVNets can be further improved when paired with a state-of-the-art data augmentation technique, surpassing the performance of the isolated data augmentation approach by 6.2\% on our robustness benchmark. This result reveals complementary benefits between changes in architecture to better mimic biology and training-based machine learning approaches.

Recently, it has been shown that simulating computations in early primate visual areas, up to the primary visual cortex (V1), at the front of convolutional neural networks (CNNs) leads to improvements in robustness to image corruptions. However, it remains unclear whether this improvement requires precisely matching the receptive field (RF) properties of V1 neurons or if some aspects are sufficient. Here, we explore this question by building several variants of a CNN model with a front-end modeling the primate V1 using a classical neuroscientific model, a Gabor Filter Bank (GFB) followed by simple- and complex- cell nonlinearities. Each model variant had varying levels of biological detail according to how the RF properties were sampled. The model variant sampling these parameters according to empirical biological distributions was considerably more robust to image corruptions than the variant sampling the parameters uniformly and independently (relative difference of 8.72%). However, a uniform variant capturing correlations between some GFB parameters obtained the same performance as the biological sampling variant. Our results show that it is not sufficient to approximate V1 with only the right class of function and parameter range, as it is also required to include the observed correlations between RF properties. However, it is not necessary to fully replicate the empirical distributions of V1 RF properties to obtain the desired improvement in robustness.

*How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed?* We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text. We propose DE-COP, a method to determine whether a piece of copyrighted content is included in training. DE-COP's core approach is to probe an LLM with multiple-choice questions, whose options include both verbatim text and their paraphrases. We construct BookTection, a benchmark with excerpts from 165 books published prior and subsequent to a model's training cutoff, along with their paraphrases. Our experiments show that DE-COP outperforms the prior best method by 8.6% in detection accuracy (AUC) on models with logits available. Moreover, DE-COP also achieves an average accuracy of 72% for detecting suspect books on fully black-box models where prior methods give approximately 0% accuracy. The code and datasets are available at https://github.com/LeiLiLab/DE-COP.

The Vision Transformer architecture has shown to be competitive in the computer vision (CV) space where it has dethroned convolution-based networks in several benchmarks. Nevertheless, convolutional neural networks (CNN) remain the preferential architecture for the representation module in reinforcement learning. In this work, we study pretraining a Vision Transformer using several state-of-the-art self-supervised methods and assess the quality of the learned representations. To show the importance of the temporal dimension in this context we propose an extension of VICReg to better capture temporal relations between observations by adding a temporal order verification task. Our results show that all methods are effective in learning useful representations and avoiding representational collapse for observations from the Atari Learning Environment (ALE) which leads to improvements in data efficiency when we evaluated in reinforcement learning (RL). Moreover, the encoder pretrained with the temporal order verification task shows the best results across all experiments, with richer representations, more focused attention maps and sparser representation vectors throughout the layers of the encoder, which shows the importance of exploring such similarity dimension. With this work, we hope to provide some insights into the representations learned by ViT during a self-supervised pretraining with observations from RL environments and to understand which properties arise in the representations that lead to the best-performing agents.

Address matching is a crucial activity for post offices and companies responsible for parcel processing and delivery. Inaccurate delivery of parcels can significantly impact the reputation of these companies and result in considerable economic and environmental costs. This paper proposes a deep learning model that aims to increase efficiency on the address matching task for portuguese addresses. The model consists on a bi-encoder, trained to create meaningful embeddings of portuguese postal addresses, which is then used to retrieve from a normalized database the matches of the target unnormalized addresses. We argue that a good initialization of the bi-encoder weights is a crucial step for achieving optimal performance and we support our hypothesis by showing that training a transformer from scratch leads to better results, when compared with using a pre-trained model. We also evaluate the bi-encoder's performance when using a standard contrastive loss, where we carefully select the negative samples, versus using a multiple negatives ranking loss, where we use larger batch sizes with multiple random in-batch negatives. The model, trained from scratch with the multiple negatives ranking loss, was tested with data retrieved from a real-life scenario of portuguese addresses and exhibited a very high mapping accuracy, exceeding 99.60% at the door level. The implementation of this system in a real context of parcel deliveries is expected to result in significant efficiency gains in the distribution process. Such an implementation is currently under evaluation.

Road object detection in low-visibility conditions, such as nighttime, fog, and rain, is difficult for standard machine learning models, which often struggle because of limited training data. The collection of comprehensive datasets that encompass all possible scenarios encountered during deployment is often impractical in terms of time and cost. To overcome this limitation, this work proposes the use of specific augmentations tailored to address the challenges associated with low-visibility conditions. The model employed in this research was trained on sub-sets that complemented the missing low-visibility circumstances. Augmentations based on depth and Fourier domain techniques were applied to simulate such conditions during training and enhance the model's performance when faced with such a scenario. Experimental results demonstrated that appropriately applied augmentations can improve the model's performance. Specifically, in rainy weather, the best-performing model trained on augmented data achieved a 3.4% improvement over a model trained on non-augmented data. In other cases, however, the proposed augmentations did not increase significantly the per-formance of the classifier.

Attention mechanisms have raised significant interest in the research community, since they promise relevant improvements in the performance of neural network architectures. However, in any specific problem, we still lack a principled way to choose specific mechanisms and hyper-parameters that lead to guaranteed improvements. More recently, self-attention has been proposed and widely used in transformer-like architectures, leading to significant breakthroughs in some applications. In this work we focus on two forms of attention mechanisms, attention modules and self-attention. Attention modules are used to reweigh the features of each layer input tensor. Different modules have different ways to perform this reweighting in fully connected or convolutional layers. The attention models studied are completely modular and in this work they will be used with the popular ResNet architecture. Self-attention, originally proposed in the area of natural language processing makes it possible to relate all the items in an input sequence. Self-attention is becoming increasingly popular in computer vision, where it is sometimes combined with convolutional layers, although some recent architectures do away entirely with convolutions. In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset. The results show that attention modules do sometimes improve the performance of convolutional neural network architectures, but also that this improvement, although noticeable and statistically significant, is not consistent in different settings. The results obtained with self-attention mechanisms, on the other hand, show consistent and significant improvements, leading to the best results even in architectures with a reduced number of parameters.

Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such as the ASTRAL, DRAGON, and THRIVE have been proposed as tools to help physicians predict the patient functional outcome after a stroke. These scores are rule-based classifiers that use features available when the patient is admitted to the emergency room. In this paper, we apply machine learning techniques to the problem of predicting the functional outcome of ischemic stroke patients, three months after admission. We show that a pure machine learning approach achieves only a marginally superior Area Under the ROC Curve (AUC) ( 0.808±0.085) than that of the best score ( 0.771±0.056) when using the features available at admission. However, we observed that by progressively adding features available at further points in time, we can significantly increase the AUC to a value above 0.90. We conclude that the results obtained validate the use of the scores at the time of admission, but also point to the importance of using more features, which require more advanced methods, when possible.

BACKGROUND: Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DEGREECOX, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes. RESULTS: We applied DEGREECOX to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with RIDGE and LASSO, DEGREECOX shows an improvement in the classification of high and low risk patients in a par with NET-COX. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DEGREECOX gives results that are similar to those of the best performing methods, in a few cases slightly better. CONCLUSIONS: Network-based regularization seems a promising framework to deal with the dimensionality problem. The centrality metrics proposed can be easily expanded to accommodate other topological properties of different biological networks.

SUMMARY The human interaction through the web generates both implicit and explicit knowledge. An example of an implicit contribution is searching, as people contribute with their knowledge by clicking on retrieved documents. When this information is available, an important and interesting challenge is to extract relations from query logs, and, in particular, semantic relations between queries and their terms. In this paper, we present and discuss results on query contextualization through the association of tags to queries, that is, query folksonomies. Note that tags may not even occur within the query. Our results rely on the analysis of large query log induced graphs, namely click induced graphs. Results obtained with real data show that the inferred query folksonomy provide interesting insights both on semantic relations among queries and on web users intent.Copyright © 2011 John Wiley & Sons, Ltd.

The sliding window (SW) Lempel-Ziv (LZ) 77 algorithms are widely used for universal lossless data compression. The LZ77 encoding component performs repeated substring search. Data structures, such as hash tables and trees have been used for fast search, at the expense of memory usage. Recently, suffix arrays (SA) have been used for dictionary representation and LZ77 decomposition, using less memory than those data structures.

MOTIVATION: Uncovering mechanisms underlying gene expression control is crucial to understand complex cellular responses. Studies in gene regulation often aim to identify regulatory players involved in a biological process of interest, either transcription factors coregulating a set of target genes or genes eventually controlled by a set of regulators. These are frequently prioritized with respect to a context-specific relevance score. Current approaches rely on relevance measures accounting exclusively for direct transcription factor-target interactions, namely overrepresentation of binding sites or target ratios. Gene regulation has, however, intricate behavior with overlapping, indirect effect that should not be neglected. In addition, the rapid accumulation of regulatory data already enables the prediction of large-scale networks suitable for higher level exploration by methods based on graph theory. A paradigm shift is thus emerging, where isolated and constrained analyses will likely be replaced by whole-network, systemic-aware strategies. RESULTS: We present TFRank, a graph-based framework to prioritize regulatory players involved in transcriptional responses within the regulatory network of an organism, whereby every regulatory path containing genes of interest is explored and incorporated into the analysis. TFRank selected important regulators of yeast adaptation to stress induced by quinine and acetic acid, which were missed by a direct effect approach. Notably, they reportedly confer resistance toward the chemicals. In a preliminary study in human, TFRank unveiled regulators involved in breast tumor growth and metastasis when applied to genes whose expression signatures correlated with short interval to metastasis.

BACKGROUND: Over the past few years, new massively parallel DNA sequencing technologies have emerged. These platforms generate massive amounts of data per run, greatly reducing the cost of DNA sequencing. However, these techniques also raise important computational difficulties mostly due to the huge volume of data produced, but also because of some of their specific characteristics such as read length and sequencing errors. Among the most critical problems is that of efficiently and accurately mapping reads to a reference genome in the context of re-sequencing projects. RESULTS: We present an efficient method for the local alignment of pyrosequencing reads produced by the GS FLX (454) system against a reference sequence. Our approach explores the characteristics of the data in these re-sequencing applications and uses state of the art indexing techniques combined with a flexible seed-based approach, leading to a fast and accurate algorithm which needs very little user parameterization. An evaluation performed using real and simulated data shows that our proposed method outperforms a number of mainstream tools on the quantity and quality of successful alignments, as well as on the execution time. CONCLUSIONS: The proposed methodology was implemented in a software tool called TAPyR--Tool for the Alignment of Pyrosequencing Reads--which is publicly available from http://www.tapyr.net.

Suffix trees are by far the most important data structure in stringology, with a myriad of applications in fields like bioinformatics and information retrieval. Classical representations of suffix trees require Θ( n log n ) bits of space, for a string of size n . This is considerably more than the n log 2 σ bits needed for the string itself, where σ is the alphabet size. The size of suffix trees has been a barrier to their wider adoption in practice. Recent compressed suffix tree representations require just the space of the compressed string plus Θ( n ) extra bits. This is already spectacular, but the linear extra bits are still unsatisfactory when σ is small as in DNA sequences. In this article, we introduce the first compressed suffix tree representation that breaks this Θ( n )-bit space barrier. The Fully Compressed Suffix Tree (FCST) representation requires only sublinear space on top of the compressed text size, and supports a wide set of navigational operations in almost logarithmic time. This includes extracting arbitrary text substrings, so the FCST replaces the text using almost the same space as the compressed text. An essential ingredient of FCSTs is the lowest common ancestor (LCA) operation. We reveal important connections between LCAs and suffix tree navigation. We also describe how to make FCSTs dynamic, that is, support updates to the text. The dynamic FCST also supports several operations. In particular, it can build the static FCST within optimal space and polylogarithmic time per symbol. Our theoretical results are also validated experimentally, showing that FCSTs are very effective in practice as well.

The installation of software packages depends on the correct resolution of dependencies and conflicts between packages. This problem is NP-complete and, as expected, is a hard task. Moreover, today's technology still does not address this problem in an acceptable way. This paper introduces a new approach to solving the software dependency problem in a Linux environment, devising a way for solving dependencies according to available packages and user preferences. This work introduces the "apt-pbo" tool, the first publicly available tool that solves dependencies in a complete and optimal way.

Although most biclustering formulations are NP-hard, in time series expression data analysis, it is reasonable to restrict the problem to the identification of maximal biclusters with contiguous columns, which correspond to coherent expression patterns shared by a group of genes in consecutive time points. This restriction leads to a tractable problem. We propose an algorithm that finds and reports all maximal contiguous column coherent biclusters in time linear in the size of the expression matrix. The linear time complexity of CCC-Biclustering relies on the use of a discretized matrix and efficient string processing techniques based on suffix trees. We also propose a method for ranking biclusters based on their statistical significance and a methodology for filtering highly overlapping and, therefore, redundant biclusters. We report results in synthetic and real data showing the effectiveness of the approach and its relevance in the discovery of regulatory modules. Results obtained using the transcriptomic expression patterns occurring in Saccharomyces cerevisiae in response to heat stress show not only the ability of the proposed methodology to extract relevant information compatible with documented biological knowledge but also the utility of using this algorithm in the study of other environmental stresses and of regulatory modules in general.

The Lempel-Ziv 77 (LZ77) and LZ-Storer-Szymanski (LZSS) text compression algorithms use a sliding window over the sequence of symbols, with two sub-windows: the dictionary (symbols already encoded) and the look-ahead-buffer (LAB) (symbols not yet encoded). Binary search trees and suffix trees (ST) have been used to speedup the search of the LAB over the dictionary, at the expense of high memory usage [1]. A suffix array (SA) is a simpler, more compact data structure which uses (much) less memory [2,3] to hold the same information. The SA for a length m string is an array of integers ([1], ...[k], ...a[m]) that stores the lexicographic order of suffix k of the string; sub-string searching, as used in LZ77/LZSS, is done by searching the SA.

Boolean satisfiability (SAT) finds a wide range of practical applications, including Artificial Intelligence and, more recently, Bioinformatics. Although encoding some combinatorial problems using Boolean logic may not be the most intuitive solution, the efficiency of state-of-the-art SAT solvers often makes it worthwhile to consider encoding a problem to SAT. One representative application of SAT in Bioinformatics is haplotype inference. The problem of haplotype inference under the assumption of pure parsimony consists in finding the smallest number of haplotypes that explains a given set of genotypes. The original formulations for solving the problem of Haplotype Inference by Pure Parsimony (HIPP) were based on Integer Linear Programming. More recently, solutions based on SAT have been shown to be remarkably more efficient. This paper provides an overview of SAT-based approaches for solving the HIPP problem and identifies current research directions.

BACKGROUND: Motif finding algorithms have developed in their ability to use computationally efficient methods to detect patterns in biological sequences. However the posterior classification of the output still suffers from some limitations, which makes it difficult to assess the biological significance of the motifs found. Previous work has highlighted the existence of positional bias of motifs in the DNA sequences, which might indicate not only that the pattern is important, but also provide hints of the positions where these patterns occur preferentially. RESULTS: We propose to integrate position uniformity tests and over-representation tests to improve the accuracy of the classification of motifs. Using artificial data, we have compared three different statistical tests (Chi-Square, Kolmogorov-Smirnov and a Chi-Square bootstrap) to assess whether a given motif occurs uniformly in the promoter region of a gene. Using the test that performed better in this dataset, we proceeded to study the positional distribution of several well known cis-regulatory elements, in the promoter sequences of different organisms (S. cerevisiae, H. sapiens, D. melanogaster, E. coli and several Dicotyledons plants). The results show that position conservation is relevant for the transcriptional machinery. CONCLUSION: We conclude that many biologically relevant motifs appear heterogeneously distributed in the promoter region of genes, and therefore, that non-uniformity is a good indicator of biological relevance and can be used to complement over-representation tests commonly used. In this article we present the results obtained for the S. cerevisiae data sets.

We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branching, that we call consistent k-graphs (CkG). The optimal branching is used as an heuristic for a primary causality order between network variables, which is subsequently refined, according to a certain score, into an optimal CkG Bayesian network. This approach augments the search space exponentially, in the number of nodes, relatively to trees, yet keeping a polynomial-time bound. The proposed algorithm can be applied to scores that decompose over the network structure, such as the well known LL, MDL, AIC, BIC, K2, BD, BDe, BDeu and MIT scores. We tested the proposed algorithm in a classification task. We show that the induced classifier always score better than or the same as the Naive Bayes and Tree Augmented Naive Bayes classifiers. Experiments on the UCI repository show that, in many cases, the improved scores translate into increased classification accuracy.

MOTIVATION: The ability to identify complex motifs, i.e. non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem is extremely important, not only because these motifs can accurately model biological phenomena but because its extraction is highly dependent upon the appropriate selection of numerous search parameters. Currently available combinatorial algorithms have proved to be highly efficient in exhaustively enumerating motifs (including complex motifs), which fulfill certain extraction criteria. However, one major problem with these methods is the large number of parameters that need to be specified. RESULTS: We propose a new algorithm, MUSA (Motif finding using an UnSupervised Approach), that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders. This method relies on a biclustering algorithm that operates on a matrix of co-occurrences of small motifs. The performance of this method is independent of the composite structure of the motifs being sought, making few assumptions about their characteristics. The MUSA algorithm was applied to two datasets involving the bacterium Pseudomonas putida KT2440. The first one was composed of 70 sigma(54)-dependent promoter sequences and the second dataset included 54 promoter sequences of up-regulated genes in response to phenol, as suggested by quantitative proteomics. The results obtained indicate that this approach is very effective at identifying complex motifs of biological significance. AVAILABILITY: The MUSA algorithm is available upon request from the authors, and will be made available via a Web based interface.

We propose a new algorithm for identifying cis-regulatory modules in genomic sequences. The proposed algorithm, named RISO, uses a new data structure, called box-link, to store the information about conserved regions that occur in a well-ordered and regularly spaced manner in the data set sequences. This type of conserved regions, called structured motifs, is extremely relevant in the research of gene regulatory mechanisms since it can effectively represent promoter models. The complexity analysis shows a time and space gain over the best known exact algorithms that is exponential in the spacings between binding sites. A full implementation of the algorithm was developed and made available online. Experimental results show that the algorithm is much faster than existing ones, sometimes by more than four orders of magnitude. The application of the method to biological data sets shows its ability to extract relevant consensi.

Validation of RTL descriptions remains one of the principal bottlenecks in the circuit design process. Random simulation based methods for functional validation suffer from fundamental limitations and may be inappropriate or too expensive. In fact, for some circuits, a large number of vector is required in order to make the circuit reach hard to test constructs and obtains accurate values for their testability. In this work, we present a static, non-simulation based, method for the determination of the controllability of RTL constructs that is efficient and gives accurate feedback to the designers in what regards the presence of hard to control constructs in their RTL code. The method takes as input a Verilog RTL description, solves the Chapman-Kolmogorov equations that describe the steady-state of the circuit and outputs the computed values for the controllability of the RTL constructs. To avoid the exponential blow-up that results from writing one equation for each circuit state and solving the resulting system of equations, an approximation method is used. We present results showing that the approximation is effective and describe how the method can be used to bias a random test generator in order to achieve higher coverage using a smaller number of vectors.

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.

In most libraries, gate parameters such as the pin-to-pin intrinsic delays, load-dependent coefficients, and input pin capacitances have different values for rising and falling signals. Most performance optimization algorithms, however, assume a single value for each parameter. It is known that under the load-independent delay model, the gate assignment (or resizing) problem is solvable in time polynomial in the circuit size when a single value is assumed for each parameter (Kukimoto et al., 1998). We show that, in the presence of different rise and fall parameter values, this problem is NP-complete even for chain and tree topology circuits under the simple load-independent delay model (Murgai, 1999). However, we also show that, for tree circuits, the problem is not NP-complete in the strong sense, and we propose a dynamic programming algorithm that solves it exactly in pseudopolynomial time. More specifically, we show that the problem can be solved in time proportional to the size of the circuit, the number of choices available in the library for each gate and the delay of the circuit. We also present a straightforward way of extending this algorithm to general directed acyclic networks. We present experimental results on a set of benchmark problems using a standard commercial library and show that our algorithm generates provably optimum delays for 69 out of 73 circuits. We also compare our technique with two approaches traditionally used to solve this problem in the industry and academia and show that it performs better than these two. Interestingly, both traditional approaches also yield delays that are, in general, not far from the optimum.

Clock-gating techniques are very effective in the reduction of the switching activity in sequential logic circuits. In this paper, we describe a clock-gating technique based on finite-state machine (FSM) decomposition. The approach is based on the computation of two sub-FSMs that together have the same functionality as the original FSM. For all the transitions within one sub-FSM, the clock for the other sub-FSM is disabled. To minimize the average switching activity, we search for a small cluster of states with high stationary state probability and use it to create the small sub-FSM. Explicit manipulation of the state transition graph requires time and space exponential on the number of registers in the circuit, thereby restricting the applicability of explicit methods to relatively small circuits. The approach we propose is based on a method that implicitly performs the FSM decomposition. Using this technique, the FSM decomposition is performed by direct manipulation of the circuit. We provide a set of experiments that show that power consumption can be substantially reduced, in some cases by more than 70%.

We present a methodology for the watermarking of synchronous sequential circuits that makes it possible to identify the authorship of designs by imposing a digital watermark on the state transition graph (STG) of the circuit. The methodology is applicable to sequential designs that are made available as firm intellectual property, the designation commonly used to characterize designs specified as structural hardware description languages or circuit netlists. The watermarking is obtained by manipulating the STG of the design in such a way as to make it exhibit a chosen property that is extremely rare in nonwatermarked circuits while, at the same time, not changing the functionality of the circuit. This manipulation is performed without ever actually computing this graph in either implicit or explicit form. Instead, the digital watermark is obtained by direct manipulation of the circuit description. We present evidence that no known algorithms for circuit manipulation can be used to efficiently remove or change the watermark and that the process is immune to a variety of other attacks. We present both theoretical and experimental results that show that the watermarking can be created and verified efficiently. We also test possible attack strategies and verify that they are inapplicable to realistic designs of medium to large complexity.

Article Free Access Share on FSM decomposition by direct circuit manipulation applied to low power design Authors: José C. Monteiro IST-INESC, Lisbon, Portugal IST-INESC, Lisbon, PortugalView Profile , Arlindo L. Oliveira Cadence European Labs/IST-INESC, Lisbon, Portugal Cadence European Labs/IST-INESC, Lisbon, PortugalView Profile Authors Info & Claims ASP-DAC '00: Proceedings of the 2000 Asia and South Pacific Design Automation ConferenceJanuary 2000 Pages 351–358https://doi.org/10.1145/368434.368678Published:28 January 2000Publication History 4citation185DownloadsMetricsTotal Citations4Total Downloads185Last 12 Months7Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In most libraries, gate parameters such as the pin-to-pin intrinsic delays, load-dependent coefficients, and input pin capacitances have different values for rising and falling signals. The performance optimization algorithms, however, assume a single value for each parameter. It is known that under the load-independent delay model, the gate assignment (or resizing) problem is solvable in time polynomial in the circuit size when a single value is assumed for each parameter. In the presence of different rise and fall parameter values, this problem was recently shown to be NP-complete even for chain and tree topology circuits under the simple load-independent delay model. In this paper, we propose it dynamic programming algorithm for solving this problem exactly in pseudo-polynomial time for tree circuits. More specifically, we show that the problem can be solved in time proportional to the size of the tree circuit, the number of choices available in the library for each gate, and the delay of the circuit. To the best of our knowledge, this is the first pseudo-polynomial exact algorithm for the gate assignment problem for trees in the presence of different rise and fall delays. We present a straightforward way of extending this algorithm to general directed acyclic graphs. We present experimental results on a set of benchmark problems using a standard commercial library and show that our algorithm generates provably optimum delays for 72 out of 76 circuits. We also compare our technique with two approaches traditionally used to solve this problem in the industry and academia and show that it is slightly better than these two. Interestingly, both traditional approaches also yield delays not far from the optimum.

We present a methodology for the watermarking of synchronous sequential circuits that makes it possible to identify the authorship of designs by imposing a digital watermark on the state transition graph of the circuit.The methodology is applicable to sequential designs that are made available as firm Intellectual Property (IP), the designation commonly used to characterize designs specified as structural descriptions or circuit netlists.The watermarking is obtained by manipulating the state transition graph of the design in such a way as to make it exhibit a chosen property that is extremely rare in non-watermarked circuits, while, at the same time, not changing the functionality of the circuit.This manipulation is performed without ever actually computing this graph in either implicit or explicit form.We present both theoretical and experimental results that show that the watermarking can be created and verified efficiently.

We propose a new algorithm for the problem of state reduction in incompletely specified finite state machines. Unlike the most commonly used algorithms for this problem, our approach is not based on the enumeration of compatible sets, and, therefore, its performance is not dependent on its number. Instead, the algorithm uses techniques for finite state machine identification that are well known in the computer science literature, but have never been applied to this problem. We prove that the algorithm is exact and present results that show that, in a set of hard problems, it is much more efficient than both the explicit and implicit approaches based on the enumeration of compatible sets. We also present a complexity analysis for the special cases where worst case polynomial time bounds can be obtained and present experiments that validate empirically the bounds obtained.

Clock-gating techniques have been shown to be very effective in the reduction of the switching activity in sequential logic circuits. In this paper we describe a new clock-gating technique based on finite state machine (FSM) decomposition. We compute two sub-FSMs that together have the same functionality as the original FSM. For all the transitions within one sub-FSM, the clock for the other sub-FSM is disabled. To minimize the average switching activity, we search for a small cluster of states with high stationary state probability and use it to create the small sub-FSM. This way we will have a small amount of logic that is active most of the time, during which is disabling a much larger circuit, the other sub-FSM.

Recently, in [3], the following problem was addressed: Given a set of data words or messages to be transmitted over a bus such that the sequence (order) in which they are transmitted is irrelevant, determine the optimum sequence that minimizes the total number of transitions on the bus. In 1994, Stan and Burleson [5] presented the bus-invert method as a means of encoding words for reducing I/O power, in which a word may be inverted and then transmitted if doing so reduces the number of transitions. In this paper, we combine the two paradigms into one — that of sequencing words under the bus-invert scheme for the minimum transitions, i.e., words can be complemented, reordered and then transmitted. We prove that this problem DOPI — Data Ordering Problem with Inversion — is NP-complete. We present a polynomial-time approximation algorithm to solve DOPI that comes within a factor of 1.5 from the optimum. Experimental results show that, on average, the solutions generated by our algorithm were within 4.4% of the optimum, and that resequencing along with complementation leads to 34.4% reduction in switching activity.

We propose a new rdgorithm to the problem of state reduction in incompletely specified finite state machines.~is algorithm is not based on the enumeration of compatible sets, and, therefore, its performance is not dependent on the number of prime compatibles.We prove that the algorithm is exact and present results that show that, in a set of hard problems, it is much more efficient than both the explicit and implicit approaches based on the enumeration of compatible sets.

We propose a new algorithm for the inference of the minimum size deterministic automaton consistent with a prespecified set of input/output strings. Our approach improves a well known search algorithm proposed by A.W. Bierman and J.A. Feldman (1972), by incorporating a set of techniques known as dependency directed backtracking. These techniques have already been used in other applications, but we are the first to apply them to this problem. The results show that the application of these techniques yields an algorithm that is, for the problems studied, orders of magnitude faster than existing approaches.

This paper addresses the problem of binary decision diagram (BDD) minimization in the presence of don't care sets. Specifically given an incompletely specified function g and a fixed ordering of the variables, we propose an exact algorithm for selecting f such that f is a cover for g and the binary decision diagram for f is of minimum size. The approach described is the only known exact algorithm for this problem not based on the enumeration of the assignments to the points in the don't care set. We show also that our problem is NP-complete. We show that the BDD minimization problem can be formulated as a binate covering problem and solved using implicit enumeration techniques. In particular, we show that the minimum-sized binary decision diagram compatible with the specification can be found by solving a problem that is very similar to the problem of reducing incompletely specified finite state machines. We report experiments of an implicit implementation of our algorithm, by means of which a class of interesting examples was solved exactly. We compare it with existing heuristic algorithms to measure the quality of the latter.

The computation of prime implicants has several and significant applications in different areas, including automated reasoning, non-monotonic reasoning, electronic design automation, among others. The authors describe a new model and algorithm for computing minimum-size prime implicants of propositional formulas. The proposed approach is based on creating an integer linear program (ILP) formulation for computing the minimum-size prime implicant, which simplifies existing formulations. In addition, they introduce two new algorithms for solving ILPs, both of which are built on top of an algorithm for propositional satisfiability (SAT). Given the organization of the proposed SAT algorithm, the resulting ILP procedures implement powerful search pruning techniques, including a non-chronological backtracking search strategy, clause recording procedures and identification of necessary assignments. Experimental results, obtained on several benchmark examples, indicate that the proposed model and algorithms are significantly more efficient than other existing solutions.

The authors present an algorithm for the synthesis of two-level threshold gate networks inspired by techniques used in classical two-level minimization of logic circuits. They specifically address a restricted version of the problem where the on and off set minterms are explicitly listed. Experimental results show that a simple branch and bound algorithm can be used to obtain solutions close to the absolute minimum in a set of standard problems, outperforming other minimizers even when restricted to using only classic logic gates as building blocks. The algorithm has a run time polynomial in the input size and its performance degrades slowly with the size of the problem.>