Brute-force Strategy Research Articles

Brute Force Virtual Drug Screening with Molecular Dynamics Simulation and MM/PBSA to Find Potent Inhibitors of METTL16.

Epitranscriptomic modification is a dynamic modification of RNAs. Epitranscriptomic writer proteins are methyltransferases, such as METTL3 and METTL16. The up regulation of METTL3 have been found to be linked to different cancers and targeting METTL3 is an effective way to reduce tumour progression. Drug development against METTL3 is an active field of research. METTL16, SAM dependent methyltransferase, is another writer protein, that has been found to be upregulated in hepatocellular carcinoma and gastric cancer. In this pioneering study METTL16 has been targeted for virtual drug screening for the very first time using brute force strategy to identify a drug molecule that could be repurposed for the treatment of the disease caused. An unbiased library of the commercially available drug molecules has been used for screening using a multipoint validation process developed for this work, which includes molecular docking, ADMET analysis, protein-ligand interaction analysis, Molecular Dynamics Simulation, binding energy calculation via Molecular Mechanics Poisson-Boltzmann Surface Area method. Upon the in-silico screening of over 650 drugs the authors have found NIL and VXL passed the validation process. The data strongly indicates the potency of these two drugs in the treatment of disease where METTL16 needs to be inhibited.

Game Design for Better Security of Combination Locks

Dial locks are commonly used to secure a person’s items. Commercially available dial locks often use four or five wheels of letters, allowing a user to select a word as a combination. In order to evaluate the security of these locks, we create a game, with an instance created by the lock designer, and played by a lock owner and a thief. In the game, the lock owner chooses a word as a combination, and the thief creates a brute force strategy to try all possible combinations that yield words until the combination is found. To accomplish the task, the thief will solve a version of the Probabilistic Travelling Salesman Problem (PTSP) by creating an a priori tour through all the words a lock can create. The goal for the game designer, then, is to create a lock configuration that maximizes the expected length of the best possible PTSP tour. This paper describes a Genetic Algorithm (GA) approach to design a near-optimal game, i.e. a lock configuration that makes it as difficult for the thief to crack. An analysis of the output of the GA shows that the locks that the system creates are significantly more secure than both commercial locks, in the context of this game.

The strategy of model building in climate science

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling.

Feature points-based image registration between satellite imagery and aerial images of agricultural land

Rapid advancement in remote sensing sensors has resulted in an enormous increase in the use of satellite imagery (SI) and images taken from unmanned aerial vehicles (UAVs) in a wide range of remote sensing applications. These applications include urban planning, environment monitoring, map updating, change detection, and precision agriculture. This paper focuses on an agricultural application of SI and UAV images. SI-UAV images possess high temporal, textural, and intensity differences due to rapid changes in agricultural crops with the passage of time. Feature points such as scale invariant feature transform (SIFT), oriented FAST and rotated BRIEF (ORB), and speeded-up robust features (SURF) are not invariant to such differences and underperform in SI-UAV image registration. To deal with this problem, we propose a new method that combines the strength of nearest neighbor (NN) and brute force (BF) descriptor matching strategies to register SI?UAV images. The proposed method is named NN-BF. For NN-BF first corresponding feature point descriptor matches are identified between SI-UAV images of the training set with overlap error. Then the corresponding descriptors are matched with the descriptors of SI images of the test set with NN strategy. The resulting descriptor matches are then further matched with the descriptors of UAV images of the test set using BF strategy. Finally, the descriptor matches obtained are processed with RANSAC to remove outliers and estimate a homography for image registration. Experiments are performed on an agricultural land image dataset. The experimental results show that the NN-BF method improves SIFT, SURF, and ORB feature point performances and also outperforms recently proposed feature matching strategies for remote sensing images. SIFT on average obtains 6.1% and 18.9% better precision scores than SURF and ORB with NN-BF, respectively. SIFT also obtains lower root mean square error than SURF and ORB with NN-BF.

Centralized vs Decentralized Targeted Brute-Force Attacks: Guessing With Side-Information

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which $m$ adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords X of length $n$ , generated independently and identically from a distribution $P_{X}$ , we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information $\mathrm {Y}_{(m)}$ are generated independently from passing X through a memoryless channel $P_{Y|X}$ , as the length of the password $n$ goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks.

Transcriptome profiling helps to identify potential and true molecular switches of stealth to brute force behavior in Pectobacterium atrosepticum during systemic colonization of tobacco plants

In the present study, we have monitored the process of systemic plant colonization by the plant pathogenic bacterium Pectobacterium atrosepticum (Pba) using RNA-Seq analysis in order to compare bacterial traits under in planta and in vitro conditions and to reveal potential players that participate in switching from stealth to brute force strategy of the pathogen. Two stages of tobacco plant colonization have been assayed: i) the initial one associated with visually symptomless spread of bacteria throughout the host body via primary xylem vessels where bacterial emboli were formed (stealth strategy), and ii) the advanced stage coupled with an extensive colonization of core parenchyma and manifestation of soft rot symptoms (brute force strategy). Plant-inducible genes in Pba and potential players switching the pathogen’s behavior were revealed. Genes from the cfa locus responsible for the production of coronafacic acid displayed the strongest induction in the asymptomatic zone relative to the symptomatic one and were shown experimentally to act as the true strategy “switchers” of Pba behavior in planta. Surprisingly, cfa genes appeared to be unnecessary for establishment of the asymptomatic stage of plant colonization but were required for the transition to soft-rot-associated symptomatic stage coupled with over-induction of jasmonate-mediated pathway in the plant.

Advanced bioinformatics methods for practical applications in proteomics.

Mass spectrometry (MS)-based proteomics has undergone rapid advancements in recent years, creating challenging problems for bioinformatics. We focus on four aspects where bioinformatics plays a crucial role (and proteomics is needed for clinical application): peptide-spectra matching (PSM) based on the new data-independent acquisition (DIA) paradigm, resolving missing proteins (MPs), dealing with biological and technical heterogeneity in data and statistical feature selection (SFS). DIA is a brute-force strategy that provides greater width and depth but, because it indiscriminately captures spectra such that signal from multiple peptides is mixed, getting good PSMs is difficult. We consider two strategies: simplification of DIA spectra to pseudo-data-dependent acquisition spectra or, alternatively, brute-force search of each DIA spectra against known reference libraries. The MP problem arises when proteins are never (or inconsistently) detected by MS. When observed in at least one sample, imputation methods can be used to guess the approximate protein expression level. If never observed at all, network/protein complex-based contextualization provides an independent prediction platform. Data heterogeneity is a difficult problem with two dimensions: technical (batch effects), which should be removed, and biological (including demography and disease subpopulations), which should be retained. Simple normalization is seldom sufficient, while batch effect-correction algorithms may create errors. Batch effect-resistant normalization methods are a viable alternative. Finally, SFS is vital for practical applications. While many methods exist, there is no best method, and both upstream (e.g. normalization) and downstream processing (e.g. multiple-testing correction) are performance confounders. We also discuss signal detection when class effects are weak.

Introducing an Educational Tool for Learning Branch & Bound Strategy

Abstract—According to our informal survey, Branch & Bound strategy is considerably difficult to learn compared to other strategies. This strategy consists of several complex algorithmic steps such as Reduced Cost Matrix (RCM) calculation and Breadth First Search. Thus, to help students understanding this strategy, AP-BB, an educational tool for learning Branch & Bound is developed. This tool includes four modules which are Brute Force solving visualization, Branch & Bound solving visualization, RCM calculator, and case-based performance comparison. These modules are expected to enhance student’s understanding about Branch & Bound strategy and its characteristics. Furthermore, our work incorporates TSP as its case study and Brute Force strategy as a baseline to provide a concrete impact of Branch & Bound strategy. According to our qualitative evaluation, AP-BB and all of its features fulfil student necessities for learning Branch & Bound strategy. Keywords— Educational Tool; Branch & Bound; Algorithm Strategy; Algorithm Visualization

Workflows for science: a challenge when facing the convergence of HPC and Big Data

Workflows have been used traditionally as a mean to describe and implement the computing usually parametric studies and explorations searching for the best solution that scientific researchers want to perform. A workflow is not only the computing application, but a way of documenting a process. Science workflows may be of very different nature depending on the area of research, matching the actual experiment that the scientist want to perform. Workflow Management Systems are environments that offer the researchers tools to define, publish, execute and document their workflows. In some cases, the science workflows are used to generate data; in other cases are used to analyse existing data; only in a few cases, workflows are used both to generate and analyse data. The design of experiments is in some cases generated blindly, without a clear idea of which points are relevant to be computed/simulated, ending up with huge amount of computation that is performed following a brute-force strategy. However, the evolution of systems and the large amount of data generated by the applications require an in-situ analysis of the data, thus requiring new solutions to develop workflows that includes both the simulation/computational part and the analytic part. What is more, the fact that both components, computation and analytics, can be run together will enable the possibility of defining more dynamic workflows, with new computations being decided by the analytics in a more efficient way. The first part of the paper will review current approaches that a set of scientific communities follows in the development of their workflows. Due to the election of several scientific communities and use cases using a specific Workflow Management System, this survey maybe incomplete with regard a complete revision of the literature about workflows, but we expect that the reader appreaciates the effort performed in trying to see the scientific communities needs and requirements. The second part of the paper will propose a new software architecture to develop a new family of end-to-end workflows that enables the management of dynamic workflows composed of simulations, analytics and visualization, including inputs/outputs from streams.

Minimum guesswork discrimination between quantum states

Error probability is a popular and well-studied optimization criterion in discriminating non-orthogonal quantum states. It captures the threat from an adversary who can only query the actual state once. However, when the adversary is able to use a brute-force strategy to query the state, discrimination measurement with minimum error probability does not necessarily minimize the number of queries to get the actual state. In light of this, we take Massey's guesswork as the underlying optimization criterion and study the problem of minimum guesswork discrimination. We show that this problem can be reduced to a semidefinite programming problem. Necessary and sufficient conditions when a measurement achieves minimum guesswork are presented. We also reveal the relation between minimum guesswork and minimum error probability. We show that the two criteria generally disagree with each other, except for the special case with two states. Both upper and lower information-theoretic bounds on minimum guesswork are given. For geometrically uniform quantum states, we provide sufficient conditions when a measurement achieves minimum guesswork. Moreover, we give the necessary and sufficient condition under which making no measurement at all would be the optimal strategy.

A Proposed Modification of K-Means Algorithm

K-means algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of this algorithm is very high. Several researches have been carried out to minimize this complexity. This paper presents the result of our research, which proposes a modified version of k-means algorithm with an improved technique to divide the data set into specific numbers of clusters with the help of several check point values. It requires less computation and has enhanced accuracy than the traditional k-means algorithm as well as some modified variant of the traditional kMeans.

Extending Type Inference to Variational Programs

Through the use of conditional compilation and related tools, many software projects can be used to generate a huge number of related programs. The problem of typing such variational software is difficult. The brute-force strategy of generating all variants and typing each one individually is: (1) usually infeasible for efficiency reasons and (2) produces results that do not map well to the underlying variational program. Recent research has focused mainly on efficiency and addressed only the problem of type checking. In this work we tackle the more general problem of variational type inference and introduce variational types to represent the result of typing a variational program. We introduce the variational lambda calculus (VLC) as a formal foundation for research on typing variational programs. We define a type system for VLC in which VLC expressions are mapped to correspondingly variational types. We show that the type system is correct by proving that the typing of expressions is preserved over the process of variation elimination, which eventually results in a plain lambda calculus expression and its corresponding type. We identify a set of equivalence rules for variational types and prove that the type unification problem modulo these equivalence rules is unitary and decidable; we also present a sound and complete unification algorithm. Based on the unification algorithm, the variational type inference algorithm is an extension of algorithm W . We show that it is sound and complete and computes principal types. We also consider the extension of VLC with sum types, a necessary feature for supporting variational data types, and demonstrate that the previous theoretical results also hold under this extension. Finally, we characterize the complexity of variational type inference and demonstrate the efficiency gains over the brute-force strategy.

Integral region-based covariance tracking with occlusion detection

Covariance tracking has achieved impressive successes in recent years due to its competent region covariance-based feature descriptor. Although adopt fast integral image computation, covariance tracking's brute-force search strategy is still inefficient and it possibly leads to inconsecutive tracking trajectory and distraction. In this work, a generalized, adaptive covariance tracking approach with novel integral region computation and occlusion detection is proposed. The integral region is much faster than integral image and adaptive to the tracking target and tracking condition. The adaptive search window can be adjusted dynamically by simple occlusion detection. The integral image and the global covariance tracking can be seen just a special case of integral region and the proposed approach, respectively. The proposed approach unifies the local and global search strategies in an elegant way and smoothly switches between them according to the tracking conditions (i.e. occlusion distraction or sudden motion) which are judged by occlusion detector. It gets much better efficiency, robustness of distraction, and stable trajectory by local search in normal steady state, while obtains more abilities for occlusion and re-identification by enlarged search window (until to global search) in abnormal situation at the same time. Our approach shows excellent target representation ability, faster speed, and more robustness, which has been verified on some video sequences.

Evaluating Locality Sensitive Hashing for Matching Partial Image Patches in a Social Media Setting

Images posted to a social media site can employ image completion techniques to efficiently and seamlessly remove sensitive content and safeguard privacy. Image completion algorithms typically employ a time consuming patch matching stage derived nearest neighbour search algorithms. Typical patch matching processes perform poorly in the social media context which performs once-off edits on a range of high resolution images with plentiful exemplar material. We make use of hash tables to accelerate the matching stage. Our refinement is the development of a set of perceptually inspired hash functions that can exploit locality and provide a categorization consistent across any exemplar image. Descriptors derived from principal component analysis (PCA), after training on exemplar database, are used for comparison. Aggregation of descriptors improves accuracy and we adapt a probabilistic approach using randomly oriented hyperplanes to employ multiple descriptors in a single hash table. Hash table strategies demonstrate a substantial improvement in performance over a brute force strategy, and perceptually inspired features provide levels of accuracy comparable with those trained on the data using PCA descriptors. The aggregation strategies further improve accuracy although measurement of this is confounded by non-uniform distribution of the aggregated keys. Evaluation with increasing levels of missing data demonstrates that the use of hashing continues to perform well relative to the Euclidean metric benchmark. The patch matching process using aggregated perceptually inspired descriptors produces comparable results with substantial reduction in matching time when used for image completion in photographic images. While sensitivity to structural elements is identified as an issue, the complexity of the resulting process is well suited to bulk manipulation of high resolution images for use in social media.

Application of the alternating projection strategy to the Capon beamforming and the MUSIC algorithm for azimuth/elevation AOA estimation

In this paper, the alternating projection (AP) scheme is applied to get the initial and the final estimates of the Capon beamforming algorithm and the MUSIC algorithm. In a previous paper, the AP scheme has been proposed as a computationally efficient method for the initialization and optimization of the cost function for the ML-based angle of arrival (AOA) estimation problem. We call the AP scheme in the previous paper the brute-force AP strategy. To improve the brute-force AP, we suggest the Newton AP which consists of the successive one-dimensional Newton search. The brute-force AP can be used for the initialization and the optimization of the cost function of AOA estimation, and the Newton AP can be used for the optimization of the cost function for the AOA problem. We compare the performance of the AP-based approach with the conventional implementation of the AOA estimation algorithm using two-dimensional brute-force search.

A variability-aware module system

Module systems enable a divide and conquer strategy to software development. To implement compile-time variability in software product lines, modules can be composed in different combinations. However, this way, variability dictates a dominant decomposition. As an alternative, we introduce a variability-aware module system that supports compile-time variability inside a module and its interface. So, each module can be considered a product line that can be type checked in isolation. Variability can crosscut multiple modules. The module system breaks with the antimodular tradition of a global variability model in product-line development and provides a path toward software ecosystems and product lines of product lines developed in an open fashion. We discuss the design and implementation of such a module system on a core calculus and provide an implementation for C as part of the TypeChef project. Our implementation supports variability inside modules from #ifdef preprocessor directives and variable linking at the composition level. With our implementation, we type check all configurations of all modules of the open source product line Busybox with 811~compile-time options, perform linker check of all configurations, and report found type and linker errors -- without resorting to a brute-force strategy.

An error-tolerant type system for variational lambda calculus

Conditional compilation and software product line technologies make it possible to generate a huge number of different programs from a single software project. Typing each of these programs individually is usually impossible due to the sheer number of possible variants. Our previous work has addressed this problem with a type system for variational lambda calculus (VLC), an extension of lambda calculus with basic constructs for introducing and organizing variation. Although our type inference algorithm is more efficient than the brute-force strategy of inferring the types of each variant individually, it is less robust since type inference will fail for the entire variational expression if any one variant contains a type error. In this work, we extend our type system to operate on VLC expressions containing type errors. This extension directly supports locating ill-typed variants and the incremental development of variational programs. It also has many subtle implications for the unification of variational types. We show that our extended type system possesses a principal typing property and that the underlying unification problem is unitary. Our unification algorithm computes partial unifiers that lead to result types that (1) contain errors in as few variants as possible and (2) are most general. Finally, we perform an empirical evaluation to determine the overhead of this extension compared to our previous work, to demonstrate the improvements over the brute-force approach, and to explore the effects of various error distributions on the inference process.

Preparing Targets for V(D)J Recombinase: Transcription Paves the Way

The adaptive immune system has evolved a brute force strategy to combat the enormous numbers of pathogens we encounter on a daily basis. Billions of distinct Ag receptors (AgRs) are generated during our lifetime, each with a unique spectrum of binding specificities, and each is clonally distributed

Automated design of genomic Southern blot probes

BackgroundSothern blotting is a DNA analysis technique that has found widespread application in molecular biology. It has been used for gene discovery and mapping and has diagnostic and forensic applications, including mutation detection in patient samples and DNA fingerprinting in criminal investigations. Southern blotting has been employed as the definitive method for detecting transgene integration, and successful homologous recombination in gene targeting experiments.The technique employs a labeled DNA probe to detect a specific DNA sequence in a complex DNA sample that has been separated by restriction-digest and gel electrophoresis. Critically for the technique to succeed the probe must be unique to the target locus so as not to cross-hybridize to other endogenous DNA within the sample.Investigators routinely employ a manual approach to probe design. A genome browser is used to extract DNA sequence from the locus of interest, which is searched against the target genome using a BLAST-like tool. Ideally a single perfect match is obtained to the target, with little cross-reactivity caused by homologous DNA sequence present in the genome and/or repetitive and low-complexity elements in the candidate probe. This is a labor intensive process often requiring several attempts to find a suitable probe for laboratory testing.ResultsWe have written an informatic pipeline to automatically design genomic Sothern blot probes that specifically attempts to optimize the resultant probe, employing a brute-force strategy of generating many candidate probes of acceptable length in the user-specified design window, searching all against the target genome, then scoring and ranking the candidates by uniqueness and repetitive DNA element content. Using these in silico measures we can automatically design probes that we predict to perform as well, or better, than our previous manual designs, while considerably reducing design time.We went on to experimentally validate a number of these automated designs by Southern blotting. The majority of probes we tested performed well confirming our in silico prediction methodology and the general usefulness of the software for automated genomic Southern probe design.ConclusionsSoftware and supplementary information are freely available at: http://www.genes2cognition.org/software/southern_blot

Recent Developments in Focused Library Design: Targeting Gene-Families

For many years, the most frequently optimized qualities of a screening library, or corporate compound collection, were size and diversity. Maximizing the number of diverse hits is the fundamental goal of such strategies. The ostensible justification that "bigger is better" is based on the large, estimated size of small-molecule space and the hypothesis that the notoriously low hit rates from high-throughput screening (HTS) could be overcome by brute force: i.e. by screening more compounds. Published, detailed studies about the success (or failure) of the brute-force strategy are rare, but it is well-known that it did not fulfill expectations. As a result, published reports in recent years have increasingly described methods for designing, selecting or synthesizing gene family-focused or -biased libraries. Moreover, many of the larger compound suppliers now sell such libraries, reflecting the growing interest in them from both the pharmaceutical and biotechnology markets. The trend towards gene family-focused libraries marks the emergence of a different hypothesis about how to increase HTS hit rates and also reflects an increasingly pragmatic focus on the management of screening libraries. An important, underlying assumption in this trend is that a high-quality, general-purpose screening library of manageable size is neither realizable nor desirable. Whether a biasing strategy based on a specific gene family will do a better job of meeting both the scientific and business needs of the drug discovery enterprise still remains to be seen, but it is certainly an active area of current research. This review focuses on the "who, what, why, when, and how" of the design of gene family-focused libraries. Particular attention is given to reports that discuss not only the techniques used, but also any results obtained.