Brute-force Computation Research Articles

On designing a modified-UI based honeyword generation approach for overcoming the existing limitations

Inverting hashed passwords by performing brute force computation is one of the latest security threats on password based authentication technique. New technologies are being developed for reducing complexity of brute force computation and these increase the success rate of inversion attack. Honeyword base authentication protocol can successfully mitigate this threat by making password cracking detectable. However, existing honeyword based methods have several limitations like Multiple System Vulnerability, Weak DoS Resistivity, Storage Overhead, etc. In this paper, we have proposed a new modified-UI based honeyword generation approach, identified as Paired Distance Protocol (PDP), which overcomes most of the drawbacks of previously proposed honeyword generation approaches. The comprehensive analysis shows that PDP not only attains a high detection rate of 97.23%, but also reduces the storage overhead to a great extent.

Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning.

Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our method using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.

Optimal construction of node-disjoint shortest paths in folded hypercubes

Node-disjoint paths have played an important role in the study of routing, reliability, and fault tolerance of an interconnection network. In this paper, we give a necessary and sufficient condition, which can be verified in O(mn1.5) time, for the existence of m node-disjoint shortest paths from one source node to other m (not necessarily distinct) target nodes, respectively, in an n-dimensional folded hypercube, where m≤n+1. Moreover, when the condition holds, the m node-disjoint shortest paths can be constructed in optimal O(mn) time. In the situation that all of the source node and target nodes are mutually distinct, brute-force computations show that the probability of existence of the m node-disjoint shortest paths in an n-dimensional folded hypercube is not less than 100%, 86%, 86%, 92%, and 94% for (n,m)=(3,4),(4,4),(5,6),(6,6), and (7, 8), respectively.

High-performance powder diffraction pattern simulation for large-scale atomistic modelsviafull-precision pair distribution function computation

A new full-precision algorithm to solve the Debye scattering equation has been developed for high-performance computing of powder diffraction line profiles from large-scale atomistic models of nanomaterials. The Debye function was evaluated using a pair distribution function computed with high accuracy, exploiting the series expansion of the error between calculated and equispace-sampled pair distances of atoms. The intensity uncertainty (standard deviation) of the computed diffraction profile was estimated as a function of the algorithm-intrinsic approximations and coordinate precision of the atomic positions, confirming the high accuracy of the simulated pattern. Based on the propagation of uncertainty, the new algorithm provides a more accurate powder diffraction profile than a brute-force calculation. Indeed, the precision of floating-point numbers employed in brute-force computations is worse than the estimated accuracy provided by the new algorithm. A software application,ROSE-X, has been implemented for parallel computing on CPU/GPU multi-core processors and distributed clusters. The computing performance is directly proportional to the total processor speed of the devices. An average speed of ∼30 × 109computed pair distances per second was measured, allowing simulation of the powder diffraction pattern of an ∼23 million atom microstructure in a couple of hours. Moreover, the pair distribution function was recorded and reused to evaluate powder diffraction profiles of the same system with different properties (i.e.Qrather than 2θ range, step and wavelength), avoiding additional pair distance computations. This approach was used to investigate a large collection of monoatomic and polyatomic microstructures, isolating the contribution from atoms belonging to different moieties (e.g.different species or crystalline domains).

Four loop scattering in the Nambu-Goto theory

We initiate the study of multiloop scattering amplitudes in the Nambu-Goto theory on the worldsheet of a non-critical string. We start with a brute force calculation of two loop four particle scattering. Somewhat surprisingly, even though non-trivial UV counterterms are present at this order, on-shell amplitudes remain polynomial in the momenta of colliding particles. We show that this can be understood as a consequence of existence of certain close by (semi)integrable models. Furthermore, these arguments can be extended to obtain the answer for three and four loop scattering, bypassing the brute force calculation. The resulting amplitudes develop non-polynomial (logarithmic) dependence on the momenta starting at three loops.

Direct generation of loop-erased transition paths in non-equilibrium reactions.

A computational procedure is proposed to generate directly loop-erased transition paths in the context of non-equilibrium reactions, i.e. reactions that occur in systems whose dynamics is not in detailed balance. The procedure builds on results from Transition Path Theory (TPT), and it avoids altogether the need to generate reactive trajectories, either by brute-force calculations or using importance sampling schemes such as Transition Path Sampling (TPS). This is computationally advantageous since these reactive trajectories can themselves be very long and intricate in complex reactions. The loop-erased transition paths, on the other hand, are shorter and simpler because, by construction, they are pruned of all the detours typical reactive trajectories make and contain only their productive pieces that carry the effective current of the reaction. As a result they give direct access to the reaction rate and mechanism.

MapReduce 환경에서 재그룹핑을 이용한 Locality Sensitive Hashing 기반의 K-Nearest Neighbor 그래프 생성 알고리즘의 개선

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of , which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

Some efficient approaches for multi-objective constrained optimization of computationally expensive black-box model problems

Multi-objective constrained optimization problems which arise in many engineering fields often involve computationally expensive black-box model simulators of industrial processes which have to be solved with limited computational time budget, and hence limited number of simulator calls. This paper proposes two heuristic approaches aiming to build proxy problem models, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate approximation of the Pareto front. The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation. The second approach builds a multi-objective nonlinear programming (MO-NLP) surrogate model using curve fitting of objectives and constraints. In both approaches the desired number of approximated solutions of the Pareto front are generated by applying the ɛ-constraint method to the multi-objective surrogate problems. The proposed approaches are tested for the cost vs. life cycle assessment (LCA)-based environmental optimization of drinking water production plants. The results obtained with both approaches show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with a state-of-the-art metaheuristic algorithm.

The crystal-fluid interfacial free energy and nucleation rate of NaCl from different simulation methods.

In this work, we calculate the crystal-fluid interfacial free energy, γ(cf), for the Tosi-Fumi model of NaCl using three different simulation techniques: seeding, umbrella sampling, and mold integration. The three techniques give an orientationaly averaged γ(cf) of about 100 mJ/m(2). Moreover, we observe that the shape of crystalline clusters embedded in the supercooled fluid is spherical. Using the mold integration technique, we compute γ(cf) for four different crystal orientations. The obtained interfacial free energies range from 100 to 114 mJ/m(2), being (100) and (111) the crystal planes with the lowest and highest γ(cf), respectively. Within the accuracy of our calculations, the interfacial free energy either does not depend on temperature or changes very smoothly with it. Combining the seeding technique with classical nucleation theory, we also estimate nucleation free energy barriers and nucleation rates for a wide temperature range (800-1040 K). The obtained results compare quite well with brute force calculations and with previous results obtained with umbrella sampling [Valeriani et al., J. Chem. Phys, 122, 194501 (2005)].

Renormalization of twist-four operators in light-cone gauge

We compute one-loop renormalization group equations for non-singlet twist-four operators in QCD. The calculation heavily relies on the light-cone gauge formalism in the momentum fraction space that essentially rephrases the analysis of all two-to-two and two-to-three transition kernels to purely algebraic manipulations both for non- and quasipartonic operators. This is the first brute force calculation of this sector available in the literature. Fourier transforming our findings to the coordinate space, we checked them against available results obtained within a conformal symmetry-based formalism that bypasses explicit diagrammatic calculations and confirmed agreement with the latter.

Chemical reactions induced by oscillating external fields in weak thermal environments.

Chemical reaction rates must increasingly be determined in systems that evolve under the control of external stimuli. In these systems, when a reactant population is induced to cross an energy barrier through forcing from a temporally varying external field, the transition state that the reaction must pass through during the transformation from reactant to product is no longer a fixed geometric structure, but is instead time-dependent. For a periodically forced model reaction, we develop a recrossing-free dividing surface that is attached to a transition state trajectory [T. Bartsch, R. Hernandez, and T. Uzer, Phys. Rev. Lett. 95, 058301 (2005)]. We have previously shown that for single-mode sinusoidal driving, the stability of the time-varying transition state directly determines the reaction rate [G. T. Craven, T. Bartsch, and R. Hernandez, J. Chem. Phys. 141, 041106 (2014)]. Here, we extend our previous work to the case of multi-mode driving waveforms. Excellent agreement is observed between the rates predicted by stability analysis and rates obtained through numerical calculation of the reactive flux. We also show that the optimal dividing surface and the resulting reaction rate for a reactive system driven by weak thermal noise can be approximated well using the transition state geometry of the underlying deterministic system. This agreement persists as long as the thermal driving strength is less than the order of that of the periodic driving. The power of this result is its simplicity. The surprising accuracy of the time-dependent noise-free geometry for obtaining transition state theory rates in chemical reactions driven by periodic fields reveals the dynamics without requiring the cost of brute-force calculations.

On the construction of all shortest vertex-disjoint paths in Cayley graphs of abelian groups

Cayley graphs provide a group-theoretic model for designing and analyzing symmetric interconnection networks. In this paper, we give a sufficient condition for the existence of m vertex-disjoint shortest paths from one source vertex to other m (not necessarily distinct) destination vertices in a Cayley graph of an abelian group, where m≤n and n is the cardinality of a (group) generator of the abelian group. In addition, when the condition holds, the m vertex-disjoint shortest paths can be constructed in O(mn) time, which is optimal in the worst case when O(n) ≤ the order of diameter. By applying our results, we can easily obtain the necessary and sufficient conditions, which can be verified in nearly optimal time, for the existence of all shortest vertex-disjoint paths in generalized hypercubes and odd tori. In the situation that all of the source vertex and destination vertices are mutually distinct, brute-force computations show that the probability of the existence of the m vertex-disjoint shortest paths in an r-dimensional generalized hypercube with r coordinates each of order k is greater than 94%, 70%, 96%, 99%, and 99% for (k,r,m)=(2,7,7),(3,4,8),(4,3,6),(5,3,6), and (6,3,6), respectively.

Constructing all shortest node-disjoint paths in torus networks

An n-dimensional torus network, also called wrap-around mesh or toroidal network, is a Cartesian product of n cycle networks. In particular, it was named k-ary n-cube when the sizes of the n cycle networks are all equal to k. In this paper, m node-disjoint shortest paths from one source node to other m (not necessarily distinct) destination nodes are constructed in an n-dimensional torus network, provided the existence of such node-disjoint shortest paths which can be verified in O(mn1.5) time, where m is not greater than the connectivity. The worst-case time and space complexities of the construction procedure are both optimal O(mn). In the situation that all of the source node and destination nodes are mutually distinct, brute-force computations show that the probability of the existence of the m node-disjoint shortest paths (from the source node to the m destination nodes) in a k-ary n-cube is greater than 94%, 70%, 91%, 69%, and 89% for (k,n,m)=(2,7,7), (3, 4, 8), (4, 3, 6), (5, 3, 6), and (6, 3, 6), respectively.

Calculating the Funding Valuation Adjustment (FVA) of Value-at-Risk (VAR) Based Initial Margin

Initial margin requirements are becoming an increasingly common feature of derivative markets. However, while the valuation of derivatives under collateralisation (Piterbarg, 2010, 2012a), under counterparty risk with unsecured funding costs (FVA) (Burgard and Kjaer, 2011a, 2011b, 2013) and in the presence of regulatory capital (KVA) (Green, Kenyon and Dennis, 2014) are established through valuation adjustments, hitherto initial margin has not been considered. This paper further extends the semi-replication framework of Burgard and Kjaer (2013), itself later extended by Green, Kenyon and Dennis (2014), to cover the cost of initial margin, leading to Margin Valuation Adjustment (MVA). Initial margin requirements are typically generated through the use of VAR or CVAR models. Given the form of MVA as an integral over the expected initial margin profile this would lead to excessive computational costs if a brute force calculation were to be used. Hence we also propose a computationally efficient approach to the calculation of MVA through the use of regression techniques, Longstaff-Schwartz Augmented Compression (LSAC).

More On Self-Tiling Tile Sets

SummaryThis is an extension of the author's previous work on self-tiling tile sets. Hitherto, the only known method for discovering the latter was by means of a brute-force computer search. A new pencil and paper method for extracting such sets from rep-tiles is introduced, and a plethora of new examples presented.

Methods for First-Principles Alloy Thermodynamics

Traditional first-principles calculations excel at providing formation energies at absolute zero, but obtaining thermodynamic information at nonzero temperatures requires suitable sampling of all the excited states visited in thermodynamic equilibrium, which would be computationally prohibitive via brute-force quantum mechanical calculations alone. In the context of solid-state alloys, this issue can be addressed via the coarse-graining concept and the cluster expansion formalism. This process generates simple, effective Hamiltonians that accurately reproduce quantum mechanical calculation results and that can be used to efficiently sample configurational, vibrational, and electronic excitations and enable the prediction of thermodynamic properties at nonzero temperatures. Vibrational and electronic degrees of freedom are formally eliminated from the problem by writing the system’s partition function in a nested form in which the inner sums can be readily evaluated to yield an effective Hamiltonian. The remaining outermost sum corresponds to atomic configurations and can be handled via Monte Carlo sampling driven by the resulting effective Hamiltonian, thereby delivering thermodynamic properties at nonzero temperatures. This article describes these techniques and their implementation in the alloy theoretic automated toolkit, an open-source software package. The methods are illustrated by applications to various alloy systems.

Green's function theory for predicting device-to-device variability

Due to random dopant fluctuations, the device-to-device variability is a serious challenge to emerging nanoelectronics. In this work we present theoretical formalisms and numerical simulations of quantum-transport variability, based on the Green's function technique and the multiple-scattering theory. We have developed a general formalism using the diagrammatic technique within the coherent-potential approximation that can be applied to a wide range of disorder concentrations. In addition, we have developed a method by using a perturbative expansion within the low-concentration approximation that is extremely useful for typical nanoelectronic devices having low dopant concentration. Applying both formalisms, transport fluctuations due to random impurities can be predicted without lengthy brute force computation of ensembles of device structures. Numerical implementations of the formalisms are demonstrated using both tight-binding models and first-principles models.

Conductance fluctuation and shot noise in disordered graphene systems, a perturbation expansion approach

We report the investigation of conductance fluctuation and shot noise in disordered graphene systems with two kinds of disorder, Anderson type impurities and random dopants. To avoid the traditional exact but time-consuming approach, known as brute-force calculation, which is somehow impractical at low doping concentration, we develop an expansion method based on the coherent potential approximation (CPA) to calculate the average of four Green's functions, and the results are obtained by truncating the expansion up to 6th order in terms of “single-site-T-matrix.” Since our expansion is with respect to “single-site-T-matrix” instead of disorder strength W, good result can be obtained at 6th order for finite W. We benchmark our results against brute-force method on disordered graphene systems as well as the two dimensional square lattice model systems for both Anderson disorder and the random doping. The results show that in the regime where the disorder strength W is small or the doping concentration is low, our results agree well with the results obtained from the brute-force method. Specifically, for the graphene system with Anderson impurities, our results for conductance fluctuation show good agreement for W up to 0.4t, where t is the hopping energy. While for average shot noise, the results are good for W up to 0.2t. When the graphene system is doped with low concentration 1%, the conductance fluctuation and shot noise agrees with brute-force results for large W which is comparable to the hopping energy t. At large doping concentration 10%, good agreement can be reached for conductance fluctuation and shot noise for W up to 0.4t. We have also tested our formalism on square lattice with similar results. Our formalism can be easily combined with linear muffin-tin orbital first-principles transport calculations for light doping nano-scaled systems, making prediction on variability of nano-devices.

Additive Decomposition of the Physical Components of the Magnetic Coupling from Broken Symmetry Density Functional Theory Calculations.

The procedure to extract and identify from broken-symmetry density functional theory (BS-DFT) calculations the various components of the magnetic couplings in diradicals [J. Chem. Phys. 2012, 137, 114106] is re-examined. It is shown that this previous decomposition scheme fails for systems exhibiting large core polarization effects and hence becomes not additive in such cases. At variance, the new scheme which differs from the previous one in the assessment of the polarization effects is perfectly additive. As done previously, the direct exchange is calculated from the MS = 1 and MS = 0 restricted solutions. We show that allowing first the delocalization of the magnetic orbitals in the field of the closed shell frozen core furnishes a good evaluation of the kinetic exchange contribution to the magnetic exchange coupling, i.e. the intersite delocalization of the magnetic electrons in the low-spin state. In a second step, allowing the polarization of the core to take place in the field of the so-revised magnetic orbitals practically leads to the same total value of the magnetic coupling obtained by the brute-force BS-DFT calculation. The success of this decomposition is illustrated on a representative series of inorganic and organic diradicals. The obtained quasi-additivity of the effects is rationalized thanks to a careful theoretical analysis of the broken-symmetry solutions.

Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems

Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.