In this work, we examine a generic class of simple distributed balls-into-bins algorithms. Exploiting the strong concentration bounds that apply to balls-into-bins games, we provide an iterative method to compute accurate estimates of the remaining balls and the load distribution after each round. Each algorithm is classified by (i) the load that bins accept in a given round, (ii) the number of...

For n particles, the original code requiresO(n) time because at each time step, the apply force function is called for each particle (to be updated) with each other particle. Since interactions are local, one particle is influenced by a few nearby particles. Thus, most of the time, the apply force function is computing a distance and returning after finding this distance to be greater than the ...

Given a set of numbers, and a set of bins of fixed capacity, the NP-complete problem of bin packing is to find the minimum number of bins needed to contain the numbers, such that the sum of the numbers assigned to each bin does not exceed the bin capacity. We present two improvements to our previous bin-completion algorithm. The first speeds up the constant factor per node generation, and the s...

We address the problem of similarity search in large time series databases. We introduce a novel indexing algorithm that allows faster retrieval. The index is formed by creating bins that contain time series subsequences of approximately the same shape. For each bin, we can quickly calculate a lower-bound on the distance between a given query and the most similar element of the bin. This bound ...

By obtaining photometric redshift information, tomography allows us to cross-correlate galaxy ellipticities in different source redshift bins. The cross-correlation is non-vanishing because the different bins share much of the foreground mass distribution from which, over Gpc scales, the lensing signal is built. If the redshift bins are thick enough however, the cross-correlations are insensiti...

For bin packing, the input consists of n items with sizes s1, . . . , sn ∈ [0,1] which have to be assigned to aminimum number of bins of size 1. Recently, the second author gave an LP-based polynomial time algorithm that employed techniques from discrepancy theory to find a solution using at mostOPT +O(logOPT · log logOPT ) bins. In this paper, we present an approximation algorithm that has an ...

In variable sized bin packing, a number of bin types are given and the goal is to pack a list of items by choosing appropriate bins such that the total size of bins used is minimized. In this paper, we present absolute worst-case analysis for on-line algorithms. Then we consider an related problem: how to design k different bin sizes such that for any item list the waste space of bins used is m...

The bin-packing problem (BPP) and its multi-dimensional variants have many practical applications, such as packing objects in boxes, production planning, multiprocessor scheduling, etc. The classical singleobjective formulation of the two-dimensional bin packing (2D-BPP) consists of packing a set of objects (pieces) in the minimum number of bins (containers). This paper presents a new Pareto-ba...

A large set of rising adiabatic parcel simulations is executed to investigate the combined diffusional and accretional growth of cloud droplets in maritime and continental conditions, and to assess the impact of enhanced droplet collisions due to small-scale cloud turbulence. The microphysical model applies the droplet number density function to represent spectral evolution of cloud and rain/dr...

The main contribution of this paper is an optimal bounded space online algorithm for variable-sized multidimensional packing. In this problem, hyperboxes must be packed in ddimensional bins of various sizes, and the goal is to minimize the total volume of the used bins. We show that the method used can also be extended to deal with the problem of resource augmented multidimensional packing, whe...