On-Line Balancing of Random Inputs

We consider an online vector balancing game where vectors vt, chosen uniformly at random in {−1,+1}n, arrive over time and a sign xt∈{−1,+1} must be picked immediately upon the arrival of vt. The goal is to minimize the L∞ norm of the signed sum ∑txtvt. We give an online strategy for picking the signs xt that has value O(n1/2) with high probability. Up to constants, this is the best possible even when the vectors are given in advance

On-Line Balancing of Random Inputs

We consider an online vector balancing game where vectors $v_t$, chosen uniformly at random in $\{-1,+1\}^n$, arrive over time and a sign $x_t \in \{-1,+1\}$ must be picked immediately upon the arrival of $v_t$. The goal is to minimize the $L^\infty$ norm of the signed sum $\sum_t x_t v_t$. We give an online strategy for picking the signs $x_t$ that has value $O(n^{1/2})$ with high probability. Up to constants, this is the best possible even when the vectors are given in advance.Comment: 13 page

On-line balancing of random inputs

We consider an online vector balancing game where vectors vt, chosen uniformly at random in {− 1, + 1}n, arrive over time and a sign xt ∈ {− 1, + 1} must be picked immediately upon the arrival of vt. The goal is to minimize the L∞ norm of the signed sum (Formula presented.).

Multifunctionality in embodied agents: Three levels of neural reuse

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels.Comment: Accepted at Cognitive Science Conference, 201

Engineering Parallel String Sorting

We discuss how string sorting algorithms can be parallelized on modern multi-core shared memory machines. As a synthesis of the best sequential string sorting algorithms and successful parallel sorting algorithms for atomic objects, we first propose string sample sort. The algorithm makes effective use of the memory hierarchy, uses additional word level parallelism, and largely avoids branch mispredictions. Then we focus on NUMA architectures, and develop parallel multiway LCP-merge and -mergesort to reduce the number of random memory accesses to remote nodes. Additionally, we parallelize variants of multikey quicksort and radix sort that are also useful in certain situations. Comprehensive experiments on five current multi-core platforms are then reported and discussed. The experiments show that our implementations scale very well on real-world inputs and modern machines.Comment: 46 pages, extension of "Parallel String Sample Sort" arXiv:1305.115

Penelope: The NBTI-aware processor

Transistors consist of lower number of atoms with every technology generation. Such atoms may be displaced due to the stress caused by high temperature, frequency and current, leading to failures. NBTI (negative bias temperature instability) is one of the most important sources of failure affecting transistors. NBTI degrades PMOS transistors whenever the voltage at the gate is negative (logic inputPeer ReviewedPostprint (published version

Practical Algorithms for Multicast Support in Input Queues Switches

Abstract — This paper deals with multicast flow support in N × N Input Queued switch architectures. A practical approach to support multicast traffic is presented, assuming that O(N) queues are available at each input port. The focus is on dynamic queueing policies, where, at each input port, multicast flows are assigned to one among the available queues when flows become active: flows are assigned to queues according to switch queue status and, possibly, to flow information. We discuss queueing assignments, scheduling algorithms and flow activity definition models. We explain why dynamic queueing disciplines may outperform static policies, and we show that, even in the most favorable conditions for static policies, they provide comparable performance. I