Approximation Algorithms for the Joint Replenishment Problem with Deadlines

The Joint Replenishment Problem (JRP) is a fundamental optimization problem in supply-chain management, concerned with optimizing the flow of goods from a supplier to retailers. Over time, in response to demands at the retailers, the supplier ships orders, via a warehouse, to the retailers. The objective is to schedule these orders to minimize the sum of ordering costs and retailers' waiting costs. We study the approximability of JRP-D, the version of JRP with deadlines, where instead of waiting costs the retailers impose strict deadlines. We study the integrality gap of the standard linear-program (LP) relaxation, giving a lower bound of 1.207, a stronger, computer-assisted lower bound of 1.245, as well as an upper bound and approximation ratio of 1.574. The best previous upper bound and approximation ratio was 1.667; no lower bound was previously published. For the special case when all demand periods are of equal length we give an upper bound of 1.5, a lower bound of 1.2, and show APX-hardness

La metacognición y el mejoramiento de la enseñanza de química universitaria

En este trabajo, que es parte de una investigación más extensa, sobre mejoramiento de la enseñanza de química universitaria, se presentan algunos resultados obtenidos luego de aplicar una nueva propuesta de enseñanza, destinada a la comprensión y resolución de problemas sobre el tema «Soluciones». Con el objeto de facilitar el aprendizaje significativo, la propuesta de trabajo incluye el uso de las denominadas herramientas metacognitivas que permitan aplicar metodologías conducentes al logro de dichos aprendizajes por parte de los estudiantes. Luego de aplicar las mencionadas herramientas, se procedió a realizar la evaluación de los estudiantes participantes para obtener datos sobre los logros alcanzados y sus aprendizajes. El análisis de los resultados muestra que el uso del nuevo enfoque instruccional ayuda a los estudiantes en sus procesos de aprendizaje, en la medida que se vayan haciendo conscientes de los mecanismos que se utilizan para obtener aprendizaje significativo.This work, which is part of a more extensive research project on the improvement of Chemistry teaching at university level, presents the results obtained by applying an innovative teaching methodology. This methodology was designed with the objective of helping students to better understand and solve problems regarding the topic "Solutions". In order to facilitate learning, the proposed methodology includes the use of metacognitive tools (concept maps, Gowin's Vee and clinical interviews), which allows the students to apply significant learning methodologies. After applying these tools, we evaluated the students in order to measure their achievements and their learning. The analysis of the results shows that the use of this new instructional approach helps the students in their learning process because they become aware of the mechanism they use to achieve significant learning

Incremental Medians via Online Bidding

In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. Following Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance, and the algorithm produces a nested sequence of facility sets where the kth set has size k. The algorithm is c-cost-competitive if the cost of each set is at most c times the cost of the optimum set of size k. We give improved incremental algorithms for the metric version: an 8-cost-competitive deterministic algorithm, a 2e ~ 5.44-cost-competitive randomized algorithm, a (24+epsilon)-cost-competitive, poly-time deterministic algorithm, and a (6e+epsilon ~ .31)-cost-competitive, poly-time randomized algorithm. The algorithm is s-size-competitive if the cost of the kth set is at most the minimum cost of any set of size k, and has size at most s k. The optimal size-competitive ratios for this problem are 4 (deterministic) and e (randomized). We present the first poly-time O(log m)-size-approximation algorithm for the offline problem and first poly-time O(log m)-size-competitive algorithm for the incremental problem. Our proofs reduce incremental medians to the following online bidding problem: faced with an unknown threshold T, an algorithm submits "bids" until it submits a bid that is at least the threshold. It pays the sum of all its bids. We prove that folklore algorithms for online bidding are optimally competitive.Comment: conference version appeared in LATIN 2006 as "Oblivious Medians via Online Bidding

The Power of Centralized PC Systems of Pushdown Automata

Parallel communicating systems of pushdown automata (PCPA) were introduced in (Csuhaj-Varj{\'u} et. al. 2000) and in their centralized variants shown to be able to simulate nondeterministic one-way multi-head pushdown automata. A claimed converse simulation for returning mode (Balan 2009) turned out to be incomplete (Otto 2012) and a language was suggested for separating these PCPA of degree two (number of pushdown automata) from nondeterministic one-way two-head pushdown automata. We show that the suggested language can be accepted by the latter computational model. We present a different example over a single letter alphabet indeed ruling out the possibility of a simulation between the models. The open question about the power of centralized PCPA working in returning mode is then settled by showing them to be universal. Since the construction is possible using systems of degree two, this also improves the previous bound three for generating all recursively enumerable languages. Finally PCPAs are restricted in such a way that a simulation by multi-head automata is possible

Information Gathering in Ad-Hoc Radio Networks with Tree Topology

We study the problem of information gathering in ad-hoc radio networks without collision detection, focussing on the case when the network forms a tree, with edges directed towards the root. Initially, each node has a piece of information that we refer to as a rumor. Our goal is to design protocols that deliver all rumors to the root of the tree as quickly as possible. The protocol must complete this task within its allotted time even though the actual tree topology is unknown when the computation starts. In the deterministic case, assuming that the nodes are labeled with small integers, we give an O(n)-time protocol that uses unbounded messages, and an O(n log n)-time protocol using bounded messages, where any message can include only one rumor. We also consider fire-and-forward protocols, in which a node can only transmit its own rumor or the rumor received in the previous step. We give a deterministic fire-and- forward protocol with running time O(n^1.5), and we show that it is asymptotically optimal. We then study randomized algorithms where the nodes are not labelled. In this model, we give an O(n log n)-time protocol and we prove that this bound is asymptotically optimal

Differential behavioral state-dependence in the burst properties of CA3 and CA1 neurons

Brief bursts of fast high-frequency action potentials are a signature characteristic of CA3 and CA1 pyramidal neurons. Understanding the factors determining burst and single spiking is potentially significant for sensory representation, synaptic plasticity and epileptogenesis. A variety of models suggest distinct functional roles for burst discharge, and for specific characteristics of the burst in neural coding. However, little in vivo data demonstrate how often and under what conditions CA3 and CA1 actually exhibit burst and single spike discharges. The present study examined burst discharge and single spiking of CA3 and CA1 neurons across distinct behavioral states (awake-immobility and maze-running) in rats. In both CA3 and CA1 spike bursts accounted for less than 20% of all spike events. CA3 neurons exhibited more spikes per burst, greater spike frequency, larger amplitude spikes and more spike amplitude attenuation than CA1 neurons. A major finding of the present study is that the propensity of CA1 neurons to burst was affected by behavioral state, while the propensity of CA3 to burst was not. CA1 neurons exhibited fewer bursts during maze running compared with awake-immobility. In contrast, there were no differences in burst discharge of CA3 neurons. Neurons in both subregions exhibited smaller spike amplitude, fewer spikes per burst, longer inter-spike intervals and greater spike amplitude attenuation within a burst during awake-immobility compared with maze running. These findings demonstrate that the CA1 network is under greater behavioral state-dependent regulation than CA3. The present findings should inform both theoretic and computational models of CA3 and CA1 function. © 2006 IBRO

Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation

We consider two neuronal networks coupled by long-range excitatory interactions. Oscillations in the gamma frequency band are generated within each network by local inhibition. When long-range excitation is weak, these oscillations phase-lock with a phase-shift dependent on the strength of local inhibition. Increasing the strength of long-range excitation induces a transition to chaos via period-doubling or quasi-periodic scenarios. In the chaotic regime oscillatory activity undergoes fast temporal decorrelation. The generality of these dynamical properties is assessed in firing-rate models as well as in large networks of conductance-based neurons.Comment: 4 pages, 5 figures. accepted for publication in Physical Review Letter