Universality of Load Balancing Schemes on Diffusion Scale

We consider a system of $N$ parallel queues with identical exponential service rates and a single dispatcher where tasks arrive as a Poisson process. When a task arrives, the dispatcher always assigns it to an idle server, if there is any, and to a server with the shortest queue among $d$ randomly selected servers otherwise $(1 \leq d \leq N)$. This load balancing scheme subsumes the so-called Join-the-Idle Queue (JIQ) policy $(d = 1)$ and the celebrated Join-the-Shortest Queue (JSQ) policy $(d = N)$ as two crucial special cases. We develop a stochastic coupling construction to obtain the diffusion limit of the queue process in the Halfin-Whitt heavy-traffic regime, and establish that it does not depend on the value of $d$, implying that assigning tasks to idle servers is sufficient for diffusion level optimality

Trading interactions for topology in scale-free networks

Scale-free networks with topology-dependent interactions are studied. It is shown that the universality classes of critical behavior, which conventionally depend only on topology, can also be explored by tuning the interactions. A mapping, $\gamma' = (\gamma - \mu)/(1-\mu)$, describes how a shift of the standard exponent $\gamma$ of the degree distribution $P(q)$ can absorb the effect of degree-dependent pair interactions $J_{ij} \propto (q_iq_j)^{-\mu}$. Replica technique, cavity method and Monte Carlo simulation support the physical picture suggested by Landau theory for the critical exponents and by the Bethe-Peierls approximation for the critical temperature. The equivalence of topology and interaction holds for equilibrium and non-equilibrium systems, and is illustrated with interdisciplinary applications.Comment: 4 pages, 5 figure

The Discovery and Interpretation of Evidence Accumulation Stages

To improve the understanding of cognitive processing stages, we combined two prominent traditions in cognitive science: evidence accumulation models and stage discovery methods. While evidence accumulation models have been applied to a wide variety of tasks, they are limited to tasks in which decision-making effects can be attributed to a single processing stage. Here, we propose a new method that first uses machine learning to discover processing stages in EEG data and then applies evidence accumulation models to characterize the duration effects in the identified stages. To evaluate this method, we applied it to a previously published associative recognition task (Application 1) and a previously published random dot motion task with a speed-accuracy trade-off manipulation (Application 2). In both applications, the evidence accumulation models accounted better for the data when we first applied the stage-discovery method, and the resulting parameter estimates where generally in line with psychological theories. In addition, in Application 1 the results shed new light on target-foil effects in associative recognition, while in Application 2 the stage discovery method identified an additional stage in the accuracy-focused condition — challenging standard evidence accumulation accounts. We conclude that the new framework provides a powerful new tool to investigate processing stages

Isolation and characterization of kinetoplast DNA from bloodstream form of Trypanosoma brucei

We have used restriction endonucleases PstI, EcoRI, HapII, HhaI, and S1 nuclease to demonstrate the presence of a large complex component, the maxi-circle, in addition to the major mini-circle component in kinetoplast DNA (kDNA) networks of Trypanosoma brucei (East African Trypanosomiasis Research Organization [EATRO] 427). Endonuclease PstI and S1 nuclease cut the maxi-circle at a single site, allowing its isolation in a linear form with a mol wt of 12.2 x 10(6), determined by electron microscopy. The other enzymes give multiple maxi-circle fragments, whose added mol wt is 12-13 x 10(6), determined by gel electrophoresis. The maxi-circle in another T. brucei isolate (EATRO 1125) yields similar fragments but appears to contain a deletion of about 0.7 x 10(6) daltons. Electron microscopy of kDNA shows the presence of DNA considerably longer than the mini-circle contour length (0.3 micron) either in the network or as loops extending from the edge. This long DNA never exceeds the maxi-circle length (6.3 microns) and is completely removed by digestion with endonuclease PstI. 5-10% of the networks are doublets with up to 40 loops of DNA clustered between the two halves of the mini-circle network and probably represent a division stage of the kDNA. Digestion with PstI selectively removes these loops without markedly altering the mini-circle network. We conclude that the long DNA in both single and double networks represents maxi-circles and that long tandemly repeated oligomers of mini-circles are (virtually) absent. kDNA from Trypanosoma equiperdum, a trypanosome species incapable of synthesizing a fully functional mitochondrion, contains single and double networks of dimensions similar to those from T. brucei but without any DNA longer than mini-circle contour length. We conclude that the maxi-circle of trypanosomes is the genetic equivalent of the mitochondrial DNA (mtDNA) of other organisms

A stochastic network with mobile users in heavy traffic

We consider a stochastic network with mobile users in a heavy-traffic regime. We derive the scaling limit of the multi-dimensional queue length process and prove a form of spatial state space collapse. The proof exploits a recent result by Lambert and Simatos which provides a general principle to establish scaling limits of regenerative processes based on the convergence of their excursions. We also prove weak convergence of the sequences of stationary joint queue length distributions and stationary sojourn times.Comment: Final version accepted for publication in Queueing Systems, Theory and Application

Variable frame based Max-Weight algorithms for networks with switchover delay

This paper considers the scheduling problem for networks with interference constraints and switchover delays, where it takes a nonzero time to reconfigure each service schedule. Switchover delay occurs in many telecommunication applications such as satellite, optical or delay tolerant networks (DTNs). Under zero switchover delay it is well known that the Max-Weight algorithm is throughput-optimal without requiring knowledge of the arrival rates. However, we show that this property of Max-Weight no longer holds when there is a nonzero switchover delay. We propose a class of variable frame based Max-Weight (VFMW) algorithms which employ the Max-Weight schedule corresponding to the beginning of the frame during an interval of duration dependent on the queue sizes. The VFMW algorithms dynamically adapt the frame sizes to the stochastic arrivals and provide throughput-optimality without requiring knowledge of the arrival rates. Numerical results regarding the application of the VFMW algorithms to DTN and optical networks demonstrate a good delay performance.National Science Foundation (U.S.) (NSF grant CNS-0626781)National Science Foundation (U.S.) (NSF grant CNS-0915988)United States. Army Research Office (ARO Muri grant number W911NF-08-1-0238

Discovering the brain stages of lexical decision:Behavioral effects originate from a single neural decision process

Lexical decision (LD) – judging whether a sequence of letters constitutes a word – has been widely investigated. In a typical lexical decision task (LDT), participants are asked to respond whether a sequence of letters is an actual word or a nonword. Although behavioral differences between types of words/nonwords have been robustly detected in LDT, there is an ongoing discussion about the exact cognitive processes that underlie the word identification process in this task. To obtain data-driven evidence on the underlying processes, we recorded electroencephalographic (EEG) data and applied a novel machine-learning method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). In the current study, participants performed an LDT in which we varied the frequency of words (high, low frequency) and “wordlikeness” of non-words (pseudowords, random non-words). The results revealed that models with six processing stages accounted best for the data in all conditions. While most stages were shared, Stage 5 differed between conditions. Together, these results indicate that the differences in word frequency and lexicality effects are driven by a single cognitive processing stage. Based on its latency and topology, we interpret this stage as a Decision process during which participants discriminate between words and nonwords using activated lexical information

Decoding study-independent mind-wandering from EEG using convolutional neural networks

Objective. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNNs) to track mind-wandering across studies. Approach. We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N = 6) and tested the meta-learner on the data from an independent study for across-study predictions. Main results. The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel. Significance. Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps.</p