3,584 research outputs found
Routing quantum information in spin chains
Two different models for performing efficiently routing of a quantum state
are presented. Both cases involve an XX spin chain working as data bus and
additional spins that play the role of sender and receivers, one of which is
selected to be the target of the quantum state transmission protocol via a
coherent quantum coupling mechanism making use of local/global magnetic fields.
Quantum routing is achieved, in the first of the models considered, by weakly
coupling the sender and the receiver to the data bus. In the second model,
strong magnetic fields acting on additional spins located between the
sender/receiver and the data bus allow us to perform high fidelity routing.Comment: added references in v
Distributed control in virtualized networks
The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA
Approaches for Future Internet architecture design and Quality of Experience (QoE) Control
Researching a Future Internet capable of overcoming the current Internet limitations is a strategic
investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to
overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow
applications to transparently, efficiently and flexibly exploit the available network resources with the aim to
match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of
Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision
problem
Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community
In the r/AmITheAsshole subreddit, people anonymously share first person
narratives that contain some moral dilemma or conflict and ask the community to
judge who is at fault (i.e., who is "the asshole"). In general, first person
narratives are a unique storytelling domain where the author is the narrator
(the person telling the story) but can also be a character (the person living
the story) and, thus, the author has two distinct voices presented in the
story. In this study, we identify linguistic and narrative features associated
with the author as the character or as a narrator. We use these features to
answer the following questions: (1) what makes an asshole character and (2)
what makes an asshole narrator? We extract both Author-as-Character features
(e.g., demographics, narrative event chain, and emotional arc) and
Author-as-Narrator features (i.e., the style and emotion of the story as a
whole) in order to identify which aspects of the narrative are correlated with
the final moral judgment. Our work shows that "assholes" as Characters frame
themselves as lacking agency with a more positive personal arc, while
"assholes" as Narrators will tell emotional and opinionated stories.Comment: Accepted to the 17th International AAAI Conference on Web and Social
Media (ICWSM), 202
Ultrafast flow of interacting organic polaritons
The strong-coupling of an excitonic transition with an electromagnetic mode
results in composite quasi-particles called exciton-polaritons, which have been
shown to combine the best properties of their bare components in semiconductor
microcavities. However, the physics and applications of polariton flows in
organic materials and at room temperature are still unexplored because of the
poor photon confinement in such structures. Here we demonstrate that polaritons
formed by the hybridization of organic excitons with a Bloch Surface Wave are
able to propagate for hundreds of microns showing remarkable third-order
nonlinear interactions upon high injection density. These findings pave the way
for the studies of organic nonlinear light-matter fluxes and for a
technological promising route of dissipation-less on-chip polariton devices
working at room temperature.Comment: Improved version with polariton-polariton interactions. 13 pages, 4
figures, supporting 6 pages, 6 figure
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
Social media is increasingly used for large-scale population predictions,
such as estimating community health statistics. However, social media users are
not typically a representative sample of the intended population -- a
"selection bias". Within the social sciences, such a bias is typically
addressed with restratification techniques, where observations are reweighted
according to how under- or over-sampled their socio-demographic groups are.
Yet, restratifaction is rarely evaluated for improving prediction. Across four
tasks of predicting U.S. county population health statistics from Twitter, we
find standard restratification techniques provide no improvement and often
degrade prediction accuracies. The core reasons for this seems to be both
shrunken estimates (reduced variance of model predicted values) and sparse
estimates of each population's socio-demographics. We thus develop and evaluate
three methods to address these problems: estimator redistribution to account
for shrinking, and adaptive binning and informed smoothing to handle sparse
socio-demographic estimates. We show that each of these methods significantly
outperforms the standard restratification approaches. Combining approaches, we
find substantial improvements over non-restratified models, yielding a 53.0%
increase in predictive accuracy (R^2) in the case of surveyed life
satisfaction, and a 17.8% average increase across all tasks
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
Deep learning at scale is dominated by communication time. Distributing
samples across nodes usually yields the best performance, but poses scaling
challenges due to global information dissemination and load imbalance across
uneven sample lengths. State-of-the-art decentralized optimizers mitigate the
problem, but require more iterations to achieve the same accuracy as their
globally-communicating counterparts. We present Wait-Avoiding Group Model
Averaging (WAGMA) SGD, a wait-avoiding stochastic optimizer that reduces global
communication via subgroup weight exchange. The key insight is a combination of
algorithmic changes to the averaging scheme and the use of a group allreduce
operation. We prove the convergence of WAGMA-SGD, and empirically show that it
retains convergence rates similar to Allreduce-SGD. For evaluation, we train
ResNet-50 on ImageNet; Transformer for machine translation; and deep
reinforcement learning for navigation at scale. Compared with state-of-the-art
decentralized SGD variants, WAGMA-SGD significantly improves training
throughput (e.g., 2.1x on 1,024 GPUs for reinforcement learning), and achieves
the fastest time-to-solution (e.g., the highest score using the shortest
training time for Transformer).Comment: Published in IEEE Transactions on Parallel and Distributed Systems
(IEEE TPDS), vol. 32, no. 7, pp. 1725-1739, 1 July 202
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