5,106 research outputs found
Shot noise in charge and magnetization currents of a quantum ring
The shot noise in a quantum ring, connected to leads, is studied in the
presence of electron interactions in the sequential tunneling regime. Two
qualitatively different noise correlations with distinctly different behaviors
are identified and studied in a large range of parameters. Noise in the total
current is due to the discreteness of the electron charge and can become
super-Poissonian as result of electron interaction. The noise in the
magnetization current is comparatively insensitive to the interaction but can
be greatly enhanced if population inversion of the angular states is assumed.
The characteristic time scales are studied by a Monte-Carlo simulation.Comment: 5 pages, 5 color figure
Fermion-parity duality and energy relaxation in interacting open systems
We study the transient heat current out of a confined electron system into a
weakly coupled electrode in response to a voltage switch. We show that the
decay of the Coulomb interaction energy for this repulsive system exhibits
signatures of electron-electron attraction, and is governed by an
interaction-independent rate. This can only be understood from a general
duality that relates the non-unitary evolution of a quantum system to that of a
dual model with inverted energies. Deriving from the fermion-parity
superselection postulate, this duality applies to a large class of open
systems.Comment: 5 pages + 19 pages of Supplementary Materia
Improved bounds for sparse recovery from adaptive measurements
It is shown here that adaptivity in sampling results in dramatic improvements in the recovery of sparse signals in white Gaussian noise. An adaptive sampling-and-refinement procedure called distilled sensing is discussed and analyzed, resulting in fundamental new asymptotic scaling relationships in terms of the minimum feature strength required for reliable signal detection or localization (support recovery). In particular, reliable detection and localization using non-adaptive samples is possible only if the feature strength grows logarithmically in the problem dimension. Here it is shown that using adaptive sampling, reliable detection is possible provided the feature strength exceeds a constant, and localization is possible when the feature strength exceeds any (arbitrarily slowly) growing function of the problem dimension
Transfer of a quantum state from a photonic qubit to a gate-defined quantum dot
Interconnecting well-functioning, scalable stationary qubits and photonic
qubits could substantially advance quantum communication applications and serve
to link future quantum processors. Here, we present two protocols for
transferring the state of a photonic qubit to a single-spin and to a two-spin
qubit hosted in gate-defined quantum dots (GDQD). Both protocols are based on
using a localized exciton as intermediary between the photonic and the spin
qubit. We use effective Hamiltonian models to describe the hybrid systems
formed by the the exciton and the GDQDs and apply simple but realistic noise
models to analyze the viability of the proposed protocols. Using realistic
parameters, we find that the protocols can be completed with a success
probability ranging between 85-97%
Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach
The partitioning of complex processor models on the gate and register-transfer level for parallel functional simulation based on the clock-cycle algorithm is considered. We introduce a hierarchical partitioning
scheme combining various partitioning algorithms in the frame of a competing strategy. Melting together different partitioning results within one level using superpositions we crossover to a mixture of experts
one. This approach is improved applying genetic algorithms. In addition we present two new partitioning algorithms both of them taking cones as fundamental units for building partitions
The zero effect: voxel-based lesion symptom mapping of number transcoding errors following stroke
Zero represents a special case in our numerical system because it is not represented on a semantic level. Former research has shown that this can lead to specific impairments when transcoding numerals from dictation to written digits. Even though, number processing is considered to be dominated by the left hemisphere, studies have indicated that both left as well as right hemispheric stroke patients commit errors when transcoding numerals including zeros. Here, for the first time, a large sample of subacute stroke patients (N = 667) was assessed without being preselected based on the location of their lesion, or a specific impairment in transcoding zero. The results show that specific errors in transcoding zeros were common (prevalence = 14.2%) and a voxel-based lesion symptom mapping analysis (n = 153) revealed these to be related to lesions in and around the right putamen. In line with former research, the present study argues that the widespread brain network for number processing also includes subcortical regions, like the putamen with connections to the insular cortex. These play a crucial role in auditory perception as well as attention. If these areas are lesioned, number processing tasks with higher attentional and working memory loads, like transcoding zeros, can be impaired
Periodic Pattern Detection for Real-Time Application
Abstract. Digital video stabilization approaches typically degrade their performances in presence of periodic patterns. Any kind of matching between consecutive frames is not usually able to work in presence of these kind of signals: the motion estimation engine is deceived and its performances degrade abruptly. In this paper we propose a fast fuzzy classifier able to recognize periodic and aperiodic pattern in the images that takes into account the peculiarities of digital video stabilization. Finally, the proposed classifier can be used as a filtering module in a block based video stabilization approach. Key words: Video Stabilization, periodic pattern, fuzzy classifier
Hierarchical Model Partitioning for Parallel VLSI-Simulation Using Evolutionary Algorithms improved bei superpositions of partitions
Parallelization of VLSI-simulation exploiting model-inherent parallelism is a promising way to accelerate verification processes for whole processor designs. Thereby partitioning of hardware models influences the effciency of following parallel simulations essentially. Based on a formal model of Parallel Cycle Simulation we introduce partition valuation combining communication and load balancing aspects. We choose a 2-level hierarchical partitioning scheme providing a framework for a mixture of experts strategy. Considering a complete model of a PowerPC 604 processor, we demonstrate that Evolutionary Algorithms can
be applied successfully to our model partitioning problem on the second hierarchy level, supposing a reduced problem complexity after fast pre-partitioning on the first level. For the first time, we apply superpositions during execution of Evolutionary Algorithms, resulting in a faster decreasing fitness function and an acceleration of population handling
Finding needles in noisy haystacks
The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles in haystacks), provided the measurements are noiseless. However, noise is almost always present in applications, and compressed sensing suffers from it. The signal to noise ratio per dimension using random projections is very poor, since sensing energy is equally distributed over all dimensions. Consequently, the ability of compressed sensing to locate sparse components degrades significantly as noise increases. It is possible, in principle, to improve performance by "shaping" the projections to focus sensing energy in proper dimensions. The main question addressed here is, can projections be adaptively shaped to achieve this focusing effect? The answer is yes, and we demonstrate a simple, computationally efficient procedure that does so
Topotecan-vincristine-doxorubicin in stage 4 high risk neuroblastoma patients failing to achieve a complete metastatic response to rapid COJEC : a SIOPEN study
Purpose : Metastatic response to induction therapy for high-risk neuroblastoma is a prognostic factor. In the International Society of Paediatric Oncology Europe Neuroblastoma (SIOPEN) HR-NBL-1 protocol, only patients with metastatic complete response (CR) or partial response (PR) with <= three abnormal skeletal areas on iodine 123-metaiodobenzylguanidine ([I-123] mIBG) scintigraphy and no bone marrow disease proceed to high dose therapy (HDT). In this study, topotecan-vincristine-doxorubicin (TVD) was evaluated in patients failing to achieve these criteria, with the aim of improving the metastatic response rate.
Materials and Methods : Patients with metastatic high-risk neuroblastoma who had not achieved the SIOPEN criteria for HDT after induction received two courses of topotecan 1.5 mg/m(2)/day for 5 days, followed by a 48-hour infusion of vincristine, 2 mg/m(2), and doxorubicin, 45 mg/m(2).
Results : Sixty-three patients were eligible and evaluable. Following two courses of TVD, four (6.4%) patients had an overall CR, while 28 (44.4%) had a PR with a combined response rate of 50.8% (95% confidence interval [CI], 37.9 to 63.6). Of these, 23 patients achieved a metastatic CR or a PR with <= 3 mIBG skeletal areas and no bone marrow disease (36.5%; 95% CI, 24.7 to 49.6) and were eligible to receive HDT. Toxicity was mostly haematological, affecting 106 of the 126 courses (84.1%; 95% CI, 76.5 to 90.0), and dose reduction was necessary in six patients. Stomatitis was the second most common nonhematological toxicity, occurring in 20 patients (31.7%).
Conclusion : TVD was effective in improving the response rate of high-risk neuroblastoma patients after induction with COJEC enabling them to proceed to HDT. However, the long-term benefits of TVD needs to be determined in randomized clinical trials
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