90 research outputs found
Scaling of entanglement at quantum phase transition for two-dimensional array of quantum dots
With Hubbard model, the entanglement scaling behavior in a two-dimensional
itinerant system is investigated. It has been found that, on the two sides of
the critical point denoting an inherent quantum phase transition (QPT), the
entanglement follows different scalings with the size just as an order
parameter does. This fact reveals the subtle role played by the entanglement in
QPT as a fungible physical resource
Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement
We consider the problem of accurately identifying cell boundaries and
labeling individual cells in confocal microscopy images, specifically, 3D image
stacks of cells with tagged cell membranes. Precise identification of cell
boundaries, their shapes, and quantifying inter-cellular space leads to a
better understanding of cell morphogenesis. Towards this, we outline a cell
segmentation method that uses a deep neural network architecture to extract a
confidence map of cell boundaries, followed by a 3D watershed algorithm and a
final refinement using a conditional random field. In addition to improving the
accuracy of segmentation compared to other state-of-the-art methods, the
proposed approach also generalizes well to different datasets without the need
to retrain the network for each dataset. Detailed experimental results are
provided, and the source code is available on GitHub.Comment: 5 pages, 5 figures, 3 table
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
Federated learning (FL) is a promising paradigm to enable collaborative model
training with decentralized data. However, the training process of Large
Language Models (LLMs) generally incurs the update of significant parameters,
which limits the applicability of FL techniques to tackle the LLMs in real
scenarios. Prompt tuning can significantly reduce the number of parameters to
update, but it either incurs performance degradation or low training
efficiency. The straightforward utilization of prompt tuning in the FL often
raises non-trivial communication costs and dramatically degrades performance.
In addition, the decentralized data is generally non-Independent and
Identically Distributed (non-IID), which brings client drift problems and thus
poor performance. This paper proposes a Parameter-efficient prompt Tuning
approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and
effective FL of LLMs. First, an efficient partial prompt tuning approach is
proposed to improve performance and efficiency simultaneously. Second, a novel
adaptive optimization method is developed to address the client drift problems
on both the device and server sides to enhance performance further. Extensive
experiments based on 10 datasets demonstrate the superb performance (up to
60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training
time) of FedPepTAO compared with 9 baseline approaches. Our code is available
at https://github.com/llm-eff/FedPepTAO.Comment: 18 pages, accepted by EMNLP 202
Recommended from our members
High yield and ultrafast sources of electrically triggered entangled-photon pairs based on strain-tunable quantum dots
Triggered sources of entangled photon pairs are key components in most quantum communication protocols. For practical quantum applications, electrical triggering would allow the realization of compact and deterministic sources of entangled photons. Entangled-light-emitting-diodes based on semiconductor quantum dots are among the most promising sources that can potentially address this task. However, entangled-light-emitting-diodes are plagued by a source of randomness, which results in a very low probability of finding quantum dots with sufficiently small fine structure splitting for entangled-photon generation (∼10−2). Here we introduce strain-tunable entangled-light-emitting-diodes that exploit piezoelectric-induced strains to tune quantum dots for entangled-photon generation. We demonstrate that up to 30% of the quantum dots in strain-tunable entangled-light-emitting-diodes emit polarization-entangled photons. An entanglement fidelity as high as 0.83 is achieved with fast temporal post selection. Driven at high speed, that is 400 MHz, strain-tunable entangled-light-emitting-diodes emerge as promising devices for high data-rate quantum applications
Strain-tunable entangled-light-emitting diodes with high yield and fast operation speed
Triggered sources of entangled photons play crucial roles in almost any
existing protocol of quantum information science. The possibility to generate
these non-classical states of light with high speed and using electrical pulses
could revolutionize the field. Entangled-light-emitting-diodes (ELEDs) based on
semiconductor quantum dots (QDs) are at present the only devices that can
address this task 5. However, ELEDs are plagued by a source of randomness that
hampers their practical exploitation in the foreseen applications: the very low
probability (~10-2) of finding QDs with sufficiently small
fine-structure-splitting for entangled-photon-generation. Here, we overcome
this hurdle by introducing the first strain-tunable ELEDs (S-ELEDs) that
exploit piezoelectric-induced strains to tune QDs for
entangled-photon-generation. We demonstrate that up to 30% of the QDs in
S-ELEDs emit polarization-entangled photon pairs with entanglement-fidelities
as high as f+ = 0.83(5). Driven at the highest operation speed of 400 MHz ever
reported so far, S-ELEDs emerge as unique devices for high-data rate
entangled-photon applications.Comment: 28 pages in total, including supplementary information. 5 figure
The role of the amygdala during emotional processing in Huntington's disease: from pre-manifest to late stage disease.
BACKGROUND: Deficits in emotional processing can be detected in the pre-manifest stage of Huntington's disease and negative emotion recognition has been identified as a predictor of clinical diagnosis. The underlying neuropathological correlates of such deficits are typically established using correlative structural MRI studies. This approach does not take into consideration the impact of disruption to the complex interactions between multiple brain circuits on emotional processing. Therefore, exploration of the neural substrates of emotional processing in pre-manifest HD using fMRI connectivity analysis may be a useful way of evaluating the way brain regions interrelate in the period prior to diagnosis. METHODS: We investigated the impact of predicted time to disease onset on brain activation when participants were exposed to pictures of faces with angry and neutral expressions, in 20 pre-manifest HD gene carriers and 23 healthy controls. On the basis of the results of this initial study went on to look at amygdala dependent cognitive performance in 79 Huntington's disease patients from a cross-section of disease stages (pre-manifest to late disease) and 26 healthy controls, using a validated theory of mind task: "the Reading the Mind in the Eyes Test" which has been previously been shown to be amygdala dependent. RESULTS: Psychophysiological interaction analysis identified reduced connectivity between the left amygdala and right fusiform facial area in pre-manifest HD gene carriers compared to controls when viewing angry compared to neutral faces. Change in PPI connectivity scores correlated with predicted time to disease onset (r=0.45, p<0.05). Furthermore, performance on the "Reading the Mind in the Eyes Test" correlated negatively with proximity to disease onset and became progressively worse with each stage of disease. CONCLUSION: Abnormalities in the neural networks underlying social cognition and emotional processing can be detected prior to clinical diagnosis in Huntington's disease. Connectivity between the amygdala and other brain regions is impacted by the disease process in pre-manifest HD and may therefore be a useful way of identifying participants who are approaching a clinical diagnosis. Furthermore, the "Reading the Mind in the Eyes Test" is a surrogate measure of amygdala function that is clinically useful across the entire cross-section of disease stages in HD.The work included in this manuscript has been partially funded by financial support from the NIHR Cambridge Biomedical Research Centre and the Cambridge University NHS Foundation Trust. JBR is supported by the Wellcome Trust (088324).This is the final version of the article. It first appeared at http://dx.doi.org/10.1016/j.neuropsychologia.2015.02.01
Electrically-Pumped Wavelength-Tunable GaAs Quantum Dots Interfaced with Rubidium Atoms
We demonstrate the first wavelength-tunable electrically-pumped source of
non-classical light that can emit photons with wavelength in resonance with the
D2 transitions of 87Rb atoms. The device is fabricated by integrating a novel
GaAs single-quantum-dot light-emitting-diode (LED) onto a piezoelectric
actuator. By feeding the emitted photons into a 75-mm-long cell containing warm
87Rb atom vapor, we observe slow-light with a temporal delay of up to 3.4 ns.
In view of the possibility of using 87Rb atomic vapors as quantum memories,
this work makes an important step towards the realization of hybrid-quantum
systems for future quantum networks
The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure
Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV
Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T
Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus
- …