2,347 research outputs found
Suppression of low-energy Andreev states by a supercurrent in YBa_2Cu_3O_7-delta
We report a coherence-length scale phenomenon related to how the high-Tc
order parameter (OP) evolves under a directly-applied supercurrent. Scanning
tunneling spectroscopy was performed on current-carrying YBa_2Cu_3O_7-delta
thin-film strips at 4.2K. At current levels well below the theoretical
depairing limit, the low-energy Andreev states are suppressed by the
supercurrent, while the gap-like structures remain unchanged. We rule out the
likelihood of various extrinsic effects, and propose instead a model based on
phase fluctuations in the d-wave BTK formalism to explain the suppression. Our
results suggest that a supercurrent could weaken the local phase coherence
while preserving the pairing amplitude. Other possible scenarios which may
cause the observed phenomenon are also discussed.Comment: 6 pages, 4 figures, to appear in Physical Review
Topological kink states at a tilt boundary in gated multi-layer graphene
The search for new realization of topologically protected edge states is an
active area of research. We show that a tilt boundary in gated multi-layer
graphene supports topologically protected gapless kink states, associated with
quantum valley Hall insulator (QVH). We investigate such kink states from two
perspectives: the microscopic perspective of a tight-binding model and an
ab-initio calculation on bilayer, and the perspective of symmetry protected
topological (SPT) states for general multi-layer. We show that a AB-BA bilayer
tilt boundary supports gapless kink states that are undeterred by strain
concentrated at the boundary. Further, we establish the kink states as concrete
examples of edge states of {\it time-reversal symmetric} -type
SPT, protected by no valley mixing, electron number conservation, and time
reversal symmetries. This allows us to discuss possible phase transitions
upon symmetry changes from the SPT perspective. Recent experimental
observations of a network of such tilt boundaries suggest that transport
through these novel topological kink states might explain the long standing
puzzle of sub-gap conductance. Further, recent observation of gap closing and
re-opening in gated bi-layer might be the first example of a transition between
two distinct SPT's: QVH and LAF.Comment: Improved a discussion of the structural aspects of the tilt boundary.
Included a discussion of boundary condition dependence. Added new section on
connection to experiment
Exploiting Features and Logits in Heterogeneous Federated Learning
Due to the rapid growth of IoT and artificial intelligence, deploying neural
networks on IoT devices is becoming increasingly crucial for edge intelligence.
Federated learning (FL) facilitates the management of edge devices to
collaboratively train a shared model while maintaining training data local and
private. However, a general assumption in FL is that all edge devices are
trained on the same machine learning model, which may be impractical
considering diverse device capabilities. For instance, less capable devices may
slow down the updating process because they struggle to handle large models
appropriate for ordinary devices. In this paper, we propose a novel data-free
FL method that supports heterogeneous client models by managing features and
logits, called Felo; and its extension with a conditional VAE deployed in the
server, called Velo. Felo averages the mid-level features and logits from the
clients at the server based on their class labels to provide the average
features and logits, which are utilized for further training the client models.
Unlike Felo, the server has a conditional VAE in Velo, which is used for
training mid-level features and generating synthetic features according to the
labels. The clients optimize their models based on the synthetic features and
the average logits. We conduct experiments on two datasets and show
satisfactory performances of our methods compared with the state-of-the-art
methods
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Byzantine-robust federated learning aims at mitigating Byzantine failures
during the federated training process, where malicious participants may upload
arbitrary local updates to the central server to degrade the performance of the
global model. In recent years, several robust aggregation schemes have been
proposed to defend against malicious updates from Byzantine clients and improve
the robustness of federated learning. These solutions were claimed to be
Byzantine-robust, under certain assumptions. Other than that, new attack
strategies are emerging, striving to circumvent the defense schemes. However,
there is a lack of systematic comparison and empirical study thereof. In this
paper, we conduct an experimental study of Byzantine-robust aggregation schemes
under different attacks using two popular algorithms in federated learning,
FedSGD and FedAvg . We first survey existing Byzantine attack strategies and
Byzantine-robust aggregation schemes that aim to defend against Byzantine
attacks. We also propose a new scheme, ClippedClustering , to enhance the
robustness of a clustering-based scheme by automatically clipping the updates.
Then we provide an experimental evaluation of eight aggregation schemes in the
scenario of five different Byzantine attacks. Our results show that these
aggregation schemes sustain relatively high accuracy in some cases but are
ineffective in others. In particular, our proposed ClippedClustering
successfully defends against most attacks under independent and IID local
datasets. However, when the local datasets are Non-IID, the performance of all
the aggregation schemes significantly decreases. With Non-IID data, some of
these aggregation schemes fail even in the complete absence of Byzantine
clients. We conclude that the robustness of all the aggregation schemes is
limited, highlighting the need for new defense strategies, in particular for
Non-IID datasets.Comment: This paper has been accepted for publication in IEEE Transactions on
Big Dat
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