1,513 research outputs found
Majorana Edge States in Interacting Two-chain Ladders of Fermions
In this work we study interacting spinless fermions on a two-chain ladder
with inter-chain pair tunneling while single-particle tunneling is suppressed
at low energy. The model embodies a symmetry associated with the
fermion parity on each chain. We find that when the system is driven to the
strong-coupling phase by the pair tunneling, Majorana excitations appear on the
boundary. Such Majorana edge states correspond to two-fold degeneracy of ground
states distinguished by different fermion parity on each chain, thus
representing a generalization of one-dimensional topological superconductors.
We also characterize the stability of the ground state degeneracy against local
perturbations. Lattice fermion models realizing such effective field theory are
discussed.Comment: 6 pages, 1 figur
Criticality in Translation-Invariant Parafermion Chains
In this work we numerically study critical phases in translation-invariant
parafermion chains with both nearest- and next-nearest-neighbor
hopping terms. The model can be mapped to a spin model with
nearest-neighbor couplings via a generalized Jordan-Wigner transformation and
translation invariance ensures that the spin model is always self-dual. We
first study the low-energy spectrum of chains with only nearest-neighbor
coupling, which are mapped onto standard self-dual clock models.
For we match the numerical results to the known conformal field
theory(CFT) identification. We then analyze in detail the phase diagram of a
chain with both nearest and next-nearest neighbor hopping and six
critical phases with central charges being , 1 or 2 are found. We find
continuous phase transitions between and phases, while the phase
transition between and is conjectured to be of
Kosterlitz-Thouless type.Comment: published versio
Topology and Criticality in Resonating Affleck-Kennedy-Lieb-Tasaki loop Spin Liquid States
We exploit a natural Projected Entangled-Pair State (PEPS) representation for
the resonating Affleck-Kennedy-Lieb-Tasaki loop (RAL) state. By taking
advantage of PEPS-based analytical and numerical methods, we characterize the
RAL states on various two-dimensional lattices. On square and honeycomb
lattices, these states are critical since the dimer-dimer correlations decay as
a power law. On kagome lattice, the RAL state has exponentially decaying
correlation functions, supporting the scenario of a gapped spin liquid. We
provide further evidence that the RAL state on the kagome lattice is a
spin liquid, by identifying the four topological sectors and
computing the topological entropy. Furthermore, we construct a one-parameter
family of PEPS states interpolating between the RAL state and a short-range
Resonating Valence Bond state and find a critical point, consistent with the
fact that the two states belong to two different phases. We also perform a
variational study of the spin-1 kagome Heisenberg model using this
one-parameter PEPS.Comment: 10 pages, 14 figures, published versio
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning
Intermediate features of a pre-trained model have been shown informative for
making accurate predictions on downstream tasks, even if the model backbone is
kept frozen. The key challenge is how to utilize these intermediate features
given their gigantic amount. We propose visual query tuning (VQT), a simple yet
effective approach to aggregate intermediate features of Vision Transformers.
Through introducing a handful of learnable ``query'' tokens to each layer, VQT
leverages the inner workings of Transformers to ``summarize'' rich intermediate
features of each layer, which can then be used to train the prediction heads of
downstream tasks. As VQT keeps the intermediate features intact and only learns
to combine them, it enjoys memory efficiency in training, compared to many
other parameter-efficient fine-tuning approaches that learn to adapt features
and need back-propagation through the entire backbone. This also suggests the
complementary role between VQT and those approaches in transfer learning.
Empirically, VQT consistently surpasses the state-of-the-art approach that
utilizes intermediate features for transfer learning and outperforms full
fine-tuning in many cases. Compared to parameter-efficient approaches that
adapt features, VQT achieves much higher accuracy under memory constraints.
Most importantly, VQT is compatible with these approaches to attain even higher
accuracy, making it a simple add-on to further boost transfer learning.Comment: Accepted by CVPR 2023. Cheng-Hao Tu and Zheda Mai contributed equally
to this wor
On the Importance and Applicability of Pre-Training for Federated Learning
Pre-training is prevalent in nowadays deep learning to improve the learned
model's performance. However, in the literature on federated learning (FL),
neural networks are mostly initialized with random weights. These attract our
interest in conducting a systematic study to explore pre-training for FL.
Across multiple visual recognition benchmarks, we found that pre-training can
not only improve FL, but also close its accuracy gap to the counterpart
centralized learning, especially in the challenging cases of non-IID clients'
data. To make our findings applicable to situations where pre-trained models
are not directly available, we explore pre-training with synthetic data or even
with clients' data in a decentralized manner, and found that they can already
improve FL notably. Interesting, many of the techniques we explore are
complementary to each other to further boost the performance, and we view this
as a critical result toward scaling up deep FL for real-world applications. We
conclude our paper with an attempt to understand the effect of pre-training on
FL. We found that pre-training enables the learned global models under
different clients' data conditions to converge to the same loss basin, and
makes global aggregation in FL more stable. Nevertheless, pre-training seems to
not alleviate local model drifting, a fundamental problem in FL under non-IID
data.Comment: Preprin
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection
We propose ImGeoNet, a multi-view image-based 3D object detection framework
that models a 3D space by an image-induced geometry-aware voxel representation.
Unlike previous methods which aggregate 2D features into 3D voxels without
considering geometry, ImGeoNet learns to induce geometry from multi-view images
to alleviate the confusion arising from voxels of free space, and during the
inference phase, only images from multiple views are required. Besides, a
powerful pre-trained 2D feature extractor can be leveraged by our
representation, leading to a more robust performance. To evaluate the
effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments
on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The
results demonstrate that ImGeoNet outperforms the current state-of-the-art
multi-view image-based method, ImVoxelNet, on all three datasets in terms of
detection accuracy. In addition, ImGeoNet shows great data efficiency by
achieving results comparable to ImVoxelNet with 100 views while utilizing only
40 views. Furthermore, our studies indicate that our proposed image-induced
geometry-aware representation can enable image-based methods to attain superior
detection accuracy than the seminal point cloud-based method, VoteNet, in two
practical scenarios: (1) scenarios where point clouds are sparse and noisy,
such as in ARKitScenes, and (2) scenarios involve diverse object classes,
particularly classes of small objects, as in the case in ScanNet200.Comment: ICCV'23; project page: https://ttaoretw.github.io/imgeonet
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