228 research outputs found
Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation
Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic
Segmentation (FSS) to simultaneously segment unseen classes and seen classes
during evaluation. Previous works leverage additional branch or prototypical
aggregation to eliminate the constrained setting of FSS. However,
representation division and embedding prejudice, which heavily results in poor
performance of GFSS, have not been synthetical considered. We address the
aforementioned problems by jointing the prototypical kernel learning and
open-set foreground perception. Specifically, a group of learnable kernels is
proposed to perform segmentation with each kernel in charge of a stuff class.
Then, we explore to merge the prototypical learning to the update of base-class
kernels, which is consistent with the prototype knowledge aggregation of
few-shot novel classes. In addition, a foreground contextual perception module
cooperating with conditional bias based inference is adopted to perform
class-agnostic as well as open-set foreground detection, thus to mitigate the
embedding prejudice and prevent novel targets from being misclassified as
background. Moreover, we also adjust our method to the Class Incremental
Few-shot Semantic Segmentation (CIFSS) which takes the knowledge of novel
classes in a incremental stream. Extensive experiments on PASCAL-5i and
COCO-20i datasets demonstrate that our method performs better than previous
state-of-the-art.Comment: Accepted by ICCV202
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling
Personalized chatbots focus on endowing the chatbots with a consistent
personality to behave like real users and further act as personal assistants.
Previous studies have explored generating implicit user profiles from the
user's dialogue history for building personalized chatbots. However, these
studies only use the response generation loss to train the entire model, thus
it is prone to suffer from the problem of data sparsity. Besides, they
overemphasize the final generated response's quality while ignoring the
correlations and fusions between the user's dialogue history, leading to rough
data representations and performance degradation. To tackle these problems, we
propose a self-supervised learning framework MCP for capturing better
representations from users' dialogue history for personalized chatbots.
Specifically, we apply contrastive sampling methods to leverage the supervised
signals hidden in user dialog history, and generate the pre-training samples
for enhancing the model. We design three pre-training tasks based on three
types of contrastive pairs from user dialogue history, namely response pairs,
sequence augmentation pairs, and user pairs. We pre-train the utterance encoder
and the history encoder towards the contrastive objectives and use these
pre-trained encoders for generating user profiles while personalized response
generation. Experimental results on two real-world datasets show a significant
improvement in our proposed model MCP compared with the existing methods
SeDR: Segment Representation Learning for Long Documents Dense Retrieval
Recently, Dense Retrieval (DR) has become a promising solution to document
retrieval, where document representations are used to perform effective and
efficient semantic search. However, DR remains challenging on long documents,
due to the quadratic complexity of its Transformer-based encoder and the finite
capacity of a low-dimension embedding. Current DR models use suboptimal
strategies such as truncating or splitting-and-pooling to long documents
leading to poor utilization of whole document information. In this work, to
tackle this problem, we propose Segment representation learning for long
documents Dense Retrieval (SeDR). In SeDR, Segment-Interaction Transformer is
proposed to encode long documents into document-aware and segment-sensitive
representations, while it holds the complexity of splitting-and-pooling and
outperforms other segment-interaction patterns on DR. Since GPU memory
requirements for long document encoding causes insufficient negatives for DR
training, Late-Cache Negative is further proposed to provide additional cache
negatives for optimizing representation learning. Experiments on MS MARCO and
TREC-DL datasets show that SeDR achieves superior performance among DR models,
and confirm the effectiveness of SeDR on long document retrieval
Quantum Multicritical Behavior for Coupled Optical Cavities with Driven Laser Fields
Quantum phase transitions with multicritical points are fascinating phenomena
occurring in interacting quantum many-body systems. However, multicritical
points predicted by theory have been rarely verified experimentally; finding
multicritical points with specific behaviors and realizing their control
remains a challenging topic. Here, we propose a system that a quantized light
field interacts with a two-level atomic ensemble coupled by microwave fields in
optical cavities, which is described by a generalized Dicke model.
Multicritical points for the superradiant quantum phase transition are shown to
occur. We determine the number and position of these critical points and
demonstrate that they can be effectively manipulated through the tuning of
system parameters. Particularly, we find that the quantum critical points can
evolve into a Lifshitz point if the Rabi frequency of the light field is
modulated periodically in time. Remarkably, the texture of atomic pseudo-spins
can be used to characterize the quantum critical behaviors of the system. The
magnetic orders of the three phases around the Lifshitz point, represented by
the atomic pseudo-spins, are similar to those of an axial
next-nearest-neighboring Ising model. The results reported here are beneficial
for unveiling intriguing physics of quantum phase transitions and pave the way
towards to find novel quantum multicritical phenomena based on the generalized
Dicke model
Superconducting fluctuations and charge-4 plaquette state at strong coupling
Recent experiments indicate that superconducting fluctuations also play an
important role in overdoped cuprates. Here we apply the static auxiliary field
Monte Carlo approach to study phase correlations of the pairing fields in a
microscopic model with spin-singlet pairing interaction. We find that the
short- and long-range phase correlations are well captured by the phase mutual
information, which allows us to construct a theoretical phase diagram
containing the uniform -wave superconducting region, the phase fluctuating
region, the local pairing region, and the disordered region. We show that the
gradual development of phase coherence has a number of consequences on
spectroscopic measurements, such as the development of the Fermi arc and the
anisotropy in the angle-resolved spectra, scattering rate, entropy, specific
heat, and quasiparticle dispersion, in good agreement with experimental
observations. For strong coupling, our Monte Carlo simulation reveals an
unexpected charge-4 plaquette state with -wave bonds, which competes with
the uniform -wave superconductivity and exhibits a U-shaped density of
states
ConvGQR: Generative Query Reformulation for Conversational Search
In conversational search, the user's real search intent for the current turn
is dependent on the previous conversation history. It is challenging to
determine a good search query from the whole conversation context. To avoid the
expensive re-training of the query encoder, most existing methods try to learn
a rewriting model to de-contextualize the current query by mimicking the manual
query rewriting. However, manually rewritten queries are not always the best
search queries. Training a rewriting model on them would limit the model's
ability to produce good search queries. Another useful hint is the potential
answer to the question. In this paper, we propose ConvGQR, a new framework to
reformulate conversational queries based on generative pre-trained language
models (PLMs), one for query rewriting and another for generating potential
answers. By combining both, ConvGQR can produce better search queries. In
addition, to relate query reformulation to retrieval performance, we propose a
knowledge infusion mechanism to optimize both query reformulation and
retrieval. Extensive experiments on four conversational search datasets
demonstrate the effectiveness of ConvGQR.Comment: Published at ACL 202
Highly pathogenic avian influenza A virus H5N1 NS1 protein induces caspase-dependent apoptosis in human alveolar basal epithelial cells
<p>Abstract</p> <p>Background</p> <p>It is widely considered that the multifunctional NS1 protein of influenza A viruses contributes significantly disease pathogenesis by modulating a number of virus and host-cell processes, but it is highly controversial whether this non-structural protein is a proapoptotic or antiapoptotic factor in infected cells.</p> <p>Results</p> <p>NS1 protein of influenza A/chicken/Jilin/2003 virus, a highly pathogenic H5N1 strain, could induce apoptosis in the carcinomic human alveolar basal epithelial cells (A549) by electron microscopic and flow cytometric analyses. NS1 protein-triggered apoptosis in A549 cells is via caspase-dependent pathway.</p> <p>Conclusions</p> <p>Influenza A virus NS1 protein serves as a strong inducer of apoptosis in infected human respiratory epithelial cells and plays a critical role in disease pathogenesis.</p
Heteroatom-doped core/shell carbonaceous framework materials : synthesis, characterization and electrochemical properties
Organic-inorganic hybrid core@shell nanospherical particles with 200 nm to 600 nm in diameter were formed between cyclomatrix poly(organophosphazenes) (POP) and zeolitic imidazolate framework-8 (ZIF-8) in a methanol solution at room temperature. This facile synthesis route produced core@shell spheres with controlled structure and properties, such as mono-dispersed particles, 50 nm to 100 nm shell thickness, surface area of 1557 m2 g-1 and homogenously doped Zn and heteroatoms (N, S, P, O, Cl). The POP/ZIF-8 core@shell structures were subsequently converted into porous carbonaceous materials, and investigated as anode materials in a lithium-ion coin cell battery. It showed a stable discharge capacity of 538 mA h g-1 over 250 cycles, high rate capability (0.1 C to 1 C) and excellent capacity retention, which are promising for rapid charge-discharge applications. Higher ZIF-8 loading ratio in the core@shell structure increased the capacity of the electrode material and stablised the lithiated active materials. The facile synthesis method and the carbonaceous framework materials are applicable for high performance energy storage materials in electrochemical power devices
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