128 research outputs found
Understanding Data Augmentation from a Robustness Perspective
In the realm of visual recognition, data augmentation stands out as a pivotal
technique to amplify model robustness. Yet, a considerable number of existing
methodologies lean heavily on heuristic foundations, rendering their intrinsic
mechanisms ambiguous. This manuscript takes both a theoretical and empirical
approach to understanding the phenomenon. Theoretically, we frame the discourse
around data augmentation within game theory's constructs. Venturing deeper, our
empirical evaluations dissect the intricate mechanisms of emblematic data
augmentation strategies, illuminating that these techniques primarily stimulate
mid- and high-order game interactions. Beyond the foundational exploration, our
experiments span multiple datasets and diverse augmentation techniques,
underscoring the universal applicability of our findings. Recognizing the vast
array of robustness metrics with intricate correlations, we unveil a
streamlined proxy. This proxy not only simplifies robustness assessment but
also offers invaluable insights, shedding light on the inherent dynamics of
model game interactions and their relation to overarching system robustness.
These insights provide a novel lens through which we can re-evaluate model
safety and robustness in visual recognition tasks.Comment: Not published yet. arXiv admin note: text overlap with
arXiv:2212.0405
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for
out-of-distribution inputs when deployed in the open world. It is common
practice to leverage a surrogate outlier dataset to regularize the model during
training, and recent studies emphasize the role of uncertainty in designing the
sampling strategy for outlier dataset. However, the OOD samples selected solely
based on predictive uncertainty can be biased towards certain types, which may
fail to capture the full outlier distribution. In this work, we empirically
show that diversity is critical in sampling outliers for OOD detection
performance. Motivated by the observation, we propose a straightforward and
novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse
and informative outliers. Specifically, we cluster the normalized features at
each iteration, and the most informative outlier from each cluster is selected
for model training with absent category loss. With DOS, the sampled outliers
efficiently shape a globally compact decision boundary between ID and OOD data.
Extensive experiments demonstrate the superiority of DOS, reducing the average
FPR95 by up to 25.79% on CIFAR-100 with TI-300K
Tuning the Morphological Structure and Photocatalytic Activity of Nitrogen-Doped (BiO) 2
Various nitrogen-doped hierarchical (BiO)2CO3 nanosheets architectures were synthesized by a facile one-step template-free hydrothermal method through controlling the hydrothermal temperature (HT). The as-synthesized samples were characterized by XRD, SEM, FT-IR, XPS, and UV-vis DRS. The photocatalytic activity of the samples was evaluated towards degradation of NO at ppb level in air under visible light (VIL). It was found that HT acted as a crucial factor in determining the morphology of the samples. The rosa chinensis-like, red camellia-like, and lamina-like of nitrogen-doped (BiO)2CO3 (N-BOC) micro-/nanostructures can be selectively fabricated under hydrothermal temperatures of 150, 180, and 210°C. The thickness of the nanosheets was in direct proportion to the increasing HT. Nitrogen-doping can extend the light absorption spectra of (BiO)3CO3 to visible light region and enhance the VIL photocatalytic activity. Especially, the red camellia-like N-BOC-180 exhibited the highest photocatalytic performance, superior to the well-known VIL-driven photocatalyst C-doped TiO2 and N-doped TiO2. The high photocatalytic performance of N-BOC was attributed to the synergetic effects of enhanced visible light absorption, multiple light-reflections between the nanosheets, and accelerated transfer of reactants and product. This research could provide new insights to the development of excellent photocatalyst with efficient performance for pollution control
Many could be better than all: A novel instance-oriented algorithm for Multi-modal Multi-label problem
With the emergence of diverse data collection techniques, objects in real
applications can be represented as multi-modal features. What's more, objects
may have multiple semantic meanings. Multi-modal and Multi-label (MMML) problem
becomes a universal phenomenon. The quality of data collected from different
channels are inconsistent and some of them may not benefit for prediction. In
real life, not all the modalities are needed for prediction. As a result, we
propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm
for MMML problem, which can make convince prediction with partial modalities.
MCC extracts different modalities for different instances in the testing phase.
Extensive experiments are performed on one real-world herbs dataset and two
public datasets to validate our proposed algorithm, which reveals that it may
be better to extract many instead of all of the modalities at hand.Comment: To be published in ICME 201
Near-Field Communications: A Tutorial Review
Extremely large-scale antenna arrays, tremendously high frequencies, and new
types of antennas are three clear trends in multi-antenna technology for
supporting the sixth-generation (6G) networks. To properly account for the new
characteristics introduced by these three trends in communication system
design, the near-field spherical-wave propagation model needs to be used, which
differs from the classical far-field planar-wave one. As such, near-field
communication (NFC) will become essential in 6G networks. In this tutorial, we
cover three key aspects of NFC. 1) Channel Modelling: We commence by reviewing
near-field spherical-wave-based channel models for spatially-discrete (SPD)
antennas. Then, uniform spherical wave (USW) and non-uniform spherical wave
(NUSW) models are discussed. Subsequently, we introduce a general near-field
channel model for SPD antennas and a Green's function-based channel model for
continuous-aperture (CAP) antennas. 2) Beamfocusing and Antenna Architectures:
We highlight the properties of near-field beamfocusing and discuss NFC antenna
architectures for both SPD and CAP antennas. Moreover, the basic principles of
near-field beam training are introduced. 3) Performance Analysis: Finally, we
provide a comprehensive performance analysis framework for NFC. For near-field
line-of-sight channels, the received signal-to-noise ratio and power-scaling
law are derived. For statistical near-field multipath channels, a general
analytical framework is proposed, based on which analytical expression for the
outage probability, ergodic channel capacity, and ergodic mutual information
are derived. Finally, for each aspect, the topics for future research are
discussed.Comment: 45 pages, 35 figures; submitted to possible IEEE journa
Near-Field Communications: A Comprehensive Survey
Multiple-antenna technologies are evolving towards large-scale aperture
sizes, extremely high frequencies, and innovative antenna types. This evolution
is giving rise to the emergence of near-field communications (NFC) in future
wireless systems. Considerable attention has been directed towards this
cutting-edge technology due to its potential to enhance the capacity of
wireless networks by introducing increased spatial degrees of freedom (DoFs) in
the range domain. Within this context, a comprehensive review of the state of
the art on NFC is presented, with a specific focus on its 1) fundamental
operating principles, 2) channel modeling, 3) performance analysis, 4) signal
processing, and 5) integration with other emerging technologies. Specifically,
1) the basic principles of NFC are characterized from both physics and
communications perspectives, unveiling its unique properties in contrast to
far-field communications. 2) Based on these principles, deterministic and
stochastic near-field channel models are investigated for spatially-discrete
(SPD) and continuous-aperture (CAP) antenna arrays. 3) Rooted in these models,
existing contributions on near-field performance analysis are reviewed in terms
of DoFs/effective DoFs (EDoFs), power scaling law, and transmission rate. 4)
Existing signal processing techniques for NFC are systematically surveyed,
encompassing channel estimation, beamforming design, and low-complexity beam
training. 5) Major issues and research opportunities associated with the
integration of NFC and other emerging technologies are identified to facilitate
NFC applications in next-generation networks. Promising directions are
highlighted throughout the paper to inspire future research endeavors in the
realm of NFC.Comment: 56 pages, 23figures; submit for possible journa
Combining Electrochemical Nitrate Reduction and Anammox for Treatment of Nitrate-Rich Wastewater: A Short Review
Treatment of nitrate-rich wastewater is important but challenging for the conventional biological denitrification process. Here, we propose combining the electrochemical reduction and anaerobic ammonium oxidation (anammox) processes together for treatment of nitrate-rich wastewater. This article reviews the mechanism and current research status of electrochemical reduction of nitrate to ammonium as well as the mechanism and applicability of the anammox process. This article discusses the principles, superiorities, and challenges of this combined process. The feasibility of the combined process depends on the efficiency of electrochemical nitrate reduction to ammonium and the conditions in the anammox process to use the reduced ammonium as the substrate to achieve deep nitrogen removal. The article provides a feasible strategy for using the electrochemical reduction and anammox combined process to treat nitrate-rich wastewater
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