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

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

On the complexity of undominated core and farsighted solution concepts in coalition games

ABSTRACT In this paper, we study the computational complexity of solution concepts in the context of coalitional games. Firstly, we distinguish two different kinds of core, the undominated core and excess core, and investigate the difference and relationship between them. Secondly, we thoroughly investigate the computational complexity of undominated core and three farsighted solution concepts-farsighted core, farsighted stable set and largest consistent set