Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implicit knowledge to guide the learning of a smaller network. At first glance, these two techniques seem very different, however, we found that "smoothness" is the connecting link between the two and is also a crucial attribute in understanding KD's interplay with mixup. Although many mixup variants and distillation methods have been proposed, much remains to be understood regarding the role of a mixup in knowledge distillation. In this paper, we present a detailed empirical study on various important dimensions of compatibility between mixup and knowledge distillation. We also scrutinize the behavior of the networks trained with a mixup in the light of knowledge distillation through extensive analysis, visualizations, and comprehensive experiments on image classification. Finally, based on our findings, we suggest improved strategies to guide the student network to enhance its effectiveness. Additionally, the findings of this study provide insightful suggestions to researchers and practitioners that commonly use techniques from KD. Our code is available at https://github.com/hchoi71/MIX-KD.Comment: To be presented at WACV 202

Optothermal Trapping of Fluorescent Nanodiamonds using a Drop-casted Gold Nanoparticle

Deterministic optical manipulation of fluorescent nanodiamonds (FNDs) in a fluid environment has emerged as an experimental challenge in multimodal biological imaging. The design and development of nano-optical trapping strategies to serve this purpose is an important task. In this letter, we show how a drop-casted gold nanoparticle (Au np) can facilitate optothermal potential to trap individual entities of FNDs using a low power density illumination (532nm laser, 0.1 mW/$\mu$m$^2$). We utilize the same trapping excitation source to capture the spectral signatures of single FNDs and track their position. Furthermore, by tracking the dynamics of FND, we measure the trapping stiffness as a function of laser power and surfactant concentration and emphasize their relevance as vital parameters for nano-manipulation. Our trapping configuration combines the thermoplasmonic fields generated by individual gold nanoparticles and the optothermoelectric effect facilitated by surfactants to realize a nano-optical trap down to a single FND 120 nm in size. We envisage that our drop-casting platform can be extrapolated to perform targeted, low-power trapping, manipulation, and multimodal imaging of FNDs inside biological systems such as cells.Comment: 17 pages, 4 figures, 3 tables. Supplementary videos may be found at: https://drive.google.com/drive/folders/1gkW9g5Z7Fhl4i3ZQUOBQYuUYAPrHykzY?usp=sharin

Physiological impact of heat stress and their alleviation measures in agriculture: A review

Abiotic stresses are becoming more prevalent in modern agriculture as a result of shifting climate scenarios. Elevated temperature stress is one of the most important abiotic stresses to address since it has detrimental consequences for plant physiology, molecular structure, and phenology. The morphological impact occurs in the form of reduced germination, poor emergence, poor seedling vigor, abnormal seedling. Heat stress also results in the closure of stomata, reduced leaf size and consequent increase in stomatal density. One of the major physiological impacts of heat stress is on the fluidity of the membrane structure of the plant cell. Heat stress leads to increased fluidity of the thylakoid membrane and disruption of metabolic functions, which either deliver or accept electrons from PSII and, thus, cause dislodging of PSII from thylakoid membrane. The respiration generally increases in the temperature range of 0-35/ 40⁰C, reaches plateau at 40-50⁰C and decreases beyond 50⁰C due to damage to the respiratory mechanism. Elevated temperature directly impacts the cellular water content and indirectly through the increased water depletion rate from the soil. In order to design the appropriate corrective actions, it is crucial to research all the factors leading to heat stress thoroughly. The traditional agronomic and breeding interventions are crucial, but the rising food demand and the intensifying heat stress call for some cutting-edge biotechnological interventions, such as transgenics, genome editing, and CRISPR/cas9, to induce genome-level heat tolerance. The present review deals in detail with each of the previously listed aspects.

Directing Monolayer Tungsten Disulfide Photoluminescence using a Bent Plasmonic Nanowire on a Mirror Cavity

Designing directional optical antennas without compromising the field enhancement requires specially designed optical cavities. Herein, we report on the experimental observations of directional photoluminescence emission from a monolayer Tungsten Disulfide using a bent-plasmonic nanowire on a mirror cavity. The geometry provides field enhancement and directivity to photoluminescence by sandwiching the monolayer between an extended cavity formed by dropcasting bent silver nanowire and a gold mirror. We image the photoluminescence emission wavevectors by using the Fourier plane imaging technique. The cavity out-couples the emission in a narrow range of wavevectors with a radial and azimuthal spreading of only 11.0{\deg} and 25.1{\deg}, respectively. Furthermore, we performed three dimensional finite difference time domain based numerical calculations to corroborate and understand the experimental results. We envisage that the results presented here will be readily harnessed for on-chip coupling applications and in designing inelastic optical antennas

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to efficiently act in the world by pruning out implausible actions, and to perform look-ahead planning to determine how current actions might affect future world states. We design a new text-based gaming environment called TextWorld Commonsense (TWC) for training and evaluating RL agents with a specific kind of commonsense knowledge about objects, their attributes, and affordances. We also introduce several baseline RL agents which track the sequential context and dynamically retrieve the relevant commonsense knowledge from ConceptNet. We show that agents which incorporate commonsense knowledge in TWC perform better, while acting more efficiently. We conduct user-studies to estimate human performance on TWC and show that there is ample room for future improvement

Orthogonality and graph divergence losses promote disentanglement in generative models

Over the last decade, deep generative models have evolved to generate realistic and sharp images. The success of these models is often attributed to an extremely large number of trainable parameters and an abundance of training data, with limited or no understanding of the underlying data manifold. In this article, we explore the possibility of learning a deep generative model that is structured to better capture the underlying manifold's geometry, to effectively improve image generation while providing implicit controlled generation by design. Our approach structures the latent space into multiple disjoint representations capturing different attribute manifolds. The global representations are guided by a disentangling loss for effective attribute representation learning and a differential manifold divergence loss to learn an effective implicit generative model. Experimental results on a 3D shapes dataset demonstrate the model's ability to disentangle attributes without direct supervision and its controllable generative capabilities. These findings underscore the potential of structuring deep generative models to enhance image generation and attribute control without direct supervision with ground truth attributes signaling progress toward more sophisticated deep generative models