S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions

Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle when applied to specialized domains like remote sensing, and adapting to such domains is challenging due to the limited number of image-text pairs available for training. To address this, we propose S-CLIP, a semi-supervised learning method for training CLIP that utilizes additional unpaired images. S-CLIP employs two pseudo-labeling strategies specifically designed for contrastive learning and the language modality. The caption-level pseudo-label is given by a combination of captions of paired images, obtained by solving an optimal transport problem between unpaired and paired images. The keyword-level pseudo-label is given by a keyword in the caption of the nearest paired image, trained through partial label learning that assumes a candidate set of labels for supervision instead of the exact one. By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures, and comics. For instance, S-CLIP improves CLIP by 10% for zero-shot classification and 4% for image-text retrieval on the remote sensing benchmark, matching the performance of supervised CLIP while using three times fewer image-text pairs.Comment: NeurIPS 202

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments

Electroacupuncture Attenuates Ovalbumin-Induced Allergic Asthma via Modulating CD4+CD25+ Regulatory T Cells

A mouse pulmonary hypersensitivity experimental model that mimics human asthma was developed, and electroacupuncture (EA) treatment was shown to reduce allergic inflammatory processes. In addition, we also assessed whether the beneficial effects of EA on allergic asthma could be correlated with CD4+CD25+Foxp3+ regulatory T cells (Treg). Cellular profiles and histopathologic analysis demonstrated that peribronchial and perivascular inflammatory cell infiltrates were significantly decreased in the EA-treated groups when compared to the OVA and anti-CD25 Ab-injected (Treg depletion) groups. Furthermore, total BAL cells were reduced in the EA groups when compared to other groups. Interestingly, the population of CD4+CD25+Foxp3+Tregs in pneumonocytes increased in EA-treated group when compared to OVA and Treg depletion groups. These results imply that EA stimulation at ST 36 may affect CD4+CD25+Foxp3+ Treg in an OVA-induced experimental model and may enhance Treg function by suppressing other T cells and limiting the immune response

Essay on Digital Platforms and AI

The first chapter is a joint work with Jiwoong Shin, Soheil Ghili, and Jaehwan Kim. In this chapter, we demonstrate the impact of the gig economy on product quality in seemingly unrelated local industries through the labor market. Our empirical context is the quality of service for restaurants in the city of Austin and we examine how they were impacted by the {exogenous exit and re-entry of rideshare platforms, Uber and Lyft into the city due to regulatory changes. We leverage these exogenous shocks and combine them with sentiment-analyzed data from Yelp reviews that capture how customers assess the quality of service at each restaurant. We show that, compared to control cities, customers in Austin become more negative about service quality when Uber and Lyft are present in the city. Additionally, we use rich data on employee turnover and wages to demonstrate that, compared to the control cities, service staff turnover indeed increases in Austin when Uber and Lyft are present. We also conduct several additional studies and robustness checks that are all congruent with our hypothesis that Uber and Lyft lower the quality of service in Austin restaurants by raising the turnover of their staff. Together, these results suggest significant ramifications of the gig economy on the broader industries through the labor market. The second essay, a joint work with K.Sudhir and Kosuke Uetake, studies whether and how a private communication channel can affect decision-making of players in an online freelancing platform. Online platforms that facilitate exchanges and trade through matching have proliferated in recent years. Though these platforms allow for information provision by participants, information asymmetry remains a significant impediment in facilitating matches between participants. This paper investigates whether the platform can enhance efficiency by providing private communication channels to exchange tailored information between participants to reduce information asymmetry. As such communications tend to be ``cheap talk\u27\u27 in that messages are neither verifiable nor differentially costly. Hence, whether such a communication channel can improve efficiency is an empirical question. We take advantage of a natural experiment on an online labor market platform, which introduced a new channel of communication between service buyers and freelancers to answer the question. We find that the communication channel does improve efficiency in long-term projects. Specifically, both short-term and long-term projects have higher matching probabilities, but the benefits of the communication channel seem to be greater for long-term projects in terms of more communications, higher contract probabilities. Interestingly, the ``cheap talk\u27\u27 channel also reduces the need for ``costly signaling\u27\u27 by service buyers to post a higher project price. Because most online labor platforms charge percentage fees from a contract price, our finding gives additional managerial insight that the platform should contemplate the trade-off between increased quantity and reduced price. In the third chapter, a joint work with Jin Kim and Minkyung Kim, we study human learning from Artificial Intelligence. Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe {reasoning processes of AI, rather than mere {actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to {reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated

Flexible HIV-1 Biosensor Based on the Au/MoS2 Nanoparticles/Au Nanolayer on the PET Substrate

An electrochemical flexible biosensor composed of gold (Au), molybdenum disulfide nanoparticles (MoS2 NPs), and Au (Au/MoS2/Au nanolayer) on the polyethylene terephthalate (PET) substrate is developed to detect envelope glycoprotein GP120 (gp120), the surface protein of HIV-1. To fabricate the nanolayer on the PET substrate, Au is sputter coated on the flexible PET substrate and MoS2 NPs are spin coated on Au, which is sputter coated once again with Au. The gp120 antibody is then immobilized on this flexible electrode through cysteamine (Cys) modified on the surface of the Au/MoS2/Au nanolayer. Fabrication of the biosensor is verified by atomic force microscopy, scanning electron microscopy, and cyclic voltammetry. A flexibility test is done using a micro-fatigue tester. Detection of the gp120 is measured by square wave voltammetry. The results indicate that the prepared biosensor detects 0.1 pg/mL of gp120, which is comparable with previously reported gp120 biosensors prepared even without flexibility. Therefore, the proposed biosensor supports the development of a nanomaterial-based flexible sensing platform for highly sensitive biosensors with flexibility for wearable device application

Human Learning from Artificial Intelligence: Evidence from Human Go Players’ Decisions after AlphaGo

Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe reasoning processes of AI, rather than mere actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated

Constructing a Knowledge-Based Database for Dermatological Integrative Medical Information

Recently, overuse of steroids and immunosuppressive drugs has produced incurable dermatological health problems. Traditional medical approaches have been studied for alternative solutions. However, accessing relevant information is difficult given the differences in information for western medicine (WM) and traditional medicine (TM). Therefore, an integrated medical information infrastructure must be utilized to bridge western and traditional treatments. In this study, WM and TM information was collected based on literature searches and information from internet databases on dermatological issues. Additionally, definitions for unified terminology and disease categorization based on individual cases were generated. Also a searchable database system was established that may be a possible model system for integrating both WM and TM medical information on dermatological conditions. Such a system will yield benefits for researchers and facilitate the best possible medical solutions for patients. The DIMI is freely available online