Airline Booking Limit Competition Game Under Differentiated Fare Structure

We address a two-firm booking limit competition game in the airline industry. We assume aggregate common demand, and differentiated ticket fare and capacity, to make this study more realistic. A game theoretic approach is used to analyze the competition game. The optimal booking limits and the best response functions are derived. We show the existence of a pure Nash equilibrium and provide the closed-form equilibrium solution. The location of the Nash equilibrium depends on the relative magnitude of the ratios of the full and discount fares. We also show that the sum of the booking limits of the two firms remains the same regardless of the initial allocation proportion of the demand

Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders

Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correlated image-text pairs, causing data inefficiency. To address the issue, knowledge distillation have been explored at the expense of extra image and text momentum encoders to generate teaching signals for misaligned image-text pairs. In this paper, our goal is to resolve the misalignment problem with an efficient distillation framework. To this end, we propose ECLIPSE: Expediting Contrastive Language-Image Pretraining with Self-distilled Encoders. ECLIPSE features a distinctive distillation architecture wherein a shared text encoder is utilized between an online image encoder and a momentum image encoder. This strategic design choice enables the distillation to operate within a unified projected space of text embedding, resulting in better performance. Based on the unified text embedding space, ECLIPSE compensates for the additional computational cost of the momentum image encoder by expediting the online image encoder. Through our extensive experiments, we validate that there is a sweet spot between expedition and distillation where the partial view from the expedited online image encoder interacts complementarily with the momentum teacher. As a result, ECLIPSE outperforms its counterparts while achieving substantial acceleration in inference speed.Comment: AAAI 202

Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining

Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-efficiency, recent efforts have introduced additional supervision terms that involve random-augmented views of the image. However, since the image augmentation process is unaware of its text counterpart, this procedure could cause various degrees of image-text misalignments during training. Prior methods either disregarded this discrepancy or introduced external models to mitigate the impact of misalignments during training. In contrast, we propose a novel metric learning approach that capitalizes on these misalignments as an additional training source, which we term "Misalign, Contrast then Distill (MCD)". Unlike previous methods that treat augmented images and their text counterparts as simple positive pairs, MCD predicts the continuous scales of misalignment caused by the augmentation. Our extensive experimental results show that our proposed MCD achieves state-of-the-art transferability in multiple classification and retrieval downstream datasets.Comment: ICCV 202

UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time 4x~14x while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.Comment: ECCV 202

Tumor-microenvironment-on-chip to Mimic Tumor Heterogeneity

Ductal Carcinoma In Situ (DCIS) is a non-invasive cancer that forms around breast milk ducts that can potentially progress into invasive breast cancer if untreated. Lack of models to study its diverse pathophysiology and differential response to treatments poses a challenge to develop standard treatment modalities with improved therapeutic outcomes. The traditional in vitro models such as cell monolayer are convenient but insufficient to represent the physiological characteristics of DCIS tumor microenvironment and often fail to predict clinical outcomes. The animal models effectively simulate the in vivo environment but also lack the ability to control the environmental parameters to match specific conditions making it difficult to address the heterogeneities in disease state and patient-to-patient variations. It is critical to develop a new DCIS model system that offers physiologically relevant features with high degree of control. In order to address this need, a novel microfluidic in vitro model was developed. A lumen structure to represent the milk duct in breast was generated along the microfluidic channel using a fluid dynamic phenomenon called viscous finger patterning in which as the less viscous fluid passes through, it leaves a continuous trail that makes a hollow tubular structure in the collagen hydrogel. Consequently, MCF-7 breast cancer cell lines were cultured along the lumen surface with BR5 stromal fibroblast in collagen hydrogel. A relatively straight, smooth lumen was achieved at a higher concentration of collagen gel by viscous finger patterning with an optimal flow rate. The interaction between a non-invasive breast cancer cell line, MCF-7 and stromal fibroblast most likely remain unchanged, thus mimicking the DCIS. This new model system is a potential tool to study DCIS progression and treatment response by offering physiologically relevant features that can be tailored to match disease state and patient specific conditions

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.Comment: Published at IEEE Acces

Investigation of Advanced Cathode Contacting Solutions in SOFC

Contacting solutions for air electrode in Solid Oxide Cells stacks often implement a ceramic paste made of electronic conducting perovskite, comparable or same as the electro-active material. This contacting layer, is applied in a green state by wet-powder-spray or screen-printing, and in situ fired during stack commissioning. The low level of necking between ceramic particles causes increased ohmic losses. Moreover the shrinkage usually observed during long term operation in temperature of this layer, due to sintering effect, lead to cracks and contact losses which hinder the cell performance. Increasing cell’s footprint, performance and lifetime at the stack level requires appropriate contacting solution. In this paper we reports the investigation of a new advanced monolithic contacting solution, easy to handle, soft and flexible, highly porous and highly conductive. Two different compositions have been investigated, with respect of their compatibility with Crofer (SEM, XRD). In addition, solid oxide cells contacted with this solution as well as with a ceramic paste have also been electrochemically tested up to 1000 hours in order to compare and assess the impact of this contacting solution on cell’s performance. Results will be presented and discussed

Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging

Video face re-aging deals with altering the apparent age of a person to the target age in videos. This problem is challenging due to the lack of paired video datasets maintaining temporal consistency in identity and age. Most re-aging methods process each image individually without considering the temporal consistency of videos. While some existing works address the issue of temporal coherence through video facial attribute manipulation in latent space, they often fail to deliver satisfactory performance in age transformation. To tackle the issues, we propose (1) a novel synthetic video dataset that features subjects across a diverse range of age groups; (2) a baseline architecture designed to validate the effectiveness of our proposed dataset, and (3) the development of novel metrics tailored explicitly for evaluating the temporal consistency of video re-aging techniques. Our comprehensive experiments on public datasets, including VFHQ and CelebA-HQ, show that our method outperforms existing approaches in age transformation accuracy and temporal consistency. Notably, in user studies, our method was preferred for temporal consistency by 48.1\% of participants for the older direction and by 39.3\% for the younger direction.Comment: 28 pages, 11 figures, 11 tables, Project page: https://video-reaging.github.io