Design and Pricing of Probabilistic Quality

Increasingly, sellers are offering goods characterized by probabilistic quality. In such offers, buyers receive a synthetic product comprising of a lottery between two vertically differentiated goods. Given this emerging practice, I formally investigate the design and pricing of probabilistic quality. In this dissertation, I ask: How does probabilistic selling improve seller profits in vertical markets? When probabilistic quality is optimal, how is it designed; in particular, how are the associated probability, pricing, and product set determined? Further, what is the impact of transaction costs on the design of probabilistic quality? Next, what is the impact of probabilistic selling on consumer surplus? Finally, will probabilistic quality arise under demand uncertainty? My analysis reveals that probabilistic quality can enhance seller profits via three distinct routes: profitably disposing excess capacity, better targeting of the high-quality product, and greater market coverage. In addition, transaction costs can play a critical role on the emergence and manner of emergence of probabilistic quality. Next, I find that probabilistic quality can potentially enhance consumer surplus even though its implementation necessitates a dissipative transaction cost. Finally, I find that probabilistic quality is robust to considerations of demand uncertainty

Formulating Event-based Image Reconstruction as a Linear Inverse Problem using Optical Flow

Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously. Recovering brightness from events is appealing since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications and to generate slow-motion HDR videos. However, state-of-the-art methods tackle this problem by training an event-to-image recurrent neural network (RNN), which lacks explainability and is difficult to tune. In this work we show, for the first time, how tackling the joint problem of motion and brightness estimation leads us to formulate event-based image reconstruction as a linear inverse problem that can be solved without training an image reconstruction RNN. Instead, classical and learning-based image priors can be used to solve the problem and remove artifacts from the reconstructed images. The experiments show that the proposed approach generates images with visual quality on par with state-of-the-art methods despite only using data from a short time interval. The proposed linear formulation and solvers have a unifying character because they can be applied also to reconstruct brightness from the second derivative. Additionally, the linear formulation is attractive because it can be naturally combined with super-resolution, motion-segmentation and color demosaicing.Comment: 19 pages, 22 figures, 5 table

Research on Method of Dynamic Stability Analysis for Slopes of Earth and Rockfill Dam Basing on the P-Z Model

According to the problems in dynamic stability analysis for slopes of earth and rockfill dam, the P-Z constitutive model, which is a kind of the multi-mechanism plastic model based on generalized plasticity, is introduced in the paper. Strength reduction factors of P-Z model are derived and verified, and based on them a new kind of method of dam slopes dynamic stability is put forward. For the method, the dynamic stability of dam slopes is judged by dynamic displacement time history and post-earthquake permanent displacement. The results show that local instability of dam slopes and variation features of dynamic response are obtained by the method, which is more reasonable

BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data

Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing methods incorporate the visual modality as a pivot for audio-text pre-training, which inevitably induces data noise. In this paper, we propose BLAT: Bootstrapping Language-Audio pre-training based on Tag-guided synthetic data. We utilize audio captioning to generate text directly from audio, without the aid of the visual modality so that potential noise from modality mismatch is eliminated. Furthermore, we propose caption generation under the guidance of AudioSet tags, leading to more accurate captions. With the above two improvements, we curate high-quality, large-scale parallel audio-text data, based on which we perform audio-text pre-training. Evaluation on a series of downstream tasks indicates that BLAT achieves SOTA zero-shot classification performance on most datasets and significant performance improvement when fine-tuned on downstream tasks, suggesting the effectiveness of our synthetic data

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure

Self‐organization of ionic liquid‐modified organosilica hollow nanospheres and heteropolyacids: efficient preparation of 5‐HMF under mild conditions

As a biomass‐derived platform molecule, 5‐hydroxymethylfurfural (5‐HMF) is a highly desirable feedstock for manufacturing of high value‐added chemicals ranging from starting materials for polyesters to biofuels. In this work, we reported the fabrication of a series of multicomponent solid acid catalysts based on heteropolyacids immobilized ILs‐modified organosilica hollow nanospheres (denoted as PW12‐ILs‐Cn‐HNS), in which PW12 (PW12=H3PW12O40 ⋅ xH2O) provides Brønsted acid site, ILs show strong electrostatic interactions with PW12, Cn (Cn=alkyl chain) is attached for hydrophobicity and HNS represents organosilica hollow nanospheres. When applied for catalytic dehydration of fructose to 5‐HMF, the PW12‐ILs‐C4‐HNS catalyst with 15.2 % PW12 loading exhibited the best dehydration activity to 5‐HMF with 93.7 % yield in DMSO at 100 °C in 2 h. Compared with 2D hexagonal and 3D interconnected structures, the excellent porosity properties of hollow nanospherical structure can provide a high population of the PW12 sites and enough confined nanospace for the dehydration of fructose. Moreover, the PW12‐ILs‐Cn‐HNS catalyst showed excellent stability over six catalytic cycles without obvious loss of activity. Most importantly, careful identification of the observed intermediates revealed crucial information for the dehydration process of fructose to 5‐HMF. As such, the proposed heterogeneous catalysts show great potential in biomass conversion processes