Peak-Hour Pricing Under Negative Externality: Impact of Customer Flexibility and Competitive Asymmetry

Several industries that provide services to customers (e.g., public utility and transportation) charge higher prices during peak hours to smooth demand. With technologies (e.g., electronic shelf labels) enabling retailers to change prices easily within each day, should supermarkets use peak-hour pricing? To examine this question formally, we introduce a stylized duopoly model in the presence of “negative externality,” where firms compete for congestion-averse customers. We characterize how customers endogenously segment themselves regarding when and where to shop, and then use the equilibrium outcomes to examine whether the firms should implement peak-hour pricing for varying types of customer flexibility and competitive asymmetry. Our analysis shows that, if customers are not flexible in their store choice, then both firms would always use peak-hour pricing. However, if store choice flexibility is present, then firms’ decisions depend on the competitive asymmetry as follows. If one firm has a clear competitive advantage (in terms of value or price) over the other firm, then the dominant firm will use peak-hour pricing, whereas the other firm will not. Otherwise, both firms will use peak-hour pricing if they engage in symmetric competition (in terms of similar value and price), or neither firm will use it if they engage in differentiated competition (high value versus low cost). Through our analysis of different extensions, we find that a firm’s ability to set its regular price would dampen the effect of peak-period pricing. Also, we obtain consistent results when there is heterogeneity in customer valuation and customer congestion aversion level

Electric-field-induced formation and annihilation of skyrmions in two-dimensional magnet

Electric manipulation of skyrmions in 2D magnetic materials has garnered significant attention due to the potential in energy-efficient spintronic devices. In this work, using first-principles calculations and Monte Carlo simulations, we report the electric-field-tunable magnetic skyrmions in MnIn2Te4 monolayer. By adjusting the magnetic parameters, including the Heisenberg exchange interaction, DMI, and MAE, through applying an electric field, the formation or annihilation of skyrmions can be achieved. Our work suggests a platform for experimental realization of the electric-field-tunable magnetic skyrmions in 2D magnets

Selling innovative products in the presence of externalities

When deciding whether to adopt an innovative product or service, consumers often experience different levels of anxiety (i.e., nervousness) that prompt them to resist purchase (e.g., fear of learning new technologies, disruption of established habits or beliefs). In such cases, consumers’ anxiety is mitigated by “validation” through externality (e.g., the number of early adopters). To reduce consumers’ anxiety, firms can also invest in “familiarization” through promotion (e.g., offering free trials). We conceptualize innovation as a product that engenders anxiety, and present a model that employs a consumer utility model focusing on the psychological dimension. We examine the firm's profit-maximizing promotion and pricing decisions when selling to forward-looking consumers in the presence of externality. Our equilibrium analysis reveals that, unlike the conventional wisdom for promoting new products, for anxiety-inducing innovations with externality, accelerating the speed of adoption through promotion can actually be detrimental to the firm.</p

Selling innovative products in the presence of externalities

When deciding whether to adopt an innovative product or service, consumers often experience different levels of anxiety (i.e., nervousness) that prompt them to resist purchase (e.g., fear of learning new technologies, disruption of established habits or beliefs). In such cases, consumers’ anxiety is mitigated by “validation” through externality (e.g., the number of early adopters). To reduce consumers’ anxiety, firms can also invest in “familiarization” through promotion (e.g., offering free trials). We conceptualize innovation as a product that engenders anxiety, and present a model that employs a consumer utility model focusing on the psychological dimension. We examine the firm's profit-maximizing promotion and pricing decisions when selling to forward-looking consumers in the presence of externality. Our equilibrium analysis reveals that, unlike the conventional wisdom for promoting new products, for anxiety-inducing innovations with externality, accelerating the speed of adoption through promotion can actually be detrimental to the firm.</p

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances

Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models

Replicating the innate human ability to detect all objects based on free-form texts at any granularity remains a formidable challenge for Vision-Language models. Current Large Vision Language Models (LVLMs) are predominantly constrained to grounding a single, pre-existing object, relying solely on data from Referring Expression Comprehension tasks. The limitation leads to a compromise in model design, necessitating the introduction of visual expert models or the integration of customized head structures. Beyond these constraints, our research delves into the untapped potential of LVLMs and uncover their inherent capability for basic object perception, allowing them to accurately identify and locate objects of interest. Building on this insight, we introduce a novel language-prompted localization dataset designed to fully unleash the capabilities of LVLMs in integrating fine-grained object perception with precise location awareness. More importantly, we present $\textbf{Griffon}$, a purely LVLM-based baseline, which does not require the introduction of any special tokens, expert models, or additional detection modules. It simply maintains a consistent structure with popular LVLMs by unifying data formats across various localization-related scenarios and is trained end-to-end through a well-designed pipeline. Comprehensive experiments demonstrate that $\textbf{Griffon}$ not only achieves state-of-the-art performance on the fine-grained RefCOCO series but also approaches the capabilities of the expert model Faster RCNN on the detection benchmark MSCOCO.Comment: Technical report. The codes and dataset will be released soon at https://github.com/jefferyZhan/Griffo

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)--that are randomly masked from the input--with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.Comment: Accepted to WWW'2