Pricing, competition and market segmentation in ride hailing

We analyse a non-cooperative strategic game among two ride-hailing platforms, each of which is modeled as a two-sided queueing system, where drivers (with a certain patience level) are assumed to arrive according to a Poisson process at a fixed rate, while the arrival process of passengers is split across the two providers based on QoS considerations. We also consider two monopolistic scenarios: (i) each platform has half the market share, and (ii) the platforms merge into a single entity, serving the entire passenger base using their combined driver resources. The key novelty of our formulation is that the total market share is fixed across the platforms. The game thus captures the competition among the platforms over market share, which is modeled using two different Quality of Service (QoS) metrics: (i) probability of driver availability, and (ii) probability that an arriving passenger takes a ride. The objective of the platforms is to maximize the profit generated from matching drivers and passengers. In each of the above settings, we analyse the equilibria associated with the game. Interestingly, under the second QoS metric, we show that for a certain range of parameters, no Nash equilibrium exists. Instead, we demonstrate a new solution concept called an equilibrium cycle. Our results highlight the interplay between competition, cooperation, passenger-side price sensitivity, and passenger/driver arrival rates.Comment: 13 page

Bag-of-Words vs. Sequence vs. Graph vs. Hierarchy for Single- and Multi-Label Text Classification

Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art. We show that a simple multi-layer perceptron (MLP) using a Bag of Words (BoW) outperforms the recent graph-based models TextGCN and HeteGCN in an inductive text classification setting and is comparable with HyperGAT in single-label classification. We also run our own experiments on multi-label classification, where the simple MLP outperforms the recent sequential-based gMLP and aMLP models. Moreover, we fine-tune a sequence-based BERT and a lightweight DistilBERT model, which both outperform all models on both single-label and multi-label settings in most datasets. These results question the importance of synthetic graphs used in modern text classifiers. In terms of parameters, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an $\mathcal{O}(N^2)$ graph, where $N$ is the vocabulary plus corpus size.Comment: arXiv admin note: substantial text overlap with arXiv:2109.0377

Evaluation of Antimicrobial, Anti-Inflammatory and Wound Healing Potentiality of Various Indian Small Herbs: A Meta Analysis

The immune system has the ability to provoke inflammation in response to a wide variety of different triggers. Toxic chemicals, infectious diseases, radiation, and cells that have been harmed are some examples of these stimuli. It removes the detrimental stimuli and at the same time initiates the healing process, which is a win-win situation. As a result, the protective reaction of inflammation is essential for ensuring that the body continues to function properly. The majority of the time, cellular and molecular activities and interactions work together to successfully minimise the risk of experiencing damage or infection during acute inflammatory reactions. This is because these activities and interactions are coordinated to function together. This review article was prepared utilising materials written in English, and it has been published in time intervals of 15 years beginning in 1995 and continuing all the way up until the current day. Both systematic reviews and randomised controlled trials (RCTs), which are considered to be the two most reliable types of research, were included in the collection of publications that were pertinent to the goal that we set for ourselves. The first two approaches are the only ones that should be prioritised above the others. Studies with an open label and studies with cohorts are not as essential as those with a case-control design, which are called preclinical trials

MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device

Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that use pixel-dense warping operations for temporal modeling, making them too computationally expensive for deployment on mobile devices. Recent work has demonstrated that running a neural decoder in real time on mobile is feasible, but shows this only for 720p RGB video. This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device. Our codec relies on two major contributions. First, we design an efficient codec that uses a block-based motion compensation algorithm available on the warping core of the mobile accelerator, and we show how to quantize this model to integer precision. Second, we implement a fast decoder pipeline that concurrently runs neural network components on the neural signal processor, parallel entropy coding on the mobile GPU, and warping on the warping core. Our codec outperforms the previous on-device codec by a large margin with up to 48% BD-rate savings, while reducing the MAC count on the receiver side by $10 \times$. We perform a careful ablation to demonstrate the effect of the introduced motion compensation scheme, and ablate the effect of model quantization.Comment: Matches version published at WACV 202