Studying JD’s Value Creation-Based Business Model

People’s spending patterns are changing due to the economy’s quick expansion, and one such change is the advent of e-commerce, which has drawn a lot of investors. This article uses JD as an example to study the fundamental elements of its business model and assess and evaluate it from the standpoint of value creation

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The digital world is just another reality, alongside all the other parallel universes. It is similar to dreams, reflect- ing our fear and desire. When we are not conscious, the particles from our mind will travel freely and construct dreams. While in virtual space, digits are those wandering particles which form the world and are partially controlled by our minds. What is interesting is that no one in those realities will question the logic and behaviours, even though some of them are ridiculous, if you think carefully when you are awake in this physical world. We do find things go wrong sometimes, and we call them glitches. Glitches are particles that are left behind. They exist in the in-between space; they are the chaos, the awkwardness, and moments of uncertainty. In terms of dreams, the glitches happen when I experience sleep paralysis; I am half awake, and I can’t move my body. In the digital world, the glitches are system errors, disconnections and beyond recognition. In my life, the glitches are failures to communicate, in which I lose reactivity and do not know who I am. My perspective - one that oscillates between different worlds - allows me to better approach the contemporary collision zone between digital and physical space. I exa- mine the ways that we behave are constantly shaped by the technological tools we create, changing how we see ourselves, communicate with people and interact with our surroundings. As an artist in conversation with technology, I investigate my anxiety through social communications and my fear of disappearance and senselessness. The journey from ‘unease’ to ‘acceptance’ requires me to embrace the ‘glitch’ inside, know that uncertainty is liberty. My artistic practice imagines possibilities to rethink technology, offering positions beyond complicity or opposition. Through live performance, wearables and installations, I reveal what happens in cyberspace to the physical world, illuminating our current use of technology by creating absurd scenarios. Supported by ideas from quantum physics and inspired by cyberspace, this book will be a mosaic of multiple realities including dreams and digital platforms, ‘glitches’ in those different spaces and the works I’ve been doing. The reading experience will be a trip to multiple universes with floating particles or surfing various popup windows with hidden errors

Stable Principal Component Pursuit

In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently, it has been shown that a convex program, named Principal Component Pursuit (PCP), can recover the low-rank matrix when the data matrix is corrupted by gross sparse errors. We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors. More precisely, our result shows that the proposed convex program recovers the low-rank matrix even though a positive fraction of its entries are arbitrarily corrupted, with an error bound proportional to the noise level. We present simulation results to support our result and demonstrate that the new convex program accurately recovers the principal components (the low-rank matrix) under quite broad conditions. To our knowledge, this is the first result that shows the classical Principal Component Analysis (PCA), optimal for small i.i.d. noise, can be made robust to gross sparse errors; or the first that shows the newly proposed PCP can be made stable to small entry-wise perturbations.Comment: 5-page paper submitted to ISIT 201

Sentiment Analysis on Inflation after Covid-19

We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public's sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index. We use manually labeled trigrams to test the prediction performance of several machine learning models(logistic regression,random forest etc.) and choose Bert model for final demonstration. Later, we sum daily tweets' sentiment scores gained from Bert model to obtain the predicted inflation sentiment index, and we further analyze the regional and pre/post covid patterns of these inflation indexes. Lastly, we take other empirical inflation-related data as references and prove that twitter-based inflation sentiment analysis method has an outstanding capability to predict inflation. The results suggest that Twitter combined with deep learning methods can be a novel and timely method to utilize existing abundant data sources on inflation expectations and provide daily indicators of consumers' perception on inflation.Comment: 18 pages, 12 figure

Bandwidth Tunable Optical Filter Based on the Quad-Mode Resonator

An innovative bandwidth tunable optical filter with controllable bandwidth, center frequency, and transmission zero is proposed in this paper. The proposed filter utilizes a quad-mode resonator to achieve a wideband filter centered at 2.42 GHz. By incorporating varactor diodes into the open branches of the resonator, the proposed filter's center frequency and bandwidth can be dynamically adjusted via the voltage applied to the diodes. This tunable filter exhibits low insertion loss of less than 2 dB, return loss exceeding 10 dB, and a relative bandwidth of up to 40%