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Register a new userThe Donation-Payment Gift Card Concept: how to give twice with one card
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Standard economic theory, starting with Adam Smiths invisible hand, holds that those who trade for their own selfish motives of maximizing their private preferences may contribute more to the public wealth than those who claim altruistic motives. Under restrictive conditions, this has been shown to result from a self-organizing mechanism acting at the global system level, which ensures that optimal allocation of resources derives from competition within the rule of law. In a nutshell, according to this view, being competitively selfish adds value for all. But to what degree can doing good add competitive value to products and services? We propose the creation of the Donation-Payment card as a solution to the legal and moral issues plaguing the emerging anonymous, direct-to-consumer, stored-value prepaid (SVP) card market. Our proposal to redirect millions of dollars whose status is in conflict not only improves society and empowers people, but also represents the best free-market solution to the SVP-Cards industry. The Donation-Payment card kills two birds with one stone by marrying business interests with societal benefits. In addition, think of the state of mind of a grand-parent giving a SVP-card to a grand-child, who also feels like killing two birds with one stone or should we say more aptly feels like giving twice with one card.
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In our model, $n$ traders interact with each other and with a central bank; they are taxed on the money they make, some of which is dissipated away by corruption. A generic feature of our model is that the richest trader always wins by consuming all the others: another is the existence of a threshold wealth, below which all traders go bankrupt. The two-trader case is examined in detail,in the socialist and capitalist limits, which generalise easily to $n>2$. In its mean-field incarnation, our model exhibits a two-time-scale glassy dynamics, as well as an astonishing universality.When preference is given to local interactions in finite neighbourhoods,a novel feature emerges: instead of at most one overall winner in the system,finite numbers of winners emerge, each one the overlord of a particular region.The patterns formed by such winners (metastable states) are very much a consequence of initial conditions, so that the fate of the marketplace is ruled by its past history; hysteresis is thus also manifested.
The Compact Linear Collider, CLIC, is a multi-TeV electron-positron collider proposed for construction at CERN. A detector model, CLICdet, that is suited for the experimental conditions at CLIC and is based on realistic performance, has been developed. This paper describes the implementation of CLICdet in a fast simulation tool for particle physics collider experiments, DELPHES. The geometry of the detector concept as well as performance parameters extracted from full simulation studies are implemented in DELPHES parameter cards for CLICdet. Jet reconstruction for electron-positron colliders is added to the DELPHES analysis chain. Parameters for using DELPHES to simulate the detector effects of CLICdet are provided in three parameter cards, one for each energy stage of CLIC. The effects of beam-induced background at the higher-energy stages of CLIC are also incorporated. The results from the fast simulation with DELPHES are validated with respect to full detector simulation in a number of relevant processes.
In this paper, we target at recovering the exact routes taken by commuters inside a metro system that arenot captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategicallypropose two inference tasks to handle the recovering, one to infer the travel time of each travel link thatcontributes to the total duration of any trip inside a metro network and the other to infer the route preferencesbased on historical trip records and the travel time of each travel link inferred in the previous inferencetask. As these two inference tasks have interrelationship, most of existing works perform these two taskssimultaneously. However, our solutionTripDecoderadopts a totally different approach. To the best of ourknowledge,TripDecoderis the first model that points out and fully utilizes the fact that there are some tripsinside a metro system with only one practical route available. It strategically decouples these two inferencetasks by only taking those trip records with only one practical route as the input for the first inference taskof travel time and feeding the inferred travel time to the second inference task as an additional input whichnot only improves the accuracy but also effectively reduces the complexity of both inference tasks. Twocase studies have been performed based on the city-scale real trip records captured by the AFC systems inSingapore and Taipei to compare the accuracy and efficiency ofTripDecoderand its competitors. As expected,TripDecoderhas achieved the best accuracy in both datasets, and it also demonstrates its superior efficiencyand scalability.
Digital payment volume has proliferated in recent years with the rapid growth of small businesses and online shops. When processing these digital transactions, recognizing each merchants real identity (i.e., business type) is vital to ensure the integrity of payment processing systems. Conventionally, this problem is formulated as a time series classification problem solely using the merchant transaction history. However, with the large scale of the data, and changing behaviors of merchants and consumers over time, it is extremely challenging to achieve satisfying performance from off-the-shelf classification methods. In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i.e., affinity) to verify the self-reported business type (i.e., merchant category) of a given merchant. Specifically, we design two individual encoders, where one is responsible for encoding temporal information and the other is responsible for affinity information, and a mechanism to fuse the outputs of the two encoders to accomplish the identification task. Our experiments on real-world credit card transaction data between 71,668 merchants and 433,772,755 customers have demonstrated the effectiveness and efficiency of the proposed model.
Quantum technologies, i.e., technologies benefiting from the features of quantum physics such as objective randomness, superposition, and entanglement, have enabled an entirely different way of distributing and processing information. The enormous progress over the last decades has also led to an urgent need for young professionals and new educational programs. However, the lack of intuitive analogies and the necessity of complex mathematical frameworks often hinder teaching and learning efforts. Thus, novel education methods, such as those involving gamification, are promising supplements to traditional teaching methods. Here, we present a strategic card game in which the building blocks of a quantum computer can be experienced. While playing, participants start with the lowest quantum state, play cards to program a quantum computer, and aim to achieve the highest possible quantum state. By extending the game to high-dimensional quantum systems, i.e., systems that can take more than two possible values, and by developing different multi-player modes, the game can be used as an introduction to quantum computational tasks for students. As such, it can also be used in a classroom environment to increase the conceptual understanding, interest, and motivation of a student. Therefore, the presented game contributes to the ongoing efforts on gamifying quantum physics education with a particular focus on the counter-intuitive features which quantum computing is based on.
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