To surcharge or not to surcharge? A two-sided market perspective of the no-surchage rule

In Electronic Payment Networks (EPNs) the No-Surcharge Rule (NSR) requires that merchants charge the same final good price regardless of the means of payment chosen by the customer. In this paper, we analyze a three-party model (consumers, merchants, and proprietary EPNs) to assess the impact of a NSR on the electronic payments system, in particular, on competition among EPNs, network pricing to merchants and consumers, EPNs' profits, and social welfare. We show that imposing a NSR has a number of effects. First, it softens competition among EPNs and rebalances the fee structure in favor of cardholders and to the detriment of merchants. Second, we show that the NSR is a profitable strategy for EPNs if and only if the network e¤ect from merchants to cardholders is sufficiently weak. Third, the NSR is socially (un)desirable if the network externalities from merchants to cardholders are sufficiently weak (strong) and the merchants' market power in the goods market is sufficiently high (low). Our policy advice is that regulators should decide on whether the NSR is appropriate on a market-by-market basis instead of imposing a uniform regulation for all markets. JEL Classification: L13, L42, L80American Express, Discover, Electronic payment system, market power, MasterCard, network externalities, no-surcharge rule, regulation, two-sided markets, Visa

To surcharge or not to surcharge? A two-sided market perspective of the no-surchage rule

In Electronic Payment Networks (EPNs) the No-Surcharge Rule (NSR) requires that merchants charge the same final good price regardless of the means of payment chosen by the customer. In this paper, we analyze a three-party model (consumers, merchants, and proprietary EPNs) to assess the impact of a NSR on the electronic payments system, in particular, on competition among EPNs, network pricing to merchants and consumers, EPNs' profits, and social welfare. We show that imposing a NSR has a number of effects. First, it softens competition among EPNs and rebalances the fee structure in favor of cardholders and to the detriment of merchants. Second, we show that the NSR is a profitable strategy for EPNs if and only if the network e¤ect from merchants to cardholders is sufficiently weak. Third, the NSR is socially (un)desirable if the network externalities from merchants to cardholders are sufficiently weak (strong) and the merchants' market power in the goods market is sufficiently high (low). Our policy advice is that regulators should decide on whether the NSR is appropriate on a market-by-market basis instead of imposing a uniform regulation for all markets

Gravitational diffraction radiation

We show that if the visible universe is a membrane embedded in a higher-dimensional space, particles in uniform motion radiate gravitational waves because of spacetime lumpiness. This phenomenon is analogous to the electromagnetic diffraction radiation of a charge moving near to a metallic grating. In the gravitational case, the role of the metallic grating is played by the inhomogeneities of the extra-dimensional space, such as a hidden brane. We derive a general formula for gravitational diffraction radiation and apply it to a higher-dimensional scenario with flat compact extra dimensions. Gravitational diffraction radiation may carry away a significant portion of the particle's initial energy. This allows to set stringent limits on the scale of brane perturbations. Physical effects of gravitational diffraction radiation are briefly discussed.Comment: 5 pages, 2 figures, RevTeX4. v2: References added. Version to appear in Phys. Rev.

Open-Amp:Synthetic data framework for audio effect foundation models

This paper introduces Open-Amp, a synthetic data framework for generating large-scale and diverse audio effects data. Audio effects are relevant to many musical audio processing and Music Information Retrieval (MIR) tasks, such as modelling of analog audio effects, automatic mixing, tone matching and transcription. Existing audio effects datasets are limited in scope, usually including relatively few audio effects processors and a limited amount of input audio signals. Our proposed framework overcomes these issues, by crowdsourcing neural network emulations of guitar amplifiers and effects, created by users of open-source audio effects emulation software. This allows users of Open-Amp complete control over the input signals to be processed by the effects models, as well as providing high-quality emulations of hundreds of devices. Open-Amp can render audio online during training, allowing great flexibility in data augmentation. Our experiments show that using Open-Amp to train a guitar effects encoder achieves new state-of-the-art results on multiple guitar effects classification tasks. Furthermore, we train a one-to-many guitar effects model using Open-Amp, and use it to emulate unseen analog effects via manipulation of its learned latent space, indicating transferability to analog guitar effects data

Improving Unsupervised Clean-to-Rendered Guitar Tone Transformation Using GANs and Integrated Unaligned Clean Data

Recent years have seen increasing interest in applying deep learning methods to the modeling of guitar amplifiers or effect pedals. Existing methods are mainly based on the supervised approach, requiring temporally-aligned data pairs of unprocessed and rendered audio. However, this approach does not scale well, due to the complicated process involved in creating the data pairs. A very recent work done by Wright et al. has explored the potential of leveraging unpaired data for training, using a generative adversarial network (GAN)-based framework. This paper extends their work by using more advanced discriminators in the GAN, and using more unpaired data for training. Specifically, drawing inspiration from recent advancements in neural vocoders, we employ in our GAN-based model for guitar amplifier modeling two sets of discriminators, one based on multi-scale discriminator (MSD) and the other multi-period discriminator (MPD). Moreover, we experiment with adding unprocessed audio signals that do not have the corresponding rendered audio of a target tone to the training data, to see how much the GAN model benefits from the unpaired data. Our experiments show that the proposed two extensions contribute to the modeling of both low-gain and high-gain guitar amplifiers.Comment: Accepted to DAFx 202

PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Searchable symmetric encryption (SSE) has been used to protect the confidentiality of genomic data while providing substring search and range queries on a sequence of genomic data, but it has not been studied for protecting single nucleotide polymorphism (SNP)-phenotype data. In this article, we propose a novel model, PrivGenDB, for securely storing and efficiently conducting different queries on genomic data outsourced to an honest-but-curious cloud server. To instantiate PrivGenDB, we use SSE to ensure confidentiality while conducting different types of queries on encrypted genomic data, phenotype and other information of individuals to help analysts/clinicians in their analysis/care. To the best of our knowledge, PrivGenDB construction is the first SSE-based approach ensuring the confidentiality of shared SNP-phenotype data through encryption while making the computation/query process efficient and scalable for biomedical research and care. Furthermore, it supports a variety of query types on genomic data, including count queries, Boolean queries, and k'-out-of-k match queries. Finally, the PrivGenDB model handles the dataset containing both genotype and phenotype, and it also supports storing and managing other metadata like gender and ethnicity privately. Computer evaluations on a dataset with 5,000 records and 1,000 SNPs demonstrate that a count/Boolean query and a k'-out-of-k match query over 40 SNPs take approximately 4.3s and 86.4{\mu}s, respectively, that outperforms the existing schemes

Employment fluctuations in a dual labor market

In light of the huge cross-country differences in job losses during the recent crisis, we study how labor market duality - meaning the coexistence of "temporary" contracts with low firing costs and "permanent" contracts with high firing costs - affects labor market volatility. In a model of job creation and destruction based on Mortensen and Pissarides (1994), we show that a labor market with these two contract types is more volatile than an otherwise-identical economy with a single contract type. Calibrating our model to Spain, we find that unemployment fluctuates 21% more under duality than it would in a unified economy with the same average firing cost, and 33% more than it would in a unified economy with the same average unemployment rate. In our setup, employment grows gradually in booms, due to matching frictions, whereas the onset of a recession causes a burst of firing of "fragile" low-productivity jobs. Unlike permanent jobs, some newly-created temporary jobs are already near the firing margin, which makes temporary jobs more likely to be fragile and means they play a disproportionate role in employment fluctuation

A complex systems approach to e-governance adoption and implementation in Bayelsa State, Nigeria

Globally, public sector innovation has become a big issue as citizens demand for greater accountability, effectiveness and efficiency in service delivery, and the liberalisation of the governance system. Debate on e-government evolved in the last decade in parallel with a broader discussion on e-governance, where the concept and practice of e-governance further encompasses the e-government phenomenon. Because of the complexities of governance and e-governance, this chapter presents e-governance as a close, large integrated, open and sociotechnical (CLIOS) framework to meet present and emerging challenges of the e-world, as well as enhance good governance for sustainability.  Novel descriptions of e-governance, governance, CLIOS, complex adaptive systems, sociotechnical systems were provided from literature. From a socio-technical perspective, the design consideration for the adoption and implementation of e-governance architecture for a State in an emerging economy like Nigeria was provided. The contextual aspects that needed to be considered for the adoption of e-governance were discussed and citizens interface with governance through e-governance platforms were highlighted. Examples of countries implementing e-governance, benefits and challenges regarding the Bayelsa case were discussed

SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription

Guitar tablature is a form of music notation widely used among guitarists. It captures not only the musical content of a piece, but also its implementation and ornamentation on the instrument. Guitar Tablature Transcription (GTT) is an important task with broad applications in music education and entertainment. Existing datasets are limited in size and scope, causing state-of-the-art GTT models trained on such datasets to suffer from overfitting and to fail in generalization across datasets. To address this issue, we developed a methodology for synthesizing SynthTab, a large-scale guitar tablature transcription dataset using multiple commercial acoustic and electric guitar plugins. This dataset is built on tablatures from DadaGP, which offers a vast collection and the degree of specificity we wish to transcribe. The proposed synthesis pipeline produces audio which faithfully adheres to the original fingerings, styles, and techniques specified in the tablature with diverse timbre. Experiments show that pre-training state-of-the-art GTT model on SynthTab improves transcription accuracy in same-dataset tests. More importantly, it significantly mitigates overfitting problems of GTT models in cross-dataset evaluation.Comment: Submitted to ICASSP202