On Shimura's decomposition

Let $k$ be an odd integer $\ge 3$ and $N$ a positive integer such that $4 \mid N$. Let $\chi$ be an even Dirichlet character modulo $N$. Shimura decomposes the space of half-integral weight cusp forms $S_{k/2}(N,\chi)$ as a direct sum of $S_0(N,\chi)$ (the subspace spanned by 1-variable theta- series) and $S_{k/2}(N,\chi,\phi)$ where $\phi$ runs through a certain family of integral weight newforms. The explicit computation of this decomposition is important for practical applications of a theorem of Waldspurger relating critical values of $L$-functions of quadratic twists of newforms of even weight to coefficients of modular forms of half-integral weight.Comment: 12 pages, to appear in the International Journal of Number Theor

Collapse/Flattening of Nucleonic Bags in Ultra-Strong Magnetic Field

It is shown explicitly using MIT bag model that in presence of ultra-strong magnetic fields, a nucleon either flattens or collapses in the direction transverse to the external magnetic field in the classical or quantum mechanical picture respectively. Which gives rise to some kind of mechanical instability. Alternatively, it is argued that the bag model of confinement may not be applicable in this strange situation.Comment: 8 pages, REVTEX, 3 figures .eps files (included

FISHRENT; Bio-economic simulation and optimisation model

Key findings: The FISHRENT model is a major step forward in bio-economic model-ling, combining features that have not been fully integrated in earlier models: 1- Incorporation of any number of species (or stock) and/or fleets 2- Integration of simulation and optimisation over a period of 25 years 3- Integration of effort and TAC-driven management policies 4- Three independent relations for stock growth, production and investments. The feedbacks within the model allow for a dynamic simulation. The main application of the model is scenario analysis of policy options. Complementary findings: The model formulates a complete set of mathematical relations, but it also con-tains a number of important assumptions, which remain to be tested empirically. Therefore the model presents a challenging agenda for empirical research, which should lead to further qualitative and quantitative improvements of the in-dividual mathematical equations and parameter values. Method: This model was developed during the EU-funded project 'Remuneration of spawning stock biomass'. Its aim was to generate consistent sets of scenarios for an assessment of potential resource rents in different EU fisheries. The model comprises six modules, each focussing on a different aspect of the functioning of the fisheries system: biology (stocks), economy (costs, earnings and profits), policy (TACs, effort and access fees), behaviour (investments), prices (fish and fuel) and an interface linking the modules together. Input, calculation and output are clearly separated. The model produces a standard set of graphics, which provide a quick insight into the results of any model run. All output of the model runs can be exported to database software for further analysis. The model has been built in Excel, which makes it accessible for most us-ers. It has been used in new applications and even translated to other software. The model is continually further developed

Building a Benchmark for Industrial IoT Application

In this project, we have developed a rather robust means of processing and displaying large sums of IoT data using several cutting-edge, industry-standard technologies. Our data pipeline integrates physical sensors that send various environmental data like temperature, humidity, and pressure. Once created, the data is then collected at an MQTT broker, streamed through a Kafka cluster, processed within a Spark Cluster, and stored in a Cassandra database. In order to test the rigidity of the pipeline, we also created virtual sensors. This allowed us to send an immense amount of data, which wasn’t necessarily feasible with just the physical sensors. The web interface allows users to create as many of these virtual sensors as testing requires. Once the data goes through the pipeline, it is made viewable on the same web interface. Users can search for key sensors, look through important data, and analyze as necessary. Our IoT pipeline enables seamless data flow and near real-time analytics. Using industry-standard technologies allows for scalability and reliability, making it suitable for all sorts of data-intensive applications

A Coupled Compressive Sensing Scheme for Unsourced Multiple Access

This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several sub-blocks, and subsequently adds redundancy using a systematic linear block code. Compressive sensing techniques are then employed to recover sub-blocks, and the original messages are obtained by connecting pieces together using a low-complexity tree-based algorithm. Numerical results suggest that the proposed scheme outperforms other existing practical coding schemes. Measured performance lies approximately $4.3$~dB away from the Polyanskiy achievability limit, which is obtained in the absence of complexity constraints