The MMT API: A Generic MKM System

The MMT language has been developed as a scalable representation and interchange language for formal mathematical knowledge. It permits natural representations of the syntax and semantics of virtually all declarative languages while making MMT-based MKM services easy to implement. It is foundationally unconstrained and can be instantiated with specific formal languages. The MMT API implements the MMT language along with multiple backends for persistent storage and frontends for machine and user access. Moreover, it implements a wide variety of MMT-based knowledge management services. The API and all services are generic and can be applied to any language represented in MMT. A plugin interface permits injecting syntactic and semantic idiosyncrasies of individual formal languages.Comment: Conferences on Intelligent Computer Mathematics (CICM) 2013 The final publication is available at http://link.springer.com

Scalable Compression of Deep Neural Networks

Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems. In this paper, we propose a scalable representation of the network parameters, so that different applications can select the most suitable bit rate of the network based on their own storage constraints. Moreover, when a device needs to upgrade to a high-rate network, the existing low-rate network can be reused, and only some incremental data are needed to be downloaded. We first hierarchically quantize the weights of a pre-trained deep neural network to enforce weight sharing. Next, we adaptively select the bits assigned to each layer given the total bit budget. After that, we retrain the network to fine-tune the quantized centroids. Experimental results show that our method can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201

Automatic generation of level maps with the do what's possible representation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.Final Accepted Versio

Archaeological Building Information Modeling: beyond scalable representation of architecture and archaeology

The widespread use of technologies and processes aimed at information management is one of the main trends in today’s building industry. Collaboration, coordination and validation of design results are fostered by software and workflows that involve many disciplines. Taking into account these premises, this paper deals with the application of such a paradigm to the archaeological and architectural fields. The application to the particular case study of the Etruscan town of Kainua aspires to be exemplary, since it is referred to different metric scales, from the building to the urban settlement. The digital reconstruction of the whole town, which can be explored and studied by means of Virtual Reality, was validated from a philological point of view using an original interdisciplinary approach called ArchaeoBIM, i.e. a methodology that encompasses the information flow among different disciplines with the same interest in understanding, and virtually reconstructing, lost realities. Using this method, architectural proportions brought by existing literature, physical behaviours of materials and components, layouts of rooms and spaces regulated by rituals or historic traditions are collected in a model that is able to represent morphologies, analysis and functions. This model, basically a geometric database linking heterogeneous documents, can be used in many different ways, from analytic abstractions to static simulations, from solar analysis to visual renderings. It becomes a common language for information exchange among scholars and users interested in the dissemination and study of the cultural heritage

Heat FlexOffers:a device-independent and scalable representation of electricity-heat flexibility

The increasing relevance of Renewable Energy Sources (RES) makes energy flexibility an extremely important aspect, not only regarding electricity, but also for other energy vectors such as heat. Because of this, there is the need for a flexibility model which can i) provide a common representation of flexibility for different device types, ii) perform aggregation, optimization and disaggregation while scaling for long time horizons and many devices, iii) capture most of the available flexibility, and iv) support energy conversion between different vectors. Properties i)-iii) are addressed by FlexOffer (FO), a device-independent model that describes energy constraints in an approximate yet accurate way. This paper proposes an extension of FOs, Heat FlexOffers (HFOs), capable of modeling flexibility for different energy vectors such as heat and handling energy conversion, and therefore addressing iv) as well as i)-iii). HFOs can model the optimal power curve for heat pumps, and can provide constraints for continuous optimization problems while complying to the Smart Grid-Ready (SG-Ready) interface, which operates on discrete states. We show that HFOs are very accurate, being able to retain up to of total flexibility before aggregation and of it after aggregation. HFOs aggregation is scalable, as 2 · 10^6 devices can be aggregated for a 24 hours time horizon, vastly outperforming exact models as they fail to aggregate more than 500 devices.</p

On the Equivalence of Cellular Automata and the Tile Assembly Model

In this paper, we explore relationships between two models of systems which are governed by only the local interactions of large collections of simple components: cellular automata (CA) and the abstract Tile Assembly Model (aTAM). While sharing several similarities, the models have fundamental differences, most notably the dynamic nature of CA (in which every cell location is allowed to change state an infinite number of times) versus the static nature of the aTAM (in which tiles are static components that can never change or be removed once they attach to a growing assembly). We work with 2-dimensional systems in both models, and for our results we first define what it means for CA systems to simulate aTAM systems, and then for aTAM systems to simulate CA systems. We use notions of simulate which are similar to those used in the study of intrinsic universality since they are in some sense strict, but also intuitively natural notions of simulation. We then demonstrate a particular nondeterministic CA which can be configured so that it can simulate any arbitrary aTAM system, and finally an aTAM tile set which can be configured so that it can be used to simulate any arbitrary nondeterministic CA system which begins with a finite initial configuration.Comment: In Proceedings MCU 2013, arXiv:1309.104

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for humans with each record measuring 20,000 genes. This highlights the need for unsupervised representation learning to fully ingest these data, yet classical transformer architectures are prohibitive to train on such data in terms of both computation and memory. To address this challenge, we propose a novel asymmetric encoder-decoder transformer for scRNA-seq data, called xTrimoGene$^\alpha$ (or xTrimoGene for short), which leverages the sparse characteristic of the data to scale up the pre-training. This scalable design of xTrimoGene reduces FLOPs by one to two orders of magnitude compared to classical transformers while maintaining high accuracy, enabling us to train the largest transformer models over the largest scRNA-seq dataset today. Our experiments also show that the performance of xTrimoGene improves as we scale up the model sizes, and it also leads to SOTA performance over various downstream tasks, such as cell type annotation, perturb-seq effect prediction, and drug combination prediction. xTrimoGene model is now available for use as a service via the following link: https://api.biomap.com/xTrimoGene/apply.Comment: Accepted by NeurIPS 202

Spatio-temporal analysis of flows in CDC 2013 data

We describe analysis of flows in the CDC2013 bicycles data set

Cross-temporal Detection of Novel Ransomware Campaigns: A Multi-Modal Alert Approach

We present a novel approach to identify ransomware campaigns derived from attack timelines representations within victim networks. Malicious activity profiles developed from multiple alert sources support the construction of alert graphs. This approach enables an effective and scalable representation of the attack timelines where individual nodes represent malicious activity detections with connections describing the potential attack paths. This work demonstrates adaptability to different attack patterns through implementing a novel method for parsing and classifying alert graphs while maintaining efficacy despite potentially low-dimension node features.Comment: Preprint. Under Revie