ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 28’000 known and 100’000 novel reactions predicted with generalized enzymatic reactions rules and over 70’000 compounds, of which 22’000 are known to biochemical databases and 48’000 only to PubChem. Over 1,000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational toolbox and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.

Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

Control Oriented Maneuver Model of Prismatic Planing Hull

Roll yaw coupled dynamics are not well characterized and existing low cost models are limited in range of applicability. We utilize an interpolation based approach to address a wider range of conditions with reduced computational requirements. By interpolating test data to estimate hydrodynamic forces and empirically modeling roll damping and added mass, we establish a 4DOF maneuvering model for prismatic planing hulls in calm water. It is validated against relevant tests and show significant computational resource savings in comparison with potential flow based methods. Simulation of an extreme turning maneuver and an asymmetrical loading case demonstrates its potential for use in initial design, control and evaluation.

Collaborating with Language Community Members to Enrich Ethnographic Descriptions in a Language Archive

Language archives connect users such as language communities, linguists, and other researchers, to language data. As the language archiving community develops, concerns have been raised about the ethics, ownership, accessibility, and context of archival materials. While there are no simple solutions to these questions, many language archives are seeking ways to involve language community members in these conversations as they continue. This presentation describes a pilot project undertaken at the Computational Resource for South Asian Languages (CoRSAL) which explores a collaborative archiving approach to enable language community members to tell their own stories by adding contextual information to archival materials.

Challenges to Representing Personal Names and Language Names in Language Archives: Examples from Northeast India

Language archives are not only a valuable resource for language communities to tell their stories and to create lasting records of their ways of life, but also for those interested in anthropology, linguistics, agriculture, or art history. This recent emphasis on archiving primary datasets in linguistics has resulted in an abundance of datasets online; however, of the languages of South Asia, only a small percentage are represented in digital language archives or described thoroughly. Though several of these languages are being documented, this material is at risk of being lost or inaccessible without concerted attention paid to long-term preservation. There are several obstacles to documenting and archiving language materials from this area, including political instability and lack of access to infrastructure. This submission reviews one particular challenge to data management relevant to South Asia, which is the complexity of names (of individuals, groups, and languages). We provide examples from Northeast India and recommendations based on experience from CoRSAL (Computational Resource for South Asia).

A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN

This paper investigates a computing offloading policy and the allocation of computational resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio access networks (F-RANs). Concerning the dynamically changing wireless environment where the channel state information (CSI) is difficult to predict and know exactly, we formulate the problem of task offloading and resource optimization as a mixed-integer nonlinear programming problem to maximize the total utility of all UEs. Concerning the non-convex property of the formulated problem, we decouple the original problem into two phases to solve. Firstly, a centralized deep reinforcement learning (DRL) algorithm called dueling deep Q-network (DDQN) is utilized to obtain the most suitable offloading mode for each UE. Particularly, to reduce the complexity of the proposed offloading scheme-based DDQN algorithm, a pre-processing procedure is adopted. Then, a distributed deep Q-network (DQN) algorithm based on the training result of the DDQN algorithm is further proposed to allocate the appropriate computational resource for each UE. Combining these two phases, the optimal offloading policy and resource allocation for each UE are finally achieved. Simulation results demonstrate the performance gains of the proposed scheme compared with other existing baseline schemes.

A Hopf physical reservoir computer

Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler–Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.

BUSCA DE PALAVRAS CHAVE EM IMAGENS DE LIVROS IMPRESSOS USANDO VISÃO COMPUTACIONAL

With the increasingly frequent use of books in digital format, people search for the desired subjects in a faster way compared to the search in physical books. This work aimed to develop a computational resource in the form of an application for Android smartphones, which, based on an image captured from a page in a book, performs searches by keywords. The purpose of using the application is to help the reader to find the desired information quickly. We use Computer Vision techniques with the aid of the OpenCV library in the development of algorithms to perform segmentation, correction of the perspective of the book page image, identification and rectification of the wavy lines, recognition and character classification. The results shown were promising with a hit rate of over 88%.