Miracle-dz/UniFAR-FTUs huggingface dataset:
VA‐TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations

In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.
Plumage color and pathogen-induced gene expression in a wild bird

Ornamentation is hypothesized to signal the capacity of an individual to cope with environmental challenges. At the molecular level, organisms respond to their environments largely by altering gene transcription, but the transcriptional responses linking ornamentation and disease resistance are virtually unstudied. In the house finch (Haemorhous mexicanus), carotenoid coloration displayed by males is important in female choice, and plumage redness predicts a male’s ability to recover from bacterial infection with Mycoplasma gallisepticum. To investigate the molecular mechanisms underlying links between ornamentation and disease response, we experimentally infected wild finches and used microarrays to identify genes that were differentially expressed in males with redder and yellower plumage coloration. Sixteen candidate genes were then investigated across individuals. Principal components analysis revealed that infection induced a marginally significant decrease in 11 genes, several with primary immune functions. The treatment by plumage color interaction effect was largely driven by greater disease-induced changes in expression of heat shock protein 90 and ubiquitin c (the second principal component) by red birds than by yellow birds. In a direct comparison of disease severity between infected birds, plumage color on its own did not significantly predict disease symptoms. However, in multivariate models, expression of genes loading on the second principal component, plumage color, and their interaction were significant predictors of the severity of disease symptoms. Our observations demonstrate links between ornamentation and expression of genes related to disease resistance; we suggest that male plumage color may reflect functionally associated differences in inducibility or plasticity of gene expression in this species.
Non-Transformed Prescribing Performance Function and Finite-Time RISE-Based Tracking Control for Euler-Lagrange Systems

This study addressed the issues of providing an enhanced prescribed performance control technique and a finite–time convergence of the conventional robust integral of the sign of the error (RISE) control method and for Euler-Lagrange systems. An improved RISE control strategy blended with a novel sine hyperbolic function as the prescribed function technique was designed based on the finite-time convergence theorem. The unknown uncertainty due to unknown dynamics of Euler-Lagrange dynamics was estimated by the adaptive law comprised by the command trajectory signals. The stability of the closed-loop finite-time RISE control system was proved via the constructive finite-time Lyapunov function method. The outperformed results of the proposed control strategy over the conventional controls were demonstrated by the experimental verification for an articulated manipulator.
Predation and Population Cycles of Small Mammals A reassessment of the predation hypothesis

The periodicity of mass occurrence of some northern small rodents has been known from at least the mid-sixteenth century, when Archbishop of Uppsala, Sweden, Olaus Magnus, published two reports on the phenomenon. He suggested that lemming abundances peaked at intervals of approximately three years (Stenseth and Ims 1993a). Elton (1942) initiated research on periodic fluctuations in small mammal populations, and since then such fluctuations have been the focus of much research and controversy in animal ecology. Cyclic fluctuations of population densities of many northern mammals are characterized by a regular period (the interval between successive density peaks) and a highly variable amplitude (the ratio of maximum to minimum population density). In the boreal zone of North America and some parts of Siberia, the cycle period is usually 9-10 (811) years, and the cycle amplitude
Illumination‐Guided Furniture Layout Optimization

Lighting plays a very important role in interior design. However, in the specific problem of furniture layout recommendation, illumination has been either neglected or addressed with empirical or very simplified solutions. The effectiveness of a particular layout in its expected task performance can be greatly affected by daylighting and artificial illumination in a non‐trivial manner. In this paper, we introduce a robust method for furniture layout optimization guided by illumination constraints. The method takes into account all dominant light sources, such as sun light, skylighting and fixtures, while also being able to handle movable light emitters. For this task, the method introduces multiple generic illumination constraints and physically‐based light transport estimators, operating alongside typical geometric design guidelines, in a unified manner. We demonstrate how to produce furniture arrangements that comply with important safety, comfort and efficiency illumination criteria, such as glare suppression, under complex light‐environment interactions, which are very hard to handle using empirical or simplified models.
Additions to the shark fauna of the Persian (Arabian) Gulf

Abstract The diversity of the shark fauna of the Persian (Arabian) Gulf is poorly documented. Based on surveys of fish markets in Kuwait and Qatar we provide the first Gulf records of the Sliteye Shark Loxodon macrorhinus, and the first substantiated records of Snaggletooth Shark Hemipristis elongatus, Graceful Shark Carcharhinus amblyrhynchoides and Hardnose Shark C. macloti. Based on photographs of misidentified specimens in historical literature we also add the Grey Reef Shark C. amblyrhynchos and Sharptooth Lemon Shark Negaprion acutidens to the Gulf fauna.
Differential Data-Aided Channel Estimation for Up-Link Massive SIMO-OFDM

Pilot symbol assisted modulation (PSAM) is widely used to obtain the channel state information (CSI) needed for coherent demodulation. It allows the density of pilot symbols to be dynamically chosen depending on the channel conditions. However, the insertion of pilots reduces the spectral efficiency, more severely when the channel is highly time-variant and/or frequency-selective. In these cases a significant amount of pilots is required to properly track the channel variations in both time and frequency dimensions. Alternatively, non-coherent demodulation does not require any CSI for the demodulation independently of the channel conditions. For the particular case of up-link (UL) based on massive single input - multiple output (SIMO) combined with orthogonal frequency division multiplexing (OFDM), we propose to replace the traditional reference signals of PSAM by a new differentially-encoded data stream that can be non-coherently detected. The latter can be demodulated without the knowledge of the CSI and subsequently used for the channel estimation. We denote our proposal as hybrid demodulation scheme (HDS) because it exploits both the benefits of a coherent demodulation scheme (CDS) and a non-coherent demodulation scheme (NCDS) to increase the spectral efficiency. The mean squared error (MSE) of the channel estimation, bit error rate (BER), achieved throughput and complexity are analyzed to highlight the benefits of this differential data-aided channel estimation as compared to other approaches. We show that the channel estimation is almost as good as PSAM, while the BER performance and throughput are improved for different channel conditions with a very small complexity increase.
Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates

The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.
Document-level Neural MT: A Systematic Comparison

In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions. As part of this comparison, we introduce and evaluate a document-level variant of the recently proposed Star Transformer architecture. In addition to using the traditional metric BLEU, we report the accuracy of the models in handling anaphoric pronoun translation as well as coherence and cohesion using contrastive test sets. Finally, we report the results of human evaluation in terms of Multidimensional Quality Metrics (MQM) and analyse the correlation of the results obtained by the automatic metrics with human judgments.
NICE: Neural Integrated Custom Engines

In this paper, we present a machine translation system implemented by the Translation Centre for the Bodies of the European Union (CdT). The main goal of this project is to create domain-specific machine translation engines in order to support machine translation services and applications to the Translation Centre’s clients. In this article, we explain the entire implementation process of NICE: Neural Integrated Custom Engines. We describe the problems identified and the solutions provided, and present the final results for different language pairs. Finally, we describe the work that will be done on this project in the future.
Dynamic Scheduling Optimization of Marine Oil Spill Emergency Resource

ABSTRACT Huang, X.L. ; Ren, Y.T. ; Zhang, J.A. ; Wang, D. , and Liu, J.P., 2020. Dynamic scheduling optimization of marine oil spill emergency resource. In: Qiu, Y. ; Zhu, H. , and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 437-442. Coconut Creek (Florida), ISSN 0749-0208. With the increasing development of marine resources development and marine transportation, oil spill has become one of the main threats to marine ecological environment. Considering that the oil film has the characteristics of drift, this paper studies the oil spill emergency material scheduling problem with multiple oil films. Firstly, the position of oil film drifting with time under the influence of sea condition is calculated. Secondly, aiming at the shortest scheduling time and the lowest cost, the dynamic scheduling optimization model of oil spill emergency materials is constructed. The maximum threshold of emergency dispatch time is introduced to transform the model, and MPSO algorithm is designed to solve the model. The results show that the emergency materials can be transported to the accident site within the prescribed time, thus preventing the continuous diffusion of pollution. At the same time, it can avoid unnecessary waste and reduce economic loss. And it can provide the theoretical support for the decision-maker to make the emergency material dispatch plan.
Studies on Brazilian Amauroderma s.str. reveal a new species from the Atlantic Forest, Amauroderma robledoi sp. nov. (Polyporales, Ganodermataceae)

Abstract. Amauroderma s.str. is a genus of polypores with neotropical distribution characterized mainly by species with nontruncated double-walled spores with solid columnar to semireticulate endosporic ornamentation. As a continuation of a study on the taxonomy of the genus in Brazil, specimens with dubious delimitation due to uncertain phylogenetic placement in previous works were carefully examined. A new species, Amauroderma robledoi, from the Brazilian Atlantic Forest is described based on morphological and phylogenetic evidence. It is characterized by the combination of a concentrically zonated reddish brown pilear surface, whitish stipe with grayish transversal bands, and subglobose to broadly ellipsoid ([10.5] 11–12 [12.5] × 9–11 µm), pale yellowish, double-walled basidiospores.
Practice of Marine Information Management Scheme Based on Cloud Computing Mode

ABSTRACT Zhao, X. -L. , 2020. Practice of marine information management scheme based on cloud computing mode. In: Qiu, Y. ; Zhu, H. , and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 238-240. Coconut Creek (Florida), ISSN 0749-0208. During the calculation of big data, in the principle of marine information management, it is very important to analyze the security of data storage and transmission, so as to make its information management more comprehensive and reasonable. In the cloud computing mode below, it is very important to further optimize the corresponding management scheme of ocean big data and to improve it on this basis can make its services more reliable, thereby providing more diversified ocean information management services. We must make full use of artificial intelligence technology to optimize computer protection strategies, detection strategies, rapid response strategies and security recovery strategies. Among them, the computer protection strategy led by artificial intelligence can avoid illegal access by restricting user rights and verification procedures, maintain the security of network marine data information and avoid information leakage and illegal theft.
An Overview of the SEBAMAT Project

SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.
MTUOC: easy and free integration of NMT systems in professional translation environments

In this paper the MTUOC project, aiming to provide an easy integration of neural and statistical machine translation systems, is presented. Almost all the required software to train and use neural and statistical MT systems are released under free licences. However, their use is not always easy and intuitive and medium-high specialized skills are required. MTUOC project provides simplified scripts for preprocessing and training MT systems, and a server and client for easy use of the trained systems. The server is compatible with popular CAT tools for a seamless integration. The project also distributes some free engines.
Ellipsis Translation for a Medical Speech to Speech Translation System

In diagnostic interviews, elliptical utterances allow doctors to question patients in a more efficient and economical way. However, literal translation of such incomplete utterances is rarely possible without affecting communication. Previous studies have focused on automatic ellipsis detection and resolution, but only few specifically address the problem of automatic translation of ellipsis. In this work, we evaluate four different approaches to translate ellipsis in medical dialogues in the context of the speech to speech translation system BabelDr. We also investigate the impact of training data, using an under-sampling method and data with elliptical utterances in context. Results show that the best model is able to translate 88% of elliptical utterances.
Unification of aggregate growth models by emergence from cellular and intracellular mechanisms

Multicellular aggregate growth is regulated by nutrient availability and removal of metabolites, but the specifics of growth dynamics are dependent on cell type and environment. Classical models of growth are based on differential equations. While in some cases these classical models match experimental observations, they can only predict growth of a limited number of cell types and so can only be selectively applied. Currently, no classical model provides a general mathematical representation of growth for any cell type and environment. This discrepancy limits their range of applications, which a general modelling framework can enhance. In this work, a hybrid cellular Potts model is used to explain the discrepancy between classical models as emergent behaviours from the same mathematical system. Intracellular processes are described using probability distributions of local chemical conditions for proliferation and death and simulated. By fitting simulation results to a generalization of the classical models, their emergence is demonstrated. Parameter variations elucidate how aggregate growth may behave like one classical growth model or another. Three classical growth model fits were tested, and emergence of the Gompertz equation was demonstrated. Effects of shape changes are demonstrated, which are significant for final aggregate size and growth rate, and occur stochastically.
Leveraging SIMD parallelism for accelerating network applications

Software packet processing frameworks act as critical components in modern network architecture, as their performance has a vital impact on the quality of the network services. Motivated by the increasing number and capability for advanced vector instructions in recent mainstream CPUs, this paper explores a new parallel processing design and implementation of data structures and algorithms that are frequently used for building network applications. In particular, we propose effective SIMD optimization techniques for the bloom filter and Open vSwitch megaflow cache. Our design reduces memory access latency via careful prefetching and a new design that meets the needs of fast data consuming instructions. Our evaluation shows performance improvements up to 162% in bloom filter and 48% in Open vSwitch compared to their scalar version.
Irina: Accelerating DNN Inference with Efficient Online Scheduling

DNN inference is becoming prevalent for many real-world applications. Current machine learning frameworks usually schedule inference tasks with the goal of optimizing throughput under predictable workloads and task arrival patterns. Yet, inference workloads are becoming more dynamic with bursty queries generated by various video analytics pipelines which run expensive inference only on a fraction of video frames. Thus it is imperative to optimize the completion time of these unpredictable queries and improve customer experience. We propose the preliminary design of the first online inference task scheduling system, called Irina, that takes completion time under unpredictable workload as its primary objective. Irina augments the design space of inference task scheduling with three new strategies, namely batching, stacking, and preemption, in order to more flexibly schedule the tasks and reduce overall latency. Simulation results with empirical inference execution data shows that Irina can improve average task completion time by 1.3x–2.5x over TensorFlow Serving scheduling.
Absence of cGAS-mediated type I IFN responses in HIV-1–infected T cells

Significance Whether HIV-1 infection triggers cGAS-mediated immune responses in CD4+ T cells remains debated. It is important to investigate to which extent HIV-1–infected T cells contribute to IFN production and expression of antiviral interferon-stimulated genes. By analyzing cellular responses upon productive HIV-1 infection or transduction, we demonstrate that lentiviruses and gammaretroviruses can infect and spread in primary CD4+ T cells and T cell lines without alarming the cGAS-mediated DNA sensing machinery, probably due to their replication strategy that minimizes the abundance of cGAS-sensitive DNA PAMPs. The DNA sensor cGAS catalyzes the production of the cyclic dinucleotide cGAMP, resulting in type I interferon responses. We addressed the functionality of cGAS-mediated DNA sensing in human and murine T cells. Activated primary CD4+ T cells expressed cGAS and responded to plasmid DNA by upregulation of ISGs and release of bioactive interferon. In mouse T cells, cGAS KO ablated sensing of plasmid DNA, and TREX1 KO enabled cells to sense short immunostimulatory DNA. Expression of IFIT1 and MX2 was downregulated and upregulated in cGAS KO and TREX1 KO T cell lines, respectively, compared to parental cells. Despite their intact cGAS sensing pathway, human CD4+ T cells failed to mount a reverse transcriptase (RT) inhibitor-sensitive immune response following HIV-1 infection. In contrast, infection of human T cells with HSV-1 that is functionally deficient for the cGAS antagonist pUL41 (HSV-1ΔUL41N) resulted in a cGAS-dependent type I interferon response. In accordance with our results in primary CD4+ T cells, plasmid challenge or HSV-1ΔUL41N inoculation of T cell lines provoked an entirely cGAS-dependent type I interferon response, including IRF3 phosphorylation and expression of ISGs. In contrast, no RT-dependent interferon response was detected following transduction of T cell lines with VSV-G-pseudotyped lentiviral or gammaretroviral particles. Together, T cells are capable to raise a cGAS-dependent cell-intrinsic response to both plasmid DNA challenge or inoculation with HSV-1ΔUL41N. However, HIV-1 infection does not appear to trigger cGAS-mediated sensing of viral DNA in T cells, possibly by revealing viral DNA of insufficient quantity, length, and/or accessibility to cGAS.
Precise treatment of cystic fibrosis – current treatments and perspectives for using CRISPR

ABSTRACT Cystic fibrosis (CF) affects about 70,000 individuals worldwide, whose lives are shortened mainly due to chronic pulmonary infections resulting from impaired clearance of abnormally viscous airway mucus. The development of novel drugs targeting specific CFTR gene mutations in a precision medicine framework improved treatment, so that for patients born in 2000–2003 in the UK, the median life expectancy was estimated at around 40 years. Moreover the discovery of the CRISPR (Classes of Regularly Interspaced Palindromic Repeats) and Cas9 (Crispr-ASsociated) nuclease system opened the perspective of specifically correcting the defective CFTR gene as recently demonstrated in a model of intestinal stem cell organoids from CF patients. In the present review, we shall outline the existing state-of-art treatments and the perspectives for the precision treatment of CF opened by CRISPR.