Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic surveillance of Neisseria gonorrhoeae to investigate the distribution and evolution of antimicrobial-resistance determinants and lineages

The first extensively drug resistant (XDR) Neisseria gonorrhoeae strain with high resistance to the extended-spectrum cephalosporin ceftriaxone was identified in 2009 in Japan, but no other strain with this antimicrobial-resistance profile has been reported since. However, surveillance to date has been based on phenotypic methods and sequence typing, not genome sequencing. Therefore, little is known about the local population structure at the genomic level, and how resistance determinants and lineages are distributed and evolve. We analysed the whole-genome sequence data and the antimicrobial-susceptibility testing results of 204 strains sampled in a region where the first XDR ceftriaxone-resistant N. gonorrhoeae was isolated, complemented with 67 additional genomes from other time frames and locations within Japan. Strains resistant to ceftriaxone were not found, but we discovered a sequence type (ST)7363 sub-lineage susceptible to ceftriaxone and cefixime in which the mosaic penA allele responsible for reduced susceptibility had reverted to a susceptible allele by recombination. Approximately 85 % of isolates showed resistance to fluoroquinolones (ciprofloxacin) explained by linked amino acid substitutions at positions 91 and 95 of GyrA with 99 % sensitivity and 100 % specificity. Approximately 10 % showed resistance to macrolides (azithromycin), for which genetic determinants are less clear. Furthermore, we revealed different evolutionary paths of the two major lineages: single acquisition of penA X in the ST7363-associated lineage, followed by multiple independent acquisitions of the penA X and XXXIV in the ST1901-associated lineage. Our study provides a detailed picture of the distribution of resistance determinants and disentangles the evolution of the two major lineages spreading worldwide.
Unsupervised Morphological Segmentation for Detecting Parkinson's Disease

The growth of life expectancy entails a rise in prevalence of aging-related neurodegenerative disorders, such as Parkinson’s disease. In the ongoing quest to ﬁnd sensitive behavioral markers of this condition, computerized tools prove particularly promising. Here, we propose a novel method utilizing unsupervised morphological segmentation for accessing morphological properties of a speaker’s language. According to our experiments on German, our method can classify patients vs. healthy controls with 81 percent accuracy, and estimate the neurological state of PD patients with Pearson correlation of 0.46 with respect to the uniﬁed Parkinson’s disease rating scale. Our work is the ﬁrst study to show that unsupervised morphological segmentation can be used for automatic detection of a neurological disorder.
Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph

Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.
Sixth European Seminar in Virology on Virus–Host Interaction at Single Cell and Organism Level

The 6th European Seminar in Virology (EuSeV) was held in Bertinoro, Italy, 22–24 June 2018, and brought together international scientists and young researchers working in the field of Virology. Sessions of the meeting included: virus–host-interactions at organism and cell level; virus evolution and dynamics; regulation; immunity/immune response; and disease and therapy. This report summarizes lectures by the invited speakers and highlights advances in the field.
Automatic Construction of Radio Maps by Crowdsourcing PDR Traces for Indoor Positioning

In this work, we propose an automatic radio map construction system based on crowdsourcing Pedestrian Dead Reckoning (PDR) traces, which does not rely on priori knowledge of floor plans and is robust to inaccurate PDR traces. In this system, we propose to process some opportunistic PDR traces, in which users walk through the building, to generate parts of road paths by translating, rotating and scaling the traces based on the opportunistic GPS locations and gate points as landmarks. Then, we further extend the coverage of road paths by processing the PDR traces entirely obtained indoor by compensating the turning errors and merging the PDR traces based on the similarity of WiFi fingerprints. With such procedures, we can accurately generate indoor road paths of a large-scale building and construct the radio map based on these road paths. Our proposed method achieves a median accuracy of 2.8m and mean accuracy of 2.9m for the constructed road paths. By fusing GPS, PDR, and WiFi fingerprinting with the crowdsourcing radio map, we achieve a median positioning accuracy of 2.9m and mean accuracy of 3.4m without site surveying, which significantly outperforms the positioning algorithm by merely fusing GPS and PDR.
Learning Sharing Behaviors with Arbitrary Numbers of Agents

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents. The individual behavior models are weighted finite state transducers (WFSTs) with weights dynamically updated during interactions, and the multi-agent fusion model is a logistic regression classifier. We test our models in a multi-agent tower-building environment, where a Q-learning agent learns to interact with rule-based agents. Our approach accurately models the underlying behavior patterns of the rule-based agents with accuracy ranging between 0.63 and 1.0 depending on the stochasticity of the other agent behaviors. In addition we show using KL-divergence that the model accurately captures the distribution of next actions when interacting with both a single agent (KL-divergence
Investigation on the Neural Mechanism of Hypnosis-Based Respiratory Control Using Functional MRI

Respiratory control is essential for treatment effect of radiotherapy due to the high dose, especially for thoracic-abdomen tumor, such as lung and liver tumors. As a noninvasive and comfortable way of respiratory control, hypnosis has been proven effective as a psychological technology in clinical therapy. In this study, the neural control mechanism of hypnosis for respiration was investigated by using functional magnetic resonance imaging (fMRI). Altered spontaneous brain activity as well as neural correlation of respiratory motion was detected for eight healthy subjects in normal state (NS) and hypnosis state (HS) guided by a hypnotist. Reduced respiratory amplitude was observed in HS (mean ± SD: 14.23 ± 3.40 mm in NS, 12.79 ± 2.49 mm in HS, p=0.0350), with mean amplitude deduction of 9.2%. Interstate difference of neural activity showed activations in the visual cortex and cerebellum, while deactivations in the prefrontal cortex and precuneus/posterior cingulate cortex (PCu/PCC) in HS. Within these regions, negative correlations of neural activity and respiratory motion were observed in visual cortex in HS. Moreover, in HS, voxel-wise neural correlations of respiratory amplitude demonstrated positive correlations in cerebellum anterior lobe and insula, while negative correlations were shown in the prefrontal cortex and sensorimotor area. These findings reveal the involvement of cognitive, executive control, and sensorimotor processing in the control mechanisms of hypnosis for respiration, and shed new light on hypnosis performance in interaction of psychology, physiology, and cognitive neuroscience.
Faster Convergence & Generalization in DNNs

Deep neural networks have gained tremendous popularity in last few years. They have been applied for the task of classification in almost every domain. Despite the success, deep networks can be incredibly slow to train for even moderate sized models on sufficiently large datasets. Additionally, these networks require large amounts of data to be able to generalize. The importance of speeding up convergence, and generalization in deep networks can not be overstated. In this work, we develop an optimization algorithm based on generalized-optimal updates derived from minibatches that lead to faster convergence. Towards the end, we demonstrate on two benchmark datasets that the proposed method achieves two orders of magnitude speed up over traditional back-propagation, and is more robust to noise/over-fitting.
A Multilevel Zero-Inflated Model for the Study of Copper Hillocks Growth in Integrated Circuits Manufacturing

As integrated circuits (ICs) are rapidly migrating to smaller feature sizes, copper hillocks growing vertically from the copper back-end interconnects may cause inter layer metallic shorts and reliability issues. Uncovering the impact of design factors on the formation of copper hillocks is of importance for reducing the number of shorts and improving the ICs design. The purpose of this paper is twofold: 1) to experimentally investigate the mechanisms for hillock formation in a manufacturing environment and to develop a statistical model incorporating these features for more accurate predictions and 2) to determine optimal design settings to minimize hillock related short-circuits. In this paper, we describe our approach to collecting the data and our statistical model for the analysis of copper hillocks growth on ICs. To capture the low failure observations and multilevel variations, we extend the zero-inflated model, which assumes the system randomly shifts between a zero state and a count state, to a multilevel model for our problem. We first analyze the mechanism causing the zero state to determine at which level the state shift occurs. Then, the distribution of the count state is derived based on the hillocks number and the hillock height. Finally, flexible random effects distributions are incorporated to model the non-normal variations. Our analysis of the process and development of the model enable the capture of the unique characteristics of the process, and provide a platform to understand the factors governing the growth of copper hillocks in a manufacturing environment that would not have been possible if standard existing models have been applied. This has led to some previously unknown insights on the physical hillocks growth process.
A Tangled Web: Origins of Reproductive Parasitism

Abstract While typically a flea parasite and opportunistic human pathogen, the presence of Rickettsia felis (strain LSU-Lb) in the non-blood-feeding, parthenogenetically reproducing booklouse, Liposcelis bostrychophila, provides a system to ascertain factors governing not only host transitions but also obligate reproductive parasitism (RP). Analysis of plasmid pLbAR, unique to R. felis str. LSU-Lb, revealed a toxin–antitoxin module with similar features to prophage-encoded toxin–antitoxin modules utilized by parasitic Wolbachia strains to induce another form of RP, cytoplasmic incompatibility, in their arthropod hosts. Curiously, multiple deubiquitinase and nuclease domains of the large (3,841 aa) pLbAR toxin, as well the entire antitoxin, facilitated the detection of an assortment of related proteins from diverse intracellular bacteria, including other reproductive parasites. Our description of these remarkable components of the intracellular mobilome, including their presence in certain arthropod genomes, lends insight on the evolution of RP, while invigorating research on parasite-mediated biocontrol of arthropod-borne viral and bacterial pathogens.
Static interpretation of higher-order modules in Futhark: functional GPU programming in the large

We present a higher-order module system for the purely functional data-parallel array language Futhark. The module language has the property that it is completely eliminated at compile time, yet it serves as a powerful tool for organizing libraries and complete programs. The presentation includes a static and a dynamic semantics for the language in terms of, respectively, a static type system and a provably terminating elaboration of terms into terms of an underlying target language. The development is formalised in Coq using a novel encoding of semantic objects based on products, sets, and finite maps. The module language features a unified treatment of module type abstraction and core language polymorphism and is rich enough for expressing practical forms of module composition.
SMT versus NMT: Preliminary comparisons for Irish

In this paper, we provide a preliminary comparison of statistical machine translation (SMT) and neural machine translation (NMT) for English→Irish in the fixed domain of public administration. We discuss the challenges for SMT and NMT of a less-resourced language such as Irish, and show that while an out-of-the-box NMT system may not fare quite as well as our tailor-made domain-specific SMT system, the future may still be promising for EN→GA NMT
VPsec: countering fault attacks in general purpose microprocessors with value prediction

Despite their complexity, general purpose microprocessors are susceptible to fault attacks. The state-of-the-art fault attacks rely on a precise understanding of the microprocessor datapath and the instructions critical path, to identify the exact time and location for injecting data faults that affect only targeted instructions in the pipeline. Software-only mitigations are only partially effective to defend against such attacks, whereas existing hardware-assisted mitigations require substantial changes to the microprocessor design. Both types of mitigation introduce significant overheads to the application memory footprint, the microprocessor area, or impact the overall system performance. We propose a novel hardware-only scheme: Value Prediction for security (VPSec). VPsec leverages value prediction in an embodiment and system design to mitigate fault attacks in general purpose microprocessors. Value prediction is an elegant and hitherto mature microarchitectural performance optimization, which aims to predict the data value ahead of the data production with high prediction accuracy and coverage. VPsec leverages the presence of the state-of-the-art value prediction in a general purpose microprocessors, and re-architects it for security. It augments the original value prediction embodiment with fault detection logic and reaction logic to mitigate fault attacks to both the datapath and the value predictor itself. VPsec defines a new mode of execution in which the predicted value is trusted rather than the produced value. From a design perspective, VPsec requires minimal hardware changes (negligible area impact) with respect to a baseline that supports value prediction, it has no software overheads (no increase in memory footprint), and it retains most of the performance benefits of value prediction. Our evaluation of VPsec demonstrates its efficacy in countering fault attacks as well as its ability to retain the performance benefits of value prediction on cryptographic and non-cryptographic workloads.
Point Process Models for Distribution of Cell Phone Antennas

We introduce a model for the spatial distribution of cell phone antennas in a urban environment. After showing that the complete spatial randomness (homogeneous Poisson distribution) hypothesis does not hold, we propose a model in which each point is distributed according to a bivariate Gaussian variable with mean given by the barycenter of its neighbors in the Delaunay triangulation. We show that this model is suitable, and can be used to generate a synthetic distribution of antennas. The generated distribution contains no sensitive or proprietary information, and can thus be freely shared with research groups, fostering further research on the subject.
Adaptive Transmission Range Based Topology Control Scheme for Fast and Reliable Data Collection

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near-sink nodes is reduced, the energy consumption of the near-sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.
Sequential Bayesian inference for static parameters in dynamic state space models

A method for sequential Bayesian inference of the static parameters of a dynamic state space model is proposed. The method is based on the observation that many dynamic state space models have a relatively small number of static parameters (or hyper-parameters), so that in principle the posterior can be computed and stored on a discrete grid of practical size which can be tracked dynamically. Further to this, this approach is able to use any existing methodology which computes the filtering and prediction distributions of the state process. Kalman filter and its extensions to non-linear/non-Gaussian situations have been used in this paper. This is illustrated using several applications: linear Gaussian model, Binomial model, stochastic volatility model and the extremely non-linear univariate non-stationary growth model. Performance has been compared to both existing on-line method and off-line methods.
Effects of task experience and layout on learning from text and pictures with or without unnecessary picture descriptions

The presentation of extraneous (i.e., irrelevant or unnecessary) information may hamper learning with multimedia. The present study examined whether people can learn to ignore unnecessary information with increasing experience with the task and whether this depends on the layout of that information. In two experiments, participants learned about the process of mitosis from a multimedia slideshow, with each slide presenting a combination of expository text and a picture on one of the stages in the process. Slides either contained no unnecessary text (control condition) or unnecessary text (i.e., merely describing the picture) either integrated in the picture (integrated condition) or presented underneath the picture (separated condition). Knowledge about the studied mitosis phase was tested immediately after each slide using a cloze test. Across Experiments 1 and 2, we did not find a reliable negative effect of the unnecessary text on cloze test performance. As a result, the question of whether task experience would reduce or eliminate that negative effect could not be answered. The eye movement data did confirm, however, that participants attended less to the unnecessary information with increasing task experience, suggesting that students can adapt their study strategy and learn to ignore unnecessary information.
Automatic lung segmentation based on Graph Cut using a distance-constrained energy

Lung segmentation serves to ensure that all the parts of the lungs are considered during pulmonary image analysis by isolating the lung from the surrounding anatomy in the image. Research has shown that computed tomography (CT) images greatly improves the accuracy of the diagnosis obtained by a physician for lung cancer detection. Therefore, inspired by the success of Graph Cut in image segmentation and given that manual methods of analysing CT images are tedious and time-consuming, an automatic segmentation method based on Graph Cut is proposed which makes use of a distance-constrained energy (DCE). Graph Cut produces globally optimal solutions by modelling the image data and spatial relationship among the pixels. However, several anatomical regions in the thoracic CT image have pixel intensity values similar to the lungs, leading to results where the lung tissue and all these regions are included in the segmentation result. The global energy function is, therefore, further constrained by using the distance of pixels from a coarsely segmented region of the CT image containing the lungs. The proposed method, utilising the DCE function, shows significant improvement over using the unconstrained energy function in segmenting the lungs from the CT images using Graph Cut.
Putting our heads together: interpersonal neural synchronization as a biological mechanism for shared intentionality

Abstract Shared intentionality, or collaborative interactions in which individuals have a shared goal and must coordinate their efforts, is a core component of human interaction. However, the biological bases of shared intentionality and, specifically, the processes by which the brain adjusts to the sharing of common goals, remain largely unknown. Using functional near infrared spectroscopy (fNIRS), coordination of cerebral hemodynamic activation was found in subject pairs when completing a puzzle together in contrast to a condition in which subjects completed identical but individual puzzles (same intention without shared intentionality). Interpersonal neural coordination was also greater when completing a puzzle together compared to two control conditions including the observation of another pair completing the same puzzle task or watching a movie with a partner (shared experience). Further, permutation testing revealed that the time course of neural activation of one subject predicted that of their partner, but not that of others completing the identical puzzle in different partner sets. Results indicate unique brain‐to‐brain coupling specific to shared intentionality beyond what has been previously found by investigating the fundamentals of social exchange.