Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete Genome Sequence of the African Strain AXO 1947 of Xanthomonas oryzae pv . oryzae

Xanthomonas oryzae pv. oryzae is the etiological agent of bacterial rice blight. Three distinct clades of X. oryzae pv. oryzae are known. We present the complete annotated genome of the African clade strain AXO194 using long-read single-molecule PacBio sequencing technology. The genome comprises a single chromosome of 4,674,975 bp and encodes for nine transcriptional activator-like (TAL) effectors. The approach and data presented in this announcement provide information for complex bacterial genome organization and the discovery of new virulence effectors, and they facilitate target characterization of TAL effectors.
Asynchronous optimization for machine learning

The impressive breakthroughs of the last two decades in the field of machine learning can be in large part attributed to the explosion of computing power and available data. These two limiting factors have been replaced by a new bottleneck: algorithms. The focus of this thesis is thus on introducing novel methods that can take advantage of high data quantity and computing power. We present two independent contributions. First, we develop and analyze novel fast optimization algorithms which take advantage of the advances in parallel computing architecture and can handle vast amounts of data. We introduce a new framework of analysis for asynchronous parallel incremental algorithms, which enable correct and simple proofs. We then demonstrate its usefulness by performing the convergence analysis for several methods, including two novel algorithms. Asaga is a sparse asynchronous parallel variant of the variance-reduced algorithm Saga which enjoys fast linear convergence rates on smooth and strongly convex objectives. We prove that it can be linearly faster than its sequential counterpart, even without sparsity assumptions. ProxAsaga is an extension of Asaga to the more general setting where the regularizer can be non-smooth. We prove that it can also achieve a linear speedup. We provide extensive experiments comparing our new algorithms to the current state-of-art. Second, we introduce new methods for complex structured prediction tasks. We focus on recurrent neural networks (RNNs), whose traditional training algorithm for RNNs – based on maximum likelihood estimation (MLE) – suffers from several issues. The associated surrogate training loss notably ignores the information contained in structured losses and introduces discrepancies between train and test times that may hurt performance. To alleviate these problems, we propose SeaRNN, a novel training algorithm for RNNs inspired by the “learning to search” approach to structured prediction. SeaRNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error than the MLE objective. We demonstrate improved performance over MLE on three challenging tasks, and provide several subsampling strategies to enable SeaRNN to scale to large-scale tasks, such as machine translation. Finally, after contrasting the behavior of SeaRNN models to MLE models, we conduct an in-depth comparison of our new approach to the related work.
Measurement of Atomic L‐Subshell Coster‐Kronig Transition ( f 12) for Some Elements in the Atomic Range 52 ≤ Z ≤ 92

Abstract Coster‐Kronig transitions are a special class of radiationless transitions in which an atomic inner‐shell vacancy is filled by an electron from a higher subshell of the same principal shell (same quantum number, n). In order to investigate the physical quantities relevant to the L lines affected by the nonradiative transitions, experimental measurements were carried out using a Si(Li) x‐ray spectrometer. Atomic L shell Coster‐Kronig yields, f 12, for some elements in the atomic number 52 ≤ Z ≤ 92, were determined. The results in the present paper are found to be in good agreement with the theoretical values.
A traffic micro-simulation model to estimate four-lane highways' capacity in Thailand

This article purposed to present the maximum capacity and to develop the equation in the capacity estimation of 4 types of four-lane highways by using the micro-simulation model. Regarding the analysis, the factors affecting the capacity include access-point, heavy vehicles and median u-turn. According to the study, it was found that the maximum capacity of the four-lane highways in type 1 is 2194 passenger car/hour/lane. In the descending orders, the maximum capacity of the highways with four lanes in type 2, 3, and 4 are 2161, 2094 and 2017 passenger car/hour/lane, respectively. At the same time, the maximum capacity of the prevailing condition in the study is 1300-1600 vehicles/hour/lane, which is different from the HCM2010 method for 20-30%, due to the Thai’s highway characteristics directly affect the traffic and driving behavior. Median u-turn affects the four-lane highways in type 1 and 2 the most, while access-point factor has the most influence towards type 3 and 4. In addition, the author has developed the equation models for capacity estimation, which the result derived from the relationship between the capacities and affecting factors. It was aimed at using as the guidelines in the capacity assessment of Thai’s four-lane highways in the future.
Detecting slow wave sleep and rapid eye movement stage using cortical effective connectivity

In recent neuroimaging research, there has been considerable interest in identifying neuromarkers of sleep. Automatic slow wave sleep (SWS) and rapid eye movement (REM) are two known phases of sleep. However, the level by which those changes contribute to brain interactions has not been well characterized. In recent years, it has been shown that brain connectivity measuring can be helpful in investigation of behavioral states of the brain. By considering the fact that brains have different states in different stages of sleep, the present work employs effective connectivity and machine-learning analysis to quantify and classify SWS and REM stages of sleep. We examine low-density 12-channel EEG data from 8 healthy participants during a full night of sleep. Data were epoched into 30-s windows and SWS and REM stages were labeled by a sleep consultant. Effective connectivity was quantified using a directed metric, generalized partial directed coherence, and measures were used as input features for a machine-learning system. A support vector machine classifier was used to solve 2 binary problems of REM vs. nREM and SWS vs. nSWS. Findings revealed an excellent balanced accuracy of 89.80% in REM detection and 87.32% in SWS detection. Overall, our work demonstrates a successful application of effective connectivity analysis and machine learning for sleep neuromarkers in EEG.
Precise geolocation of persistent scatterers aided and validated by a lidar DSM

Persistent Scatterers (PS) interferometry results in the deformation history of time-coherent scatterers. Although several applications focus on smooth, spatially correlated signals, we aim for the detection, identification and analysis of single anomalies. These targets can be indicative of, e.g., strain in structures, potentially leading to the failure of such structures. For the identification and analysis it is of the greatest importance to know the exact position of the effective scattering center, to avoid an improper interpretation of the driving mechanism. Here we present an approach to optimize the geolocation of important scatterers, when necessary aided by an a priori Lidar-derived DSM (AHN-1 data) with 15cm and 5m resolution in vertical and horizontal directions, respectively. The DSM is also used to validate the geocoding. We implement our approach on a near-collapse event of a shopping mall in Heerlen, the Netherlands, to unravel its driving mechanism.
NETSYN: Neural Network-based Support for Synthesis

Our goal in applying machine learning for synthesis is to develop a tool that assists in acquiring the relationships between form, function, and behavior properties that can be used to more directly determine form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property appearing in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function. This paper presents the NETSYN approach for synthesis knowledge acquisition and an example of its ability to support engineering synthesis.
Region characteristics-based fusion of spatial and transform domain image denoising methods

Nonlocal means (NLM)- and wavelet-based image denoising methods have drawn much attention in image processing due to their effectiveness and simplicity. The performance of these algorithms varies according to region characteristics in an image. For example, NLM performs well for smooth regions due to deployment of redundancy available in images, whereas wavelet-based approaches may preserve key image features by controlling the degree of threshold for shrinking the noisy coefficients. This paper presents a simple novel approach that estimates an original image by simply taking the weighted average of the denoised images pixel values obtained by NLM and wavelet thresholding schemes based on natural characteristics of regions in an image. Extensive simulations on standard images demonstrate that the proposed approach outperforms the benchmark wavelet-based schemes, NLM and its variants, in terms of peak signal-to-noise ratio (PSNR(dB)), mean structural similarity metric (MSSIM), and visual quality.
Development of a Design Procedure for Class E Amplifiers

Here, the step-by-step design procedure for a Class E amplifier is presented. An existing Class E amplifier system is described using a systems architecture approach. The design decomposition for the case study is written so that Physical Solutions (PSs; equivalent to Design Parameters) are in terms of component parameters (such as frequency or capacitance). Coupling issues are found to arise given constraints on transistor use. The design decomposition is altered to reflect the case where an amplifier is required to power a specific load. A discussion of transistor failure enables a design procedure to be developed by observing path-dependent coupling. The design procedure is tested through the design of a real amplifier. The designed amplifier is built and its performance measured.
Generalized Methods for User-Centered Brain-Computer Interfacing

Brain-computer interfaces (BCIs) create a new form of communication and control for humans by translating brain activity directly into actions performed by a computer. This new field of research, best known for its breakthroughs in enabling fully paralyzed or locked-in patients to communicate and control simple devices, has resulted in a variety of remarkable technological developments. However, the field is still in its infancy, and facilitating control of a computer application via thought in a broader context involves a number of a challenges that have not yet been met. Advancing BCIs beyond the experimental phase continues to be a struggle. End-users have rarely been reached, except for in the case of a few highly specialized applications which require continual involvement of BCI experts. While these applications are profoundly beneficial for the patients they serve, the potential for BCIs is much broader in scope and powerful in effect. Unfortunately, the current approaches to brain-computer interfacing research have not been able to address the primary limitations in the field: the poor reliability of most BCIs and the highly variable performance across individuals. In addition to this, the modes of control available to users tend to be restrictive and unintuitive (e.g., imagining complex motor activities to answer “Yes” or “No” questions). This thesis presents a novel approach that addresses both of these limitations simultaneously. Brain-computer interfacing is currently viewed primarily as a machine learning problem, wherein the computer must learn the patterns of brain activity associated with a user’s mental commands. In order to simplify this problem, researchers often restrict mental commands to those which are well characterized and easily distinguishable based on a priori knowledge about their corresponding neural correlates. However, this approach does not fully recognize two properties of a BCI which makes it unique to other human-computer interfaces. First, individuals can vary widely with respect to the patterns of activation associated with how their brains generate similar mental activity and with respect to which kinds of mental activity have been most trained due to life experience. Thus, it is not surprising that BCIs based on predefined neural correlates perform inconsistently for different users. Second, for a BCI to perform well, the human and the computer must become a cohesive unit such that the computer can adapt as the user’s brain naturally changes over time and while the user learns to make their mental commands more consistent and distinguishable given feedback from the computer. This not only implies that BCI use is a skill that must be developed, honed, and maintained in relation to the computer’s algorithms, but that the human is the fundamental component of the system in a way that makes human learning just as important as machine learning. In this thesis it is proposed that, in the long term, a generalized BCI that can discover the appropriate neural correlates of individualized mental commands is
Using the Cognitive Approach to Coherence Relations for Discourse Annotation

The Cognitive approach to Coherence Relations (Sanders, Spooren, & Noordman, 1992) was originally proposed as a set of cognitively plausible primitives to order coherence relations, but is also increasingly used as a discourse annotation scheme. This paper provides an overview of new CCR distinctions that have been proposed over the years, summarizes the most important discussions about the operationalization of the primitives, and introduces a new distinction (disjunction) to the taxonomy to improve the descriptive adequacy of CCR. In addition, it reflects on the use of the CCR as an annotation scheme in practice. The overall aim of the paper is to provide an overview of state-of-the-art CCR for discourse annotation that can form, together with the original 1992 proposal, a comprehensive starting point for anyone interested in annotating discourse using CCR.
LabelBee: a web platform for large-scale semi-automated analysis of honeybee behavior from video

The LabelBee system is a web application designed to facilitate the collection, annotation and analysis of large amounts of honeybee behavior data from video monitoring. It is developed as part of NSF BIGDATA project "Large-scale multi-parameter analysis of honeybee behavior in their natural habitat", where we analyze continuous video of the entrance of bee colonies. Due to the large volume of data and its complexity, LabelBee provides advanced Artificial Intelligence and visualization capabilities to enable the construction of good quality datasets necessary for the discovery of complex behavior patterns. It integrates several levels of information: raw video, honeybee positions, decoded tags, individual trajectories and behavior events (entrance/exit, presence of pollen, fanning, etc.). This integration enables the combination of manual and automatic processing by the biologist end-users, who also share and correct their annotation through a centralized server. These annotations are used by the Computer Scientists to create new automatic models, and improve the quality of the automatic modules. The data constructed by this semi-automatized approach can then be exported for the analytic part, which is taking place on the same server using Jupyter notebooks for the extraction and exploration of behavior patterns.
Acoustic crypsis in communication by North Atlantic right whale mother–calf pairs on the calving grounds

Mammals with dependent young often rely on cryptic behaviour to avoid detection by potential predators. In the mysticetes, large baleen whales, young calves are known to be vulnerable to direct predation from both shark and orca predators; therefore, it is possible that mother–calf pairs may show cryptic behaviours to avoid the attention of predators. Baleen whales primarily communicate through low-frequency acoustic signals, which can travel over long ranges. In this study, we explore the potential for acoustic crypsis, a form of cryptic behaviour to avoid predator detection, in North Atlantic right whale mother–calf pairs. We predicted that mother–calf pairs would either show reduced calling rates, reduced call amplitude or a combination of these behavioural modifications when compared with other demographic groups in the same habitat. Our results show that right whale mother–calf pairs have a strong shift in repertoire usage, significantly reducing the number of higher amplitude, long-distance communication signals they produced when compared with juvenile and pregnant whales in the same habitat. These observations show that right whale mother–calf pairs rely upon acoustic crypsis, potentially to minimize the risk of acoustic eavesdropping by predators.
Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer

Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0–0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs). Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results. Results: All of the four models showed predictive ability in LNM. One of the combined models, ModelCR1, consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834–0.951] and test cohort (AUC, 0.883; 95% CI, 0.786–0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3–0.8 cm group, accuracy = 0.846; <0.3 cm group, accuracy = 0.849). Furthermore, compared with the routinely preoperative MR report, the sensitivity and accuracy of this model had a great improvement. Conclusions: A predictive model was proposed based on MR radiomic features and clinical parameters for LNM in EC. The model had a good discrimination ability, especially for normal-sized LNs.
Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study

Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data.
Optic flow cues help explain altitude control over sea in freely flying gulls

For studies of how birds control their altitude, seabirds are of particular interest because they forage offshore where the visual environment can be simply modelled by a flat world textured by waves then generating only ventral visual cues. This study suggests that optic flow, i.e. the rate at which the sea moves across the eye’s retina, can explain gulls’ altitude control over seas. In particular, a new flight model that includes both energy and optical invariants helps explain the gulls’ trajectories during offshore takeoff and cruising flight. A linear mixed model applied to 352 flights from 16 individual lesser black backed gulls (Larus fuscus) revealed a statistically significant optic flow set-point of ca 25° s−1. Thereafter, an optic flow-based flight model was applied to 18 offshore takeoff flights from nine individual gulls. By introducing an upper limit in climb rate on the elevation dynamics, coupled with an optic flow set-point, the predicted altitude gives an optimized fit factor value of 63% on average (30–83% in range) with respect to the GPS data. We conclude that the optic flow regulation principle helps gulls to adjust their altitude over sea without having to directly measure their current altitude.
Pseudomonas aeruginosa-Derived Volatile Sulfur Compounds Promote Distal Aspergillus fumigatus Growth and a Synergistic Pathogen-Pathogen Interaction That Increases Pathogenicity in Co-infection

Pathogen-pathogen interactions in polymicrobial infections are known to directly impact, often to worsen, disease outcomes. For example, co-infection with Pseudomonas aeruginosa and Aspergillus fumigatus, respectively the most common bacterial and fungal pathogens isolated from cystic fibrosis (CF) airways, leads to a worsened prognosis. Recent studies of in vitro microbial cross-talk demonstrated that P. aeruginosa-derived volatile sulfur compounds (VSCs) can promote A. fumigatus growth in vitro. However, the mechanistic basis of such cross-talk and its physiological relevance during co-infection remains unknown. In this study we combine genetic approaches and GC-MS-mediated volatile analysis to show that A. fumigatus assimilates VSCs via cysteine (CysB)- or homocysteine (CysD)-synthase. This process is essential for utilization of VSCs as sulfur sources, since P. aeruginosa-derived VSCs trigger growth of A. fumigatus wild-type, but not of a ΔcysBΔcysD mutant, on sulfur-limiting media. P. aeruginosa produces VSCs when infecting Galleria mellonella and co-infection with A. fumigatus in this model results in a synergistic increase in mortality and of fungal and bacterial burdens. Interestingly, the increment in mortality is much greater with the A. fumigatus wild-type than with the ΔcysBΔcysD mutant. Therefore, A. fumigatus’ ability to assimilate P. aeruginosa derived VSCs significantly triggers a synergistic association that increases the pathobiology of infection. Finally, we show that P. aeruginosa can promote fungal growth when growing on substrates that resemble the lung environment, which suggests that this volatile based synergism is likely to occur during co-infection of the human respiratory airways.