Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete Genome of the Xanthomonas euvesicatoria Specific Bacteriophage KΦ1, Its Survival and Potential in Control of Pepper Bacterial Spot

Xanthomonas euvesicatoria phage KΦ1, a member of Myoviridae family, was isolated from the rhizosphere of pepper plants showing symptoms of bacterial spot. The phage strain expressed antibacterial activity to all X. euvesicatoria strains tested and did not lyse other Xanthomonas spp. , nor other less related bacterial species. The genome of KΦ1 is double-stranded DNA of 46.077 bp including 66 open reading frames and an average GC content of 62.9%, representing the first complete genome sequence published for a phage infecting xanthomonads associated with pepper or tomato. The highest genome similarity was observed between phage KΦ1 and the Xanthomonas oryzae pv. oryzae specific phage OP2. On the other hand, when compared with other members of the genus Bcep78virus, the genome similarity was lower. Forty-four (67%) predicted KΦ1 proteins shared homology with Xanthomonas phage OP2, while 20 genes (30%) were unique to KΦ1. Phage KΦ1, which is chloroform resistant and stable in different media and in the pH range 5-11, showed a high titer storage ability for at least 2 years at +4°C. Copper-hydroxide and copper-oxychloride reduced phage activity proportionally to the used concentrations and the exposure time. UV light was detrimental to the phage strain, but skim milk plus sucrose formulation extended its survival in vitro. The phages survived for at least 7 days on the surface of pepper leaves in the greenhouse, showing the ability to persist on the plant tissue without the presence of the host bacterium. Results of three repeated experiments showed that foliar applications of the unformulated KΦ1 phage suspension effectively controlled pepper bacterial spot compared to the standard treatment and the untreated control. The integration of the phage KΦ1 and copper-hydroxide treatments resulted in an increased efficacy compared to the copper-hydroxide alone.
Chinese Web Short Text Subject Clustering Based on Similarity Upper Approximation

In this paper, we propose a Web short text clustering method based on altered Similarity Upper Approximation algorithm. After the initial text modeling, we reduce the dimension of the text feature word matrix by singular value decomposition. After the clustering is completed, we extract the most frequent words in each text cluster to represent the subject of each cluster. The clustering process does not need to specify the number of clusters in advance, and it is suitable for Web short text clustering that is constantly updated and can not know the specific number of clusters in advance. In order to make the cluster number more accurate, we proposed to add the merger of clusters based on the average similarity of clusters and outlier detection in the original algorithm. Experiments show that the altered algorithm proposed in this paper is superior to the K-means algorithm and the hierarchical clustering algorithm in clustering accuracy and more accurate to original algorithm in cluster number.
Automatic Left Ventricle Segmentation in Cardiac Magnetic Resonance Images via Threshold Selection

In medical diagnosis, the movement of the myocardium of left ventricle (LV) can represent the pump function of the heart, which can provide the basis for diagnosis of heart diseases. Magnetic resonance imaging (MRI) is an effective tool for the clinical diagnosis of heart diseases due to its special imaging mechanism, which is particularly effective for soft tissue such as heart. Identification of the LV endocardium, especially the apical and basal slice images and some special mid-ventricular slice images with poor image quality, is still a very challenging and open problem. In this paper, an automatic segmentation method based on threshold is proposed. This method works well in some mid-ventricular slices with poor image quality and some ventricle slices with messy edge. We tested the proposed method with other 15 popular segmentation algorithms by ten frames of Cardiac MRI at end systole (ED) or end diastole (ES) phases. Those frames of images are difficult to segment the LV endocardium from Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 challenge. Those frames of images are from apical and basal slices or mid-ventricular slices that are difficult to segment the LV endocardium. Finally, we assessed the deviation between the automatically segmented and benchmark manual contours. The proposed method achieved 0.9384 average Dice metric, 1.2715 mm average perpendicular distance (APD). These results compared with other algorithms demonstrate that the proposed method is an effective and viable method to identify the LV endocardium at ED and ES phases.
Dual-robot Cooperative Sorting System with Multiple Sensors

With the rapid development of industrial robots, their applications become more and more complex. The requirements for robots are no longer just simple repetitive movements, but some intelligent coordination. A single robot is difficult to complete such a task. Hence a coordinated robotic system with dual robots should be developed. In this paper, we propose a dual-robot cooperative system with multiple sensors to perform high speed material sorting. A vision system is used to locate the objects on a conveyor and classify the objects. Based on the information from the vision system, the first robot will pick up the object and transfer it to an intermediate station. The second robot will then sort the object into different containers. The two robots are controlled to avoid collision with each other. An experimental system is set up and experiments were performed to sort different objects into different containers. The results demonstrate the effectiveness and efficiency of the proposed method. The proposed method has many applications in manufacturing automation.
Impact of DAT-SPECT on Management of Patients Suspected of Parkinsonism

Objective To evaluate the value of 123I-2&bgr;-carbomethoxy-3&bgr;-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (123I-FP-CIT) dopamine transporter single photon emission computed tomography (DAT-SPECT) to change management strategies of patients suspected of parkinsonism. Method This was an institutional review board–approved, retrospective study. DAT-SPECT scans ordered by movement disorder specialist and neurologists from 2011–2014 were reviewed. Clinical data and radiological reports of 173 patients suspected of parkinsonism were reviewed. The DAT-SPECT scan results were correlated with clinical assessment and treatment changes. Results A total of 173 patients (104 male and 69 female subjects; age, 64.4 ± 12.6 years) suspected of parkinsonism were included. Median duration of symptoms was 36 months (range, 1–480 months). Scans were most often requested when there was diagnostic uncertainty in clinical features (59.6%, 103/173) or to differentiate one other disease from parkinsonism such as Parkinson disease (PD) versus essential tremor (23.7%, 41/173), PD versus drug-induced parkinsonism (8.7%, 15/173), or PD versus psychogenic (6.4%, 11/173) or vascular (1.7%, 3/173) disorders. Patients were classified, according to the DAT-SPECT scanning results, as those with abnormal DAT-SPECT findings (59%, 102/173) and those with normal DAT-SPECT findings (41%, 71/173). In patients with normal DAT-SPECT findings, follow-up management data were available in 76.1% (54/71). The management changed in 39.4% (28/54) after DAT scan with starting a new appropriate medications or supportive therapy in 4.2% (3/28), withholding inappropriate dopaminergic treatment in 11.3% (8/28), or continuing observation in 23.9% (17/28). In patients with abnormal DAT-SPECT findings, follow-up management data were available in 78.4% (80/102). There was change in management of 37.3% (38/80), a new PD treatment was started in 89.5% (34/38). The dose of medication was adjusted in 5.3% (2/38), although the original treatment was not changed. Parkinson disease treatment was stopped in 2.6% (1/38) and discontinued in 2.6% (1/38) based on clinical decision of neurologists despite abnormal DAT-SPECT findings. Conclusions DAT-SPECT findings impacted treatment decisions in 44.7% of patients suspected of Parkinsonism.
Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts

This paper introduces a quantitative evaluation of filters that seek to separate an image into its large-scale variations, the base layer, and its fine-scale variations, the detail layer. Such methods have proliferated with the development of HDR imaging and the proposition of many new tone-mapping operators. We argue that an objective quality measurement for all methods can be based on their artifacts. To this aim, the four main recurrent artifacts are described and mathematically characterized. Among them two are classic, the luminance halo and the staircase effect, but we show the relevance of two more, the contrast halo and the compartmentalization effect. For each of these artifacts we design a test-pattern and its attached measurement formula. Then we fuse these measurements into a single quality mark, and obtain in that way a ranking method valid for all filters performing a base+detail decomposition. This synthetic ranking is applied to seven filters representative of the literature and shown to agree with expert artifact rejection criteria.
Risk stratification and treatment of ICU-acquired pneumonia caused by multidrug- resistant/extensively drug-resistant/pandrug-resistant bacteria

Purpose of review Describe the risk factors and discuss the management of multidrug-resistant (MDR) bacteria responsible for pneumonia among critically ill patients, including methicillin-resistant Staphylococcus aureus, extended spectrum beta-lactamase-producing Enterobactericeae, carbapenem-resistant Enterobactericeae, multidrug resistant Pseudomonas aeruginosa, and Acinetobacter baumannii. Recent findings Multiple factors have been associated with infections because of MDR bacteria, including prolonged hospital stay, presence of invasive devices, mechanical ventilation, colonization with resistant pathogens, and use of broad-spectrum antibiotics. Management of these infections includes the prompt use of appropriate antimicrobial therapy, implementation of antimicrobial stewardship protocols, and targeted active microbiology surveillance. Combination therapy and novel molecules have been used for the treatment of severe infections caused by resistant bacteria. Summary The exponential increase of antimicrobial resistance among virulent pathogens currently represents one of the main challenges for clinicians in the intensive care unit. Knowledge of the local epidemiology, patient risk stratification, and infection-control policies remain key elements for the management of MDR infections. Results from clinical trials on new molecules are largely awaited.
Rainbow Vertex Coloring Bipartite Graphs and Chordal Graphs

Given a graph with colors on its vertices, a path is called a rainbow vertex path if all its internal vertices have distinct colors. We say that the graph is rainbow vertex-connected if there is a rainbow vertex path between every pair of its vertices. We study the problem of deciding whether the vertices of a given graph can be colored with at most k colors so that the graph becomes rainbow vertex-connected. Although edge-colorings have been studied extensively under similar constraints, there are significantly fewer results on the vertex variant that we consider. In particular, its complexity on structured graph classes was explicitly posed as an open question. We show that the problem remains NP-complete even on bipartite apex graphs and on split graphs. The former can be seen as a first step in the direction of studying the complexity of rainbow coloring on sparse graphs, an open problem which has attracted attention but limited progress. We also give hardness of approximation results for both bipartite and split graphs. To complement the negative results, we show that bipartite permutation graphs, interval graphs, and block graphs can be rainbow vertex-connected optimally in polynomial time.
Stabilization of an underactuated AUV with hydrodynamic damping in three-dimensional space

This paper investigates stabilization of a relative equilibrium of an underactuated autonomous underwater vehicle(AUV) with three independent control inputs: moment of pitch, moment of yaw and thrust, in three-dimensional space while the hydrodynamic damping appears. The relative equilibrium we interest corresponds to a trim trajectory that can save power and that is naturally unstable. Controllers with the knowledge of the hydrodynamic damping were presented to handle this problem in considerable volume of literature. It's a drawback that controllers need the knowledge of the hydrodynamic damping terms, since the damping is notoriously difficult to model precisely even locally. Combining the interconnection and damping assignment(IDA) method and adaptive strategy together, dynamic control schemes without the information of the damping are provided to stabilize the relative equilibrium. Simulation results are included to verify the effectiveness of the proposed schemes.
The effects of parity and litter size on bone metabolic activity in pregnant and lactating sows

During gestation and lactation, female mammals often mobilize endogenous nutrient reserves to meet the resource demands of offspring production. These mobilized stores include calcium, phosphorous and other minerals that are resorbed from maternal bone to facilitate rapid mineralization of offspring bones. The extent to which bone mineral is resorbed is governed by the total amount of mineral taken in from the diet, but also by the competing demands of offspring and the minimum level of bone density that a female must sustain to support self-maintenance. The maximum amount of bone that a female may mobilize is undoubtedly dependent a variety of maternal traits, including age and reproductive experience (i.e., parity). We evaluated changes in serum concentrations of biomarkers of metabolic activity (total deoxypyridinoline [tDPD] and osteocalcin [OC]) of maternal bone and its relationship to reproductive output and parity throughout pregnancy and lactation in Yorkshire sows. Litter size did not affect bone metabolism; however, serum concentrations of both tDPD and OC were significantly higher in sows with little or no reproductive experience when compared to sows that had produced at least 3 litters prior to the current reproductive bout. This suggests a shift in ability or physiological strategy to meet offspring mineral demands that is acquired or associated with reproductive experience.
Acute Kidney Injury in a 31-year-old Male as a Consequence of Multiple Myeloma

We discuss a 31-year-old male who presented to the emergency room with a five-week history of dyspnea, chest pain, and right upper quadrant abdominal pain. Chest X-ray revealed a pleural opacity in the right lower hemothorax. Computed tomography (CT) of the chest showed a lytic rib lesion corresponding to the pleural lesion and multiple lytic lesions throughout the skeleton. Further labs revealed corrected calcium 4.43 mmol/L, total protein 115 g/L, creatinine 621 micromol/L, and urea 23.6 mmol/L. He had no prior labs for comparison. Subsequent bone marrow biopsy revealed a 50% involvement of plasma cells, which was consistent with a diagnosis of multiple myeloma (MM), and he was initiated on clone reduction therapy, with an excellent renal response but a partial hematologic response. This paper emphasizes that MM, though rare, should be in the differential diagnosis of acute kidney injury (AKI), as in this young adult.
Universal Dependency Parsing with a General Transition-Based DAG Parser

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.
The Optimum Design of Torsional Spring for Series Elastic Actuator

The use of series elastic actuators (SEA) in the applications with presence of human machine coupling has received extensive attention. One of the important research questions is how to design more efficient and compact flexible joints using physical springs. In this research work, we propose an optimization method for designing a planar torsional spiral spring with a shape of Archimedes curve. To define this optimization problem, the objective function and the constraint condition equations are put forward. We set the minimization of final volume, diameter and height of the spring as the optimization goal, the fixed stiffness of the spring as an equality constraint, the maximum deformation angle and the maximum stress of the spring when it reaches this angle, which should be less than the allowable, as two inequality constraints. To solve this optimization problem, the Spiral Dynamics Algorithm (SDA) is utilized to obtain the optimal parameter vector for this nonlinear constrained optimization problem. To validate the optimal result, a simulation with finite element analysis (FEA) is carried out, and the result shows that the obtained solution vector meets the design goal.
A Stable Trot Strategy of Quadruped Robot Based on Capture Point

Trotting is a kind of dynamic moving style for quadrupedal robot, which can make the robot move in fast and efficient way. However, the posture balance and the walking stability are two difficult problems for the robot control. In this paper, the dynamic model of the robot is obtained by Lagrange method. Based on the QR decomposition of the external force mapping matrix of the support leg, the constraints consistent dynamic model is obtained. The process of the quadruped robot trot is approximated as the locomotion of the linear inverted pendulum(LIPM). Based on the principle of the capture point for the biped robot, the trajectory of the swing leg during the trot is calculated. Through the method of joint torque optimization, inverse dynamic of robot is calculated, and each joint torque is obtained. The simulation results show that the method proposed in this paper can make the quadrupedal robot trot in a stable style.
Representation Learning for Image-based Music Recommendation

Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation. We propose a novel representation learning framework for image-based music recommendation that bridges the heterogeneity gap between music and image data; the proposed method is a key component for various contextual recommendation tasks. Preliminary experiments show that for an image-to-song retrieval task, the proposed method retrieves relevant or conceptually similar songs for input images.
Inverter Control and Implementation Options for a Novel AC Microgrid

The paper describes a novel decentralized AC microgrid architecture and its droopless primary control concept. It demonstrates the main features of the proposed microgid, including equitable current sharing, first order dynamics with no circulating currents, and stability in the presence of any load type. The paper discusses implementation and control aspects of the inverters connected to the common AC bus. It shows that a Current Source Inverter may be a preferred option for the battery storage interface to the microgrid. The findings of the paper are supported by extensive simulation results.
Natural Language Generation with Neural Variational Models

In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the Kullback-Leibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight annealing and word dropout. We also demonstrate the effectiveness of this continuous latent space through experiments such as random sampling, linear interpolation and sampling from the neighborhood of the input. We argue that if VAEs are not designed appropriately, it may lead to bypassing connections which results in the latent space being ignored during training. We show experimentally with the example of decoder hidden state initialization that such bypassing connections degrade the VAE into a deterministic model, thereby reducing the diversity of generated sentences. We discover that the traditional attention mechanism used in sequence-to-sequence VED models serves as a bypassing connection, thereby deteriorating the model's latent space. In order to circumvent this issue, we propose the variational attention mechanism where the attention context vector is modeled as a random variable that can be sampled from a distribution. We show empirically using automatic evaluation metrics, namely entropy and distinct measures, that our variational attention model generates more diverse output sentences than the deterministic attention model. A qualitative analysis with human evaluation study proves that our model simultaneously produces sentences that are of high quality and equally fluent as the ones generated by the deterministic attention counterpart.
Single-Photon Avalanche Diode-Based Detection and Imaging: Bringing the Photodiode Out of Its Comfort Zone

Our society is becoming more and more reliant on images, and we are getting used to having a visual representation of everything. However, our eyes are, in some ways, quite limited; they can see "just" colors and their intensity. What could we see if we were able to capture every piece of information that a single photon carries? Being able to measure, for example, the position, direction, wavelength, polarization, and time of arrival of every photon would give us the basis for having an almost fully complete understanding of the scene. Even though not all of these quantities need to be measured together, single-photon sensing can enable more and new dimensions to be added to the information space that we are gathering during an image acquisition.
3D Laser Odometry for a Mobile Robot Platform

We propose a method for odometry in real-time using a 2D lidar with a pitching motor. The self-position estimation problem is difficult due to the measurement datasets of point clouds collected at different time stamps, and errors in motion estimation leading to the registration problem for distortion removed point cloud. To validate the proposed method, the corresponding 3D maps can be built without the need for inertial measurements. However, using an Inertial Measurement Unit (IMU) may help the motion estimation in the Levenberg-Marquardt iterative process. This paper aim to decompose difficult problem in mobile robot localization and navigation in two parts: One process calculates the robot's motion estimation in a rough way; another method matches the neighbor frame of point clouds obtained in the global reference for the accurate transform matrix. Thus we obtain the odometry of the robot platform, by implementing the precise motion estimation, we can then construct the environment map consequently. The algorithm is tested by dataset from experiments in the factory laboratory, and the reconstruction map show that our implementation is efficient and accurate.