).
The minimum deposit is low, at 50$ only but bonuses are unclear. Some parts of their website state that bonuses will be awarded but live chat told me they don’t offer bonuses. It’s unclear which version is true but their T&C say that accepting bonuses means you cannot withdraw unless you have reached a trading volume of up to 50 times the bonus. The transaction methods supported are Visa, MasterCard, Maestro, Neteller, Skrill, Perfect Money, FasaPay and others. The minimum withdrawal amount is 10$ and the advertised processing time is 2 business days.
The platform can run two charts/assets at the same time and a full-screen feature is available, something we have not seen with many brokers before. ExpertOption is also launching an ICO for its own coin: EO Coin, which will power three platforms. For more info about the ICO, read the White Paper.
The Ocean County Prosecutor's Office announced on Thursday that Alan McGinnis, 27, of Pemberton Township, was indicted for the murder of Cody MacPherson, 20, also of Pemberton Township.
PEMBERTON TOWNSHIP — A grand jury indicted a 27-year-old township resident for the murder of Cody MacPherson, Ocean County Prosecutor Joseph Coronato announced Thursday.
Alan McGinnis was charged with six counts in the death of MacPherson, including two first-degree murder charges. He was also indicted on charges of kidnapping, possession of a weapon for an unlawful purpose, unlawful possession of a weapon and desecrating human remains.
McGinnis was charged with killing MacPherson in April 2018, a month after his remains were found in a wooded area in Ocean County.
MacPherson, 20, had recently moved to the township from Portage, Indiana. He was last seen on Sept. 23, 2017, at a home in the Browns Mills section of town. A day later, Pemberton Township police filed a missing person’s report.
On March 19, Richard Byrne, a district warden with the New Jersey Forest Service was conducting a controlled burn in the Colliers Mills Wildlife Management Area in Jackson Township, Ocean County at about 1:30 p.m., according to the Ocean County Prosecutor’s office. Byrne was preparing the area when he noticed human remains under logs and what he believed to be a human skull. After calling police, a suppression team responded to put out the burn and preserve evidence in the area.
A medical examiner determined the cause of death was a stab wound to the left abdomen. State police received confirmation from a forensic anthropologist that DNA analysis of the remains matched MacPherson, authorities said.
An investigation, which included the execution of two search warrants in Pemberton Township and Jackson, revealed that McGinnis allegedly stabbed MacPherson at a residence on Pardee Boulevard before putting his body into the trunk and driving him to Colliers Mills Wildlife Management Area. McGinnis then returned to Pemberton where the scene was then cleaned of blood and any other evidence, authorities said.
About one week later, McGinnis returned to the site where he left MacPherson to attempt to destroy any evidence associated with the crime, police said.
McGinnis is being held in the Ocean County Jail. If convicted, he faces 30 years to life in prison.University life students learn about FMU services
Photo by: Contributed Photo
Students in University Life 100 volunteer at Help4Kids warehouse packing food for hungry children as a part of the course requirements.
September 30, 2015
At the beginning of this semester, Student Health Services, Campus Police, The Office of Student Affairs and the Rogers Library spoke to the students taking University Life 100 (UL) classes and informed students about services provided on campus.
The presentations each had specific information to offer to freshmen, and most UL students were required to attend these sessions. Some professors required their students to just sign in, while others asked them to write a reflection on what they learned from the presentations.
The Office of Student Affairs focused on students’ rights and responsibilities. Students were introduced to Family Education Rights and Privacy Act (FERPA), which guarantees that the university will not disclose any information about a student even to the student’s parents without prior permission from the student. Student affairs also told students about how they could participate in student government and explained other ways they can become active on campus.
“If students only take one thing from the student affairs presentation, I would like it to be that we care about their success,” Teresa Ramey, dean of student affairs, said. “We are available to all students, and student affairs is a great resource for college students.”
Student Health Services talked to students about how to be healthier in college, the common health concerns of students and the services that are provided through student health.
“The student health presentation was the most helpful because it gave me information on where I can go if I need help,” Aaliyah Watson, a freshman psychology major said.
Campus Police used their presentation to talk to students about campus rules and regulations. They also explained the safety boxes on campus and the non- emergency services that Campus Police provides to students at FMU.
“I hope the student[s] will take away a few ideas regarding how the can stay safe on campus,” Donald Tarbell, campus police chief, said. “A few simple ideas like being aware of your surroundings, knowing where you are going and carrying your cellphone with you are all good ways a student can keep themselves safe.”
The library presentation discussed getting student ID cards made, checking out materials and accessing the library online databases. The presentation also addressed the computer and printing issues and assured students that a specialist came and fixed the problem that was making logging on and using the computer a slow process.
The presentations were designed students enrolled in UL. UL is recommended for freshmen to help them become more informed about campus. The class is worth one credit hour and only runs half of the semester.
“[University Life] gets you involved,” Kandace Solomon, a freshman pre- nursing major, said. “I probably wouldn’t have come to these presentations if they were not required.”
Overall, the students expressed that more structure to the class would improve the experience, slowing down the presentations having less information in each presentation would improve the UL presentations themselves.Pneumonia Detection in Chest Radiographs

In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only \emph{open-source} deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution was recognized as a winning entry in a contest which attracted more than 1400 participants worldwide.


I. INTRODUCTION
Recent advances in computer vision demonstrate that Convolutional Neural Network (CNN) architectures achieve human-level performance on several image-processing tasks, including classification, segmentation, and object detection. As a natural extension of this, many on-going efforts exist to leverage its capabilities in medical imaging. Clinicians utilize such algorithms for assistance in anomaly detection in a variety of medical imaging modalities. For example, CNNs achieve performance on par with board-certified dermatologists for the classification of skin cancer on biopsyproven clinical images [1] and board-certified radiologists for pathology detection in computerized tomography head scans [2].
The next frontier for AI in medicine is anomaly localization in medical imaging. Anomaly localization refers to predicting anomalies as well as their boundaries. Currently, few CNN-based anomaly localization algorithms exist because training these algorithms requires a large number of professionally curated images for each disease type and modality of measurement. However, the RSNA Pneumonia Detection Challenge [30] provided such a dataset and it became a testing ground for anomaly localization algorithms, attracting over 1400 participating teams.
Here, we present our winning solution to the RSNA Pneumonia Detection Challenge. We will discuss our training, inference and evaluation methodologies that were based on an ensemble of the CoupleNet [15] object detection architecture.

II. BACKGROUND
A. Relevant Work P. Viola and M. Jones [3] introduced the first efficient object detection (face recognition) algorithm in which they introduced a novel image representation called integral image. Half a decade later, N. Dalal and B. Triggs outperformed 1 Contact: Jameson Merkow, jameson@deepradiology.com existing feature sets by developing Histogram of Oriented Gradient (HOG) for pedestrian detection [4]. After another half a decade, Felzenszwalb et al. [13] developed the Deformable Parts Model (DPM) that uses mixture model of deformable parts to recognize objects.
Two predominant deep neural network based object detection approaches exist today. The first, single-shot detectors, pose object detection as a regression problem and perform object detection as well as classification in a single forward pass of the network. This method has been used successfully in a numbers of approaches, such as Single Shot MultiBox Detector (SSD) [29], You Only Look Once (YOLO) family, [25], [26], [27] and Feature Pyramid Network (FPN) [28]. The second method, based on region proposals, use a multistage pipeline that simultaneously learns to propose accurate bounding boxes and classify them. Since, our winning entry uses a region proposal-based detector, we will keep our description focused to region proposal based methods.
