Special Collections is excited to announce that processing of the Society of Protozoologists records (Collection 13) has recently been completed to include new materials. An updated record of the collection can now be viewed online.

The Society of Protozoologists records are among the holdings of the Center for Biological Science Archives (CBSA) at UMBC. The collection includes files that document the development and growth of the Society of Protozoologists (currently known as the International Society of Protozoologists) from its founding in 1947 to the 2010s.

New materials include correspondence, reports, meeting notes, publications, newsletters, media, photographs, and financial statements. The process included rehousing received materials into acid-free folders and boxes and integrating the materials into the existing collection.

Additionally, two new series were created out of existing and newly received materials. Series VII: Media, Photographs, and Artwork contains figures, photographs, and audio files produced or held by the Society of Protozoologists. And Series VIII: Committees contains correspondence and reports relating to the Society’s various committees including the Business Advisory Committee, the Constitution and By-Law Committee, and the Education Committee. In total the collection is now arranged in eight series: History; Executive Committee; Membership; Meetings; Publications; Affiliated Societies; Media, Photographs, and Artwork; and Committees.

The completion of this project is due to the help of Special Collections student assistants Graham Johnson and Riley Auer and graduate assistant Sarah Huston. To learn more about the Society of Protozoologists records or to view these materials, check out the new finding aid or stop by and view the collection in person at Special Collections!

Many thanks to Sarah Huston '17, who served as a graduate assistant in Special Collections from 2015-2017. Sarah assisted with the rehousing of the collection and wrote this post. Thank you, Sarah, we miss you! -- Lindsey Loeper, Archivist

Baltimore Art Gallery: Graphic Design/Marketing Assistant Full Time Local art gallery is searching for a dynamic individual to join our team.

About The Gallery:
For over 30 years, we have been helping our clients create beautiful and unique collections. With three locations, we house the largest collection of art in the Mid-Atlantic region. We are dedicated to providing our clients with the highest standards of service and finest artwork and custom framing available.

John 13:3-5 (NLT) - “Jesus knew that the Father had given him authority over everything and that he had come from God and would return to God. So he got up from the table, took off his robe, wrapped a towel around his waist, and poured water into a basin. Then he began to wash the disciples’ feet, drying them with the towel he had around him.”

Jesus who had authority over everything chose to serve, took the humble position of a slave by wrapping a towel around his waist to perform a task that no one wanted to do. Probably the disciples were wondering amongst themselves, who is going to wash our feet before supper. Jesus got up and washed the feet of the disciples. In fact, this act was another way that Jesus displays the love that He has for us and what He will do ultimately on the cross. Jesus will lay down his life on the cross to cleanse us from our sins!  This was what we ultimately needed, but Jesus did not hesitate in washing our feet to serve our physical need!

Let’s take a moment to worship God for who He is and exalt His name above all names! He alone is worthy of our praise and all of our lives.

-DP

Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

Filip Dabek

11:00-1:00 Thursday 13 July 2017, ITE 346, UMBC

History is nothing but a catalogued series of events organized into data. Amazon, the largest online retailer in the world, processes over 2,000 orders per minute. Orders come from customers on a recurring basis through subscriptions or as one-off spontaneous purchases, resulting in each customer exhibiting their own behavioral pattern when it comes to the way in which they place orders throughout the year. For a company such as Amazon, that generates over $130 billion of revenue each year, understanding and uncovering the hidden patterns and trends within this data is paramount in improving the efficiency of their infrastructure ranging from the management of the inventory within their warehouses, distribution of their labor force, and preparation of their online systems for the load of users. With the ever increasingly availability of big data, problems such as these are no longer limited to large corporations but are experienced across a wide range of domains and faced by analysts and researchers each and every day.

While many event analysis and time series tools have been developed for the purpose of analyzing such datasets, most approaches tend to target clean and evenly spaced data. When faced with noisy or irregular data, it has been recommended to undergo a pre-processing step of converting and transforming the data into being regular. This transformation technique arguably interferes on a fundamental level as to how the data is represented, and may irrevocably bias the way in which results are obtained. Therefore, operating on raw data, in its noisy natural form, is necessary to ensure that the insights gathered through analysis are accurate and valid.

In this dissertation novel approaches are presented for analyzing irregular event sequences using a variety of techniques ranging from deep learning, reinforcement learning, and visualization. We show how common tasks in event analysis can be performed directly on an irregular event dataset without requiring a transformation that alters the natural representation of the process that the data was captured from. The three tasks that we showcase include: (i) summarization of large event datasets, (ii) modeling the processes that create events, and (iii) predicting future events that will occur.

Committee: Drs. Tim Oates (Chair), Jesus Caban, Penny Rheingans, Jian Chen, Tim Finin

The post PhD Proposal: Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning & Visualization appeared first on Department of Computer Science and Electrical Engineering.

Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

Filip Dabek

11:00-1:00 Thursday 13 July 2017, ITE 346, UMBC

History is nothing but a catalogued series of events organized into data. Amazon, the largest online retailer in the world, processes over 2,000 orders per minute. Orders come from customers on a recurring basis through subscriptions or as one-off spontaneous purchases, resulting in each customer exhibiting their own behavioral pattern when it comes to the way in which they place orders throughout the year. For a company such as Amazon, that generates over $130 billion of revenue each year, understanding and uncovering the hidden patterns and trends within this data is paramount in improving the efficiency of their infrastructure ranging from the management of the inventory within their warehouses, distribution of their labor force, and preparation of their online systems for the load of users. With the ever increasingly availability of big data, problems such as these are no longer limited to large corporations but are experienced across a wide range of domains and faced by analysts and researchers each and every day.

While many event analysis and time series tools have been developed for the purpose of analyzing such datasets, most approaches tend to target clean and evenly spaced data. When faced with noisy or irregular data, it has been recommended to undergo a pre-processing step of converting and transforming the data into being regular. This transformation technique arguably interferes on a fundamental level as to how the data is represented, and may irrevocably bias the way in which results are obtained. Therefore, operating on raw data, in its noisy natural form, is necessary to ensure that the insights gathered through analysis are accurate and valid.

In this dissertation novel approaches are presented for analyzing irregular event sequences using a variety of techniques ranging from deep learning, reinforcement learning, and visualization. We show how common tasks in event analysis can be performed directly on an irregular event dataset without requiring a transformation that alters the natural representation of the process that the data was captured from. The three tasks that we showcase include: (i) summarization of large event datasets, (ii) modeling the processes that create events, and (iii) predicting future events that will occur.

Committee: Drs. Tim Oates (Chair), Jesus Caban, Penny Rheingans, Jian Chen, Tim Finin

As an honors university, we take great pride in preparing you, our student, to be successful in all of your pursuits. Managing money is an important part of your daily life and we want to assure that you are well-versed in this important life skill that many find challenging.  

Your journey to financial wellness begins now with our interactive (and entertaining) online Cash Course! This multi-module, self-paced course ensures that you are as well-prepared to manage your budgets, bank accounts, assets and debt as you are to master your academic and career interests. To enroll in the online Cash Course click 
here.

Once you’re enrolled in the course, you’re welcome to complete as many modules as you’d like….whenever you’d like. But here’s where financial smarts start to pay off….

● If you complete one module, your name will be entered in our $100 bookstore voucher drawing. 
● If you complete two or more modules, your name will be entered in our $300 bookstore voucher drawing. 
● If you complete all modules, your name will be entered in our $500 bookstore voucher drawing. 

All drawings will take place on September 15, 2017. So don’t wait...get started now! If you have questions about the online Cash Course, please feel free to contact us at financialsmarts@umbc.edu.

Enjoy the remainder of your summer and best wishes for a successful first semester at UMBC!

Sincerely,

Your UMBC FinancialSmarts Team

Hello, Future Retrievers!

Today, I come to you in complete and utter sadness as I am bringing to you our last and final blog post. You will be able to stay up to date with what is going on here at UMBC by following the university’s social media handles, @UMBC, on Facebook, Twitter, Snapchat and YouTube, and @UMBClife on Instagram.

If you have any questions for the Office of Undergraduate Admissions, please do not hesitate to contact us at 410-455-2292.

Enjoy your Summer Break! We look forward to seeing you on campus!