Meet Sigfredo,

He is a Physics major and an URA Scholar. Sigfredo transferred from Anne Arundel Community College with an Associates of Arts in Communications. He is an advocate for undergraduate research. "Take the opportunity if you can find it. It is eye opening and satisfying when you reach personal milestones. Find a mentor that you feel comfortable with. Take it as seriously as you would school work."

His research explores the skies above and is trying to understand the current mismatch between our observational and theoretical capabilities in the effort to understand the gamma-ray variable emission of blazars coming from the vicinity of a black hole. The goal of his research is to develop realistic numerical simulations to model the variable gamma-ray emission of luminous blazars, powerful relativistic jets emanating from the vicinity of the supermassive black holes found in the centers of active galaxies.  

Read more about his research here…

Rachel Denby is at MD DNR in the Monitoring and Non-Tidal Assessment Division. As part of her job, Rachel is a crew member that assists with the Maryland Biological Stream Survey (MBSS), all over the state. Through MBSS, they collect data on stream biota (mostly fish), water and physical habitat quality, and geomorphology. Rachel also assists with mussel surveys that take place largely in Harford County and with processing macroinvertebrates with microscopes. During her CCC year, she will also focus on restoring the Blackbanded Sunfish in Maryland, an endangered fish, by developing partnerships and assisting with stream data entry, analysis, interpretation, and presentation.

Ellen Woytowitz will provide support for the USGS MD-DE-DC Water Science Center with the Baltimore Urban Waters Partnership (BUWP), a federal initiative that aims to reconnect urban communities to their waterways. Her capstone project will determine if a relationship exists in the Gwynns Falls watershed between water quality constituents, specifically bacteria and total suspended solids, and proposed factors of influence including climate, land use, sewer overflows, infrastructure repairs, Best Management Practices (BMPs), and socio-economics & demographics. This analysis will help BUWP stakeholders determine if investments in restoration and compliance for stormwater permits, sewer overflow consent decrees, and Total Maximum Daily Loads (TMDLs) have produced desired improvements in water quality. Additionally, Ellen will assist with fieldwork and data processing for the Baltimore Ecosystem Study (BES) Long-Term Ecological Research (LTER) and Baltimore Streamflow Gage Network and Water Quality Real-Time Networks in greater Baltimore and D.C.

Jake Leizear will work with the Chesapeake Conservancy for the next year, focusing on improving access to green spaces and the Patapsco River in Baltimore and raising awareness of and engagement in the Greater Baltimore Wilderness Coalition. Through this Coalition, the Chesapeake Conservancy works with partners such as the United States Fish and Wildlife Service and Baltimore City Department of Recreation and Parks to advance urban wildlife conservation, public access, and environmental opportunities in Baltimore.

Corinne Griffith is working with the Prince Georges County Public Schools, over at the Schmidt Outdoor Education Center.  Her role is to facilitate and lead school groups in a unique outdoor education experience.  This includes taking children out into the elements and educating them on a diversity of topics, including: stream ecology, forest ecology, and pollination.  She is a specialist in the first grade program, but also helps across grade levels.

Psalm 136: 1, 23-24 - “Give thanks to the Lord, for he is good. His love endures forever.He remembered us in our low estate. His love endures forever. And freed us from our enemies. His love endures forever.”

“Diamonds are forever” - at least that’s what the jewelry industry hopes enamored couples everywhere will believe. Unfortunately, a diamond often outlasts the marriage it was meant to symbolize. Human love is not flawless.

So where can you find flawless and eternal love? Psalm 136 asserts repeatedly that God’s love endures forever. This song served as an anthem or pledge of mutual love between God and his people. Old Testament priests sang the first phrase in these verses while the congregation answered with the refrain: “His love endures forever.” It was much like an exchange of wedding vows between God and his bride, the nation of Israel (Isaiah 62:5).

God desires the closeness of such a relationship with each of us for eternity. And his love lasts beyond a lifetime. Not only does God offer us enduring love, he also extends to us endearing love, a strong affection that lifts one higher or increases the value of the one beloved. Enduring speaks of the quantity of God’s love while endearing portrays the quality. The psalmist says that God “remembered us in our low estate” (v 23). We are nothing; but God loved us before we were lovely. We are his diamonds in the rough.

In the rough, a diamond looks like a common pebble. The stone must undergo a finishing process to bring out its full radiance. It is then held against a large grinding wheel to cut and polish the stone. Many meticulous hours are spent perfecting it.

Like the nation of Israel, God has lifted you out of the rubble of a worthless life. He is polishing and chiseling you into a jewel of great beauty. You are his diamond. You are his bride being prepared for the bridegroom. And his love endures forever.
(From the NIV Women’s Devotional Bible.)

Today let’s worship God and give thanks to him for his enduring, endearing love for us.

-AP

Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.