text:
Teaching Reflections
Engaging the Public with and Preserving the History of Texas's First Public Historically Black University
Authors: {'first_name': 'Marco', 'last_name': 'Robinson'},{'first_name': 'Phyllis', 'last_name': 'Earles'}
The silences and erasures surrounding the histories of historically black colleges and universities (HBCUs) in many instances are caused by limited technology, lack of financial resources, and, most importantly, institutional priorities. Many aspects of HBCUs' histories, particularly in the state of Texas, have been relegated to historical voids or are becoming endangered knowledge. These are the issues that jeopardize the long and rich history of Prairie View A&M University (PVAMU), Texas's first public supported historically black university, which dates back to the post-Reconstruction and Jim Crow eras of American history. Emancipated blacks in Texas sought all avenues available to them to obtain an education, including establishing churches and schools. Freed people's efforts culminated in the creation of Alta Vista School for Colored Youth, which subsequently became PVAMU following several name changes. During the Jim Crow era, PVAMU served as the administrative home base for black education in the state of Texas, offered agricultural extension services to black farmers, and served as the central facility for black grade school athletics and extracurricular activities. Due to lack of personnel and resources, all of the archival collections that document this history are unprocessed and unavailable to the public. This article considers the collaborative efforts of the history faculty and the Special Collections and Archives (SCAD) staff at PVAMU to bring light to this important history through preservation projects, public programming and student engagement activities. Additionally, the article uses endangered archival materials from PVAMU's Special Collections to explore the history of this important institution of higher education.
Keywords: Historically Black Colleges and Universities, Prairie View A&M University, historic preservation, archival studies, digitization, oral history, service learning, community engagement, collaboration, Texas History, Black Texans
DOI: http://doi.org/10.5334/kula.33
Accepted on 20 Aug 2018 Submitted on 12 Mar 2018
The recent debut of the documentary Tell Them We Are Rising: The Story of Black Colleges and Universities (HBCUs) has stirred much discussion and brought light to the histories of institutions of higher education established for emancipated blacks in the United States following the Civil War.1 As the documentary shows, emancipated blacks joined forces with church-affiliated missionary societies and philanthropic organizations and began organizing schools all across the American South, including Texas. Historian Heather Williams argues that '[a]cquiring literacy in conjunction with freedom had the potential to open access to democratic political activity, and that in turn held a promise of enabling African Americans to participate in shaping the civil society in which they had hitherto been considered chattel' (2005: 69). In this political climate, Prairie View A&M University (PVAMU), Texas's first public supported historically black college and university, was established during the late 1800s (See Figure 1).
A skyline view of Prairie View State Normal and Industrial College, early 1900s. Courtesy of PVAMU's Special Collections and Archives Department.
The history of black colleges and universities in the US mirrors the country's longer struggle with Jim Crow segregation and centers the role of education in the fight for racial justice (Cantey and Bland 2013). Out of these turbulent circumstances, historically black colleges and universities were born. PVAMU's story is a fundamental part of the black experience in Texas, Texas educational history, and US educational history. PVAMU administrators, faculty and staff played a significant role in the fight for racial justice in Texas, the American South, and the country, working alongside organizations on the national stage. Thus, PVAMU's history is a significant piece of Americana and entails an important chapter in the American experience.
Currently, several of the signature archival collections housed by PVAMU are in dire need of being processed. Additional funding is needed to hire an audio digitization expert, and more space needs to be secured for storing the vast collections. This article highlights the efforts of the history faculty and PVAMU's Special Collections and Archives (SCAD) to document and preserve the Jim Crow-era history of PVAMU and to engage the public with the history of PVAMU. The history highlighted in this article covers significant events related to PVAMU's history during the segregation era. We examine the importance of PVAMU's Jim Crow-era history and the importance of efforts to preserve artifacts and documents, digitize photo collections, promote student engagement, and raise public awareness regarding this endangered history. The collaborative efforts of faculty and staff to save and record the history of their institution provides a model for other HBCUs to follow and brings light to the rich history of these often forgotten institutions of higher education.
The Importance of PVAMU's Jim Crow-Era History in State, Regional, and National Contexts
PVAMU was founded as Alta Vista College for Colored Youth by state legislative decree on August 14, 1876 (Nojeim and Jackson 2011). In the earlier part of 1876, William H. Holland, a black state representative from Waller County, Texas, sponsored legislation to create a state-supported college for black youth (See Figure 2). Unlike the church denomination and missionary society beginnings of many other HBCUs, Alta Vista College for Colored Youth was a satellite of the Agricultural and Mechanical College of Texas, which was to be the all-white state flagship institution of higher education (now known as Texas Agricultural and Mechanical University, located in College Station, Texas).2 According to historian George Ruble Woolfolk, the move to establish a state-supported educational institution for emancipated blacks coincided with the movement across the state of Texas for compulsory education and a statewide publicly supported education system (1962: 28). Historian James Anderson concludes that '[f]ormer slaves were the first among native southerners to depart from the planters' ideology of education and society and to campaign for universal, state-supported public education' (1988: 4). Anderson further asserts, '[i]n their movement for universal schooling the ex-slaves welcomed and actively pursued the aid of Republican politicians, the Freedmen's Bureau, northern missionary societies, and the union army' (1988: 4). Representatives of the state legislature chose the Alta Vista Plantation (also known as the Kirby Plantation) in Waller County, Texas as the site to locate the school for black youths.3
Kirby Hall, the first building on campus and the former main house of the Kirby Plantation. Courtesy of PVAMU's Special Collections and Archives Department.
L. W. Minor, a native Mississippian, was chosen as the first principal of Alta Vista College for Colored Youth.4 The first eight students enrolled in the school on March 11, 1878. Initially, the school operated as a grade or normal school with aspirations of being a full-fledged college (Woolfolk 1962). However, the first fifty years of the school's existence were very challenging due to lack of funding, low enrollment and no legislative support. Ernest Anderson and Laurine Anderson, two brothers, were principals at the institution for the next fifteen years (Ernest served from 1879 to 1885 and Laurine from 1885 to 1895). Ernest died in office at the age thirty-five, and Laurine took over, bringing vitality to campus life by working to improve the instruction and living accommodations (Woolfolk 1962: 5) (See Figure 3).
The principals and presidents of PVAMU up to 2016. Ruth Simmons, the current and first female president of the university, is not pictured. Courtesy of PVAMU's Special Collections and Archives Department.
As the Age of Segregation loomed over the American South, the focus of black education went to industrial and vocational training. Booker T. Washington championed industrial education across the American South and was founder of the Tuskegee Institute. Edward Blackshear, president of PVAMU from 1895 to 1915 and close acquaintance of Booker T. Washington, shaped the programs to reflect this trend. Prairie View women were trained in the domestic sciences, while men were trained in a wide range of vocational trades, including agricultural sciences, automotive repair and carpentry (Woolfolk 1962: 30) (see Figures 4 and 5). This was a formative period in the school's history, which proved the institution's importance in teaching blacks in Texas and in the South. Many of PVAMU's students from this period—for example, Frederick Patterson—went on to be educators and leaders in their communities. Patterson became president of Tuskegee University and was the founder of the United Negro College Fund (see Figure 6).
Female students in a domestic science class at PVAMU during the early 1900s. Courtesy of PVAMU's Special Collections and Archives Department.
Male students participating in an auto mechanic demonstration at PVAMU during the early 1900s. Courtesy of PVAMU's Special Collections and Archives Department.
Article on Fredrick Patterson becoming president of Tuskegee University in the Prairie View Standard, April 1935. Courtesy of PVAMU's Special Collections and Archives Department.
Isiah Milligan (I. M.) Terrell became principal of PVAMU in 1915 and was instrumental in helping to establish the Colored Cooperative Extension Service (CES), which served blacks all across the state of Texas (Nojeim and Jackson 2011). The CES provided local farmers with training on agricultural techniques and instruction in recent technological farming advances, and demonstration agents were assigned to black communities by counties across Texas. During the Jim Crow era, PVAMU was the home base for blacks to receive instruction in the agricultural sciences. Due to the stringent state segregation laws, blacks were not offered the same services from white institutions. PVAMU stepped forward to support black farmers at a critical time in US history, when the country was exiting World War I and was close to entering a severe economic depression (see Figures 7 and 8). In addition, PVAMU served as the home administrative base for black public schools in Texas by hosting a yearly Negro Education Conference, which was initiated under the administration of Willette Rutherford Banks, principal from 1926 to 1946 (see Figure 9). The annual conference gatherings were used to discuss issues impacting black education and communities in Texas.
Agricultural Demonstration Yearbook about buying seeds, 1950. Courtesy of PVAMU's Special Collections and Archives Department, Agricultural Extension Collection.
Photograph of a livestock management demonstration on butchering hogs, 1937. Courtesy of PVAMU's Special Collections and Archives Department, Agricultural Extension Collection.
Program pamphlet for Annual Conference on Education in Texas, 1966. Courtesy of PVAMU's Special Collections and Archives Department, Annual Negro Education Conference Collection.
Nationally, PVAMU was the first HBCU assigned an Army ROTC cadet unit and a Navy ROTC unit. The Army ROTC unit was established directly after World War I, and the Navy unit was established during the early 1970s. Many of the cadets from the PVAMU ROTC units have gone on to become high-ranking officers in the United States military (Nojeim and Jackson 2011) (see Figure 10). In addition to producing valiant military servicemen and women, PVAMU served as the primary medical hub for the black community in Waller County during the Jim Crow era. PVAMU housed the only hospital for blacks in the immediate area, and the university hosted an annual meeting for black physicians from across the state of Texas during the early 1900s (see Figures 11 and 12). At state, regional, and national levels, PVAMU administrators, faculty, staff and students had a profound impact on American life and culture during the first half of the 1900s.
Army ROTC cadets standing at attention, c. 1950. Courtesy of PVAMU's Special Collections and Archives Department, ROTC Collection.
The campus hospital, 1917. Courtesy of PVAMU's Special Collections and Archives Department.
Picture of the Post-Graduate Assembly of Negro Physicians, early 1900s. Courtesy of PVAMU's Special Collections and Archives Department.
Preservation Efforts and Student Engagement
The Jim Crow Collections held within PVAMU's Special Collections and Archives are endangered due to a lack of funding to fully process the collections, the absence of an expert on staff to digitize the audio recordings of the collections, and limited space to house the collections. Paper and audio materials are decaying and are at risk of never being revealed to the public. PVAMU's Special Collections and Archives Department (SCAD), housed in the fifth floor of the main campus library, has limited space for its more than 1,000 rare books, more than 20 manuscript collections, and more than 25,000 photographs. SCAD has two full-time staff members, including the lead archivist, who are assisted by several part-time student workers. However, the labor and expertise needed to fully process these collections are more than the SCAD staff can handle. As of the summers of 2017 and 2018, the staff had made modest progress towards making the Jim Crow Collections available to the public.
The collections still need to be processed, digitized, and described in an online database. In addition, some materials require special conservation and preservation attention. For instance, the Negro Education Collection has rare and vulnerable magnetic audio tapes that SCAD does not have the equipment, or funding, to digitize. During the fall of 2016, SCAD staff met with and solicited assistance from PVAMU's history faculty to assist with preserving these endangered items and to create a joint service-learning program where students would gain a meaningful experience through doing archival work and engaging in historic preservation. The initial meetings led to the creation of service-learning components in two history research courses to involve students in the preservation of several archival collections related to the school's history.5
By linking theory with practice and classrooms with communities, service-learning provides real-world exposure and engagement with meaningful local and global issues through concrete and ameliorative practices (Butin 2006). SCAD staff and history faculty agreed that the overall goals of the joint project would be to provide the students with a meaningful experience working within the realm of archival studies and historic preservation, to preserve the collections that needed the most attention, and to create programming that allowed the collections to be discussed and displayed for the public. Ultimately, faculty and staff wanted student participants to gain tangible skills that they could apply to jobs in the public and private sectors of education and public history, or to advanced research at the graduate level. As a result, the service-learning component of the courses focused on the digitization and cataloging of the George Ruble Woolfolk Collection, the Agricultural Extension Collection, and the Negro Education Conference records. The collections chosen were most relevant to the students' in-class research projects. The faculty and staff also agreed to add an oral history collection component to the project.
In order for student participants to have a thorough understanding of archival procedures and historic preservation, SCAD staff and history faculty conducted several training sessions. The subjects of the training sessions ranged from how to properly handle fragile documents and scan items to how to catalogue the items and make finding aids. Additionally, students were trained in conducting oral histories. In a Historical Methods research course, the instructor introduced the oral history component of the service-learning project, which entailed having students interview former PVAMU students who lived during the Jim Crow era. All of the training sessions were conducted in accordance with the best practices suggested by the Society of American Archivists and the Oral History Association. During the sessions, the facilitators conveyed to the students the importance of following the outlined procedures to ensure that the materials were properly processed, signed and secured and that students were using the best practices for preserving historical items (see Figures 13, 14, 15).
Chanel Williams, student participant, scanning items from a PVAMU yearbook for her research project, 2017. Courtesy of PVAMU's Special Collections and Archives Department. (The picture is displayed with the permission of the student).
Lisa Stafford, PVAMU SCAD staff, and Chanel Williams, student participant, sorting through boxes that need to be inventoried, 2017. Courtesy of PVAMU's Special Collections and Archives Department.
(Left to right) Dr. Walle Engedayehu, chair of the Division of Social Work, Behavioral, and Political Sciences; Adrienne Cain, assistant director of the Baylor University Institute for Oral History; and Dr. Marco Robinson, assistant professor of history, PVAMU, at a workshop on oral history in 2016. Courtesy of PVAMU's Special Collections and Archives Department.
Programming and Raising Public Awareness
A number of public panels and forums were held during the 2016–17 academic year to bring attention to the history of PVAMU and the efforts to preserve the school's rich past. The SCAD staff and history faculty team's goals for hosting these events were to provide information on PVAMU's history and to make the campus community and local community aware of the project underway. Additionally, the team wanted to solicit volunteers to expand the project, to gain candidates for interviews and to determine whether individuals who had rare materials wanted to loan them to be scanned or donate them to PVAMU. Most importantly, the team wanted to gain campus and community buy-in with the project and build a viable infra-structure of volunteers to ensure the project's longevity and sustainability. The events proved to be successful due to the number of individuals from the immediate community who volunteered to be interviewed, and the needs of the SCAD were brought to PVAMU administrators' attention. As a result, Dr. Lauretta Byars, former vice president of Student Affairs, became an active participant in the preservation effort (see Figure 16).
Dr. Lauretta Byars, vice president of Student Affairs, PVAMU, during the Archives Week Program, 2017. Courtesy of PVAMU's Special Collections and Archives Department.
Overwhelmingly, the events were well attended by PVAMU's students and members of the local community in Prairie View. Also, several PVAMU alumni reached out to the team to loan and donate items to be added to the SCAD collections. In addition, two of our student participants did poster board presentations at a campus research symposium about the material from the Woolfolk and Negro Education archival collections that they processed and scanned, as well as their subsequent research (see Figure 17). The SCAD staff and Dr. Marco Robinson were able to reach all of the goals set for the public programming. However, they still faced a number of challenges in order to maintain the momentum of the project, which are addressed in the next section.
Chanel Williams, student participant, with her poster board presentation about the George R. Woolfolk Collection, 2017. Courtesy of PVAMU's Special Collections and Archives Department.
Conclusion: Challenges of Preserving Endangered Collections and Knowledge at a HBCU
As with all preservation projects, funding is imperative. The team is currently seeking internal and external funding to support the continued work of digitizing, cataloging and making finding aids for the PVAMU SCAD Jim Crow Collections. Server and cloud space are needed for storage and web-hosting of digital stories and video-recorded oral histories. The current space is not adequate and does not have the appropriate database functionality. Making sure that these technological resources are available will enable the team to be better suited to train the student participants. Over the past three semesters, over thirty students have worked on the project in some capacity, but it is important for the overall success of the project that we gain more faculty participation. One year into this project, we have realized the importance of networking and building relationships on campus with faculty and off campus with community members. In many cases, faculty are not aware of the various ways they can incorporate archival projects in their courses. To address this issue, the archival staff is providing informational sessions. Faculty participation is essential to making sure that this project continues for years to come.
For research teams interested in engaging in projects like this, it is important to form a good relationship with your university special collections and archives department. In addition, it is important to carefully plan the roles that students and the local community will play in the project so that each group gains from the experience. PVAMU students gained a wealth of experience, and the community participants were very appreciative that PVAMU's history was being preserved. Chanel Williams, a sophomore biology major, stated, 'I learned so much about the school's history and processing historical collections while working in archives' (Williams, personal interview with author, August 25, 2017). Mrs. Jemmie Poindexter, a local resident, explained, 'I wasn't aware that this preservation project was going on, but I am glad to assist in any way that I can' (Poindexter, personal interview with author, July 15, 2017). The relationships formed from these interactions and partnerships with community members are important to a project's longevity and sustainability.
It is important that the home department or division of the project members provides the necessary support and resources to ensure the project's success. Finally, it is equally important to form relationships with local, state, and national preservation organizations. In many instances, through these organizations, teams can find the expertise and resources needed to make sure that their project reaches its goals (see Figure 18). The hard work of making sure that the endangered knowledge of a public HBCU's history does not become extinct is rewarding and preserves an important part of the American past.
Phyllis Earles, university archivist at PVAMU, and Dr. Marco Robinson, assistant professor of history, PVAMU, at the Friends of the Texas Historical Commission Banquet, summer 2017. Courtesy of PVAMU's Special Collections and Archives Department.
1This film debuted on February 19, 2018 on Public Broadcast Station (PBS) in the US. The documentary chronicles the history of and circumstances surrounding the creation of historically black colleges and universities in the United States. The documentary is available online: http://www.pbs.org/independentlens/films/tell-them-we-are-rising/.
2The fifteenth Legislature of Texas established the Agricultural and Mechanical College of Texas on August 14, 1876.
3A special committee was formed consisting of J. H. Raymond, Dr. Ashbel Smith, and J. D. Giddings to find a site to the school for youth. They chose the Kirby Plantation in Waller County, which at the time had a majority black population.
4The first administrative leaders of PVAMU were given the title of principal because they were subordinates of Thomas Garthwright, president of A&M College; PVAMU was the satellite institution for blacks. Garthwright was also from Mississippi, which arguably was the connection between the two men.
5The history courses were Introduction to Historical Methods of Research (sophomore level) and the History Senior Capstone Research Course. The Introduction to Historical Methods course is open to all students and has no prerequisites. The two students who participated in the research symposium worked in the archives through funding provided by the Student Research Opportunities Program (SROP) (funded by the University).
The authors of this work attest to the accuracy and authenticity of the information used to compose this work. Further, the authors received no support from any organization for this submitted work and have no financial relationships with any organizations that might have an interest in financial gain from the submitted work.
Anderson, James. 1988. The Education of Blacks in the South, 1860–1935. Chapel Hill: University of North Carolina Press. DOI: https://doi.org/10.5149/uncp/9780807842218
Butin, Dan. 2006. "Introduction." In "Future Directions for Service Learning in Higher Education." Dan Butin (ed.), International Journal of Teaching and Learning in Higher Education, Special issue, 18(1): 1–4.
Cantey, Nia Imani, Robert Bland, LaKerri R. Mack, and Danielle Joy-Davis. 2013. "Historically Black Colleges and Universities: Sustaining a Culture of Excellence in the Twenty-First Century." Journal of African American Studies, 17(2): 142–53. DOI: https://doi.org/10.1007/s12111-011-9191-0
Nojeim, Michael, and Frank Jackson. 2011. Down that Road: A Pictorial History of Prairie View A&M University. Virginia Beach, VA: Donning Company Publishers.
Williams, Heather. 2005. Self-Taught: African American Education in Slavery and Freedom. Chapel Hill: University of North Carolina Press. DOI: https://doi.org/10.1111/j.1748-5959.2007.00120.x
Woolfolk, George. 1962. Prairie View: A Study in Public Conscience, 1878–1946. New York: Pageant Press.


Looking for a quick, easy, and delicious salsa? Well, look no further, because this fresh tomato salsa comes together so quickly and effortlessly. It's slightly tangy and so refreshing – perfect for snacking with tortilla chips or for finishing off a main course (taquitos, anyone?).
One of the best parts about this salsa is that it only requires 5 ingredients! Grape tomatoes are the main ingredient. No cutting required, simply place them in a food processor along with the other ingredients.
Cilantro is a key ingredient in this salsa. This year is the first year I've attempted growing some herbs out on my balcony, including cilantro. I have to say, it's going way better than I thought it would (I'm not always the best at keeping plants alive). I've had three herb plants for over a month now, and they are still going strong!
Fresh herbs are so delicious and add such great flavor to dishes – I love being able to just go out and trim off however much I need. Plus, whenever I buy a bunch of fresh herbs from the grocery store I feel like I am never able to use all of it up before they go bad – so this way is much less wasteful (and more frugal!). If you've never tried growing your own herbs I highly recommend it. I used some of my fresh cilantro in this salsa.
Also, I can't forget about the lime juice. It adds some acidity and tanginess. A bit of red onion adds brightness. A small amount of salt finishes off the salsa.
If you try this fresh tomato salsa I would love to hear about it! Please rate the recipe in the comments below and let me know how it went.
Mild salsa that only requires 5 ingredients. Refreshing and slightly tangy.
Pulse until combined. Pulse for a shorter time for chunky salsa and longer for smoother salsa.
Serve immediately or store in the refrigerator for up to 3-4 days.


Prix Canapé Roche Bobois. canap roche bobois r sonnance roche bobois. canap roche bobois bouix roche bobois roche bobois. 2 canap s roche bobois roche bobois. photos canap kenzo roche bobois prix. canap roche bobois mod le laura roche bobois roche bobois. 39 inspirant prix canape composable roche bobois greyhoundsaughall. canap roche bobois cuir beige courbevoie 92400. mah jong roche bobois roche bobois hans hopfer. canap roche bobois tissu kenzo roche bobois. roche bobois roche bobois. [Pontdechevalier.com].


When I was growing-up Halloween was a fun time to dress in costumes, go to the neighbors for "trick or treat," soap windows, bob for apples, and tell ghost stories. At the time, it all seemed so harmless; little did we know that we were actually celebrating Satan's holiday. Halloween was, and is, a subtle exposure to the occult. Today our schools are blatantly introducing cultic activities to our young people, including séances, New Age meditation, and witchcraft.
Recently McNear Elementary School in Petaluma City, California decided that their fourth-graders were going to celebrate the dead in a weeklong classroom ritual commemorating the Mexican holiday "El Dia de los Muertos" (Day of the Dead). The holiday will be observed as an alternative to Halloween. The children have been instructed to bring in pictures or write memories of deceased family members, friends or pets. These items will be put on an ofrenda (altar) that has been created for this purpose.
Other materials sent to parents and teachers describing the event refer to 'altars,' 'ritual.' 'symbolic items.' 'cycle of life,' 'dead animals,' and welcoming 'death.' The literature clearly indicated that the entire event is intended to be a practical application of these spiritual and religious themes. McNear principal, Clare Eckhardt, said, "the school is merely following state guidelines for curriculum. The question is, who is preparing this curriculum and what is the agenda behind it?
A few years back, "death education" was introduced as a game in the lower grade levels. Example: The children were asked if they were in a rowboat with family and friends and the boat was going to sink unless someone was thrown overboard, whom would they choose to let die. This is not only sick but it desensitizes the idea of "death!" What are they preparing our children for? Think about this! Islam (a three month extensive course) has been implemented in many schools and now "death" is part of our children's studies. Parents should be up in arms!
The Day of the Dead is described as a religious holiday, yet we are told that it is unconstitutional to celebrate Christmas and Easter in the schoolroom. The birth and resurrection of Jesus noted on our calendars and designated as official school holidays in this country have been replaced by "paganism" and "witchcraft." And, this Day of the Dead instruction is not just for one day -- it is an entire week of this intrusion into our children's fragile minds. No wonder our children can't read and they need calculators to add and divide. Teachers are too busy taking over the minds, bodies, and souls of their students to fit the "robotic new world" that is coming. Be aware that the Scriptures direct us not to dabble in divination, witchcraft, sorcery, or consult mediums who claim to chat with the dead.


text:
Entrails
Entrails World Inferno CD
01. World Inferno
02. Condemned to the Grave
03. Serial Murder (Death Squad)
04. The Soul Collector
05. Dead and Buried
06. Insane Slaughter
07. Into Eternal Fire
08. Suffer
09. The Hour of the Casket
10. The Blood Breed
Entrails formed in 1990-91. Their goal was to follow the scene that Stockholm brought to light around the time with bands like Dismember, Entombed, Grave and many more that cemented the brilliant Swedish death metal sound.
During 2016, Entrails appeared at prestigious festivals, such as Hellfest, Dokkem and Coast Rock Open Air. These shows gave the band the confidence they needed, and they started writing their next full-length. Lundqvist started work on a new album, and in the summer, they all started to learn and prepare for the recordings, which started in December 2016 in Lundqvist's Bloodshed Studio. Dan Swanö once again handled the mixing and mastering for the album, entitled 'World Inferno.'
Metal Blade Records Item #155182
Code: Entrails-Inferno-2017
Obliteration
The Soul Collector
Raging Death

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Using Ant tasks with Flash Builder 4 Can someone help me get started with using ant in Flash Builder 4. I have never used ant before so I'm a complete novice. I will need to know how to get it installed in the first place, and how to do the basics.
Any help is appreciated.

A: The fact that the article (linked to by both answers) is no longer available is a prime example of why SO answers should not be links alone.
Here are my experiences as of 02-2011 on Flash Builder 10.1:

*

*Select "Install New Software..." from the "Help" menu

*Enter "http://download.eclipse.org/releases/galileo/" 1

*After the list refreshes, expand "Programming Languages" 2

*Check "Eclipse Java Development Tools" 2

*Click "Next", "I Agree" etc

1 If you are running a later version of Flash Builder, this URL may be incorrect. If so, click "Available Software Sites" in the install software dialog and enable the closest looking URL (it should be similar to "http://download.eclipse.org/releases/galileo")
2 If either option is not available, try unchecking "Show only the latest version of available software"

A: I just read a good article on using Ant but it was for Flex Builder 3, from the Adobe Developer Connection.
I couldn't find any good ones detailing Ant use with Flash Builder 4, but here's one article explaining installation for Flash Builder 4.  You can probably use the build files from the Adobe Developer Connection article to get an understanding.

A: Since Flash Builder 4 is just Eclipse 3.5 (Galileo) with one of Adobe's plugins, you can use all the lessons learned from Eclipse:
http://help.eclipse.org/help33/index.jsp?topic=/org.eclipse.platform.doc.user/gettingStarted/qs-81_basics.htm
http://onjava.com/pub/a/onjava/excerpt/anttdg2_ch11/index.html?page=1

redpajama_set_name: RedPajamaStackExchange


I wanted to leave a fair review. I am extremely happy with this violin for the money I paid for it. It was on sale and so I feel I got a good deal. It takes a while for the strings to warm up but when they do it sounds alright (I'm around grade 2-3 level so I'm not awesome but I can get a good tone from the violin). Before I start listing off the flaws I noticed, I am going to list off the positives. I loved that this violin was from Europe, it's hard to find well made genuinely from Europe violins in this price range. The wood that it was made from was flamed at the back and you can clearly see the grain. I think this was either oil or varnish but it looks nicer than the pictures on the website in my opinion. I was very surprised with the quality of the violin when I opened the box and I was very happy with the finish. The bow is beautiful, it's the nicest beginner bow I have ever had the pleasure of using. It doesn't look cheap, it's solid and a good weight. It glides across the violin easily. I am very happy with this bow! Here are a few of the things that bothered me, as other people said in their reviews some of the varnish is not even. Areas of my violin on the scroll got missed when varnish was applied, there is one small dot on the body where the varnish left a small mark (this doesn't bother me too much as won't affect play-ability). In my very personal opinion had I the money to do so straight away I would change the strings to something like pirastros or dominants. This is a quality enough violin for you to get the benefit of a better string, the factory strings aren't the worst I've ever heard. But I will only be keeping them until they need a string change, they are a student quality, you can find nicer strings at an affordable range. The main thing stopping me give this outfit a 5 star is the case. I have ended up putting a piece of paper over the violin hair as I place it on top of the clip. Because you have to be so very careful placing the bow in the case or the hair gets snagged, I am lucky mine did not get damaged as I was careful. The music also does not fit in the music pocket as the pocket is too small. The zips snag on the case and eventually it's going to break.... I will buy a new case when that happens. It looks nice but it's not a practical quality case. It protects the violin well.
Beautiful looking and sounding violin for a decent price!
The instrument is bright and responsive. At times the instrument has nuances of coarseness but in general it is a nice instrument. The top of the violin really impressed me. The grain is fine and uniform which is sometimes hard to find. The corners and purfling are very well done. The set up is well done. Nice work done on the bridge, and sound post. The violin is easy to play. I was also impressed with how the peg box and pegs were fitted. This is a detail that can be overlooked easy especially when selling string instruments online. The pegs are perfectly fitted. The tail piece is excellent (wittner tail piece). I saw that one reviewer mentioned the chin rest was uncomfortable. Don't trust this person he's crazy. The chin rest is fine and I have used it for hours. The bow it came with is also very nice. It's responsive, stable and resonant but can lack clarity during fast passages. The quality of the horse hair on the bow is also decent. Over all the bow is well cut and nice to look at. The case is nicer than I expected. It's it's styrofoam construction is extremely light. The handle looks and feels durable. This case even has a suspension for the violin which is a nice inclusion in this price range. The zippers on case however fails to impress. They are slightly difficult to open and close and I feel like after a year or two they will surely break. No biggie tho in my opinion. Things I didn't like about the violin... On the sides/corners of the violin the varnish is a little uneven. It also appears that a different, more glossy varnish was used for the scroll instead of the oil varnish used on the front, back and sides. Using two different varnishes makes it look a little cheap in my opinion. I don't think many people will care or see this and the beautiful quality of the grain of the wood makes up for this slight idiosyncrasy. The violin was put together well but there are slight areas of hide glue build up on the seems. Unfortunately when my violin was shipped someone had left a piece of paper towel glued underneath the label of the violin. I have a difficult time understanding how someone at quality control missed a giant piece of trash inside the violin. Were they asleep? Anyway I was able to take it out with tweezers after about 5 minutes of shaking my violin. You must put on new strings as soon as you receive your violin. The strings it comes with will do if you have nothing else but the quality of the strings is bad. I do not expect Shar to put on $100 violin strings, so this is fine. I recommend vision or dominant strings for $50/$60. Nice strings are very important. If you have a nice instrument and bad strings, it's like putting a Prius engine in the body of a Lamborghini... Nobody would do that. You shouldn't either. Over all this is a great instrument. If the quality is anywhere near mine you should be happy. Of course you should limit your expectations as this is a affordable violin but it is really of good quality. The quality of this violin should be THE standard of any student beginning the violin. It is the minimum investment needed to be able to achieve good results. This violin should last a student 2 to 3 years before they need to upgrade to a more complex sound.
On The violion you can see where someone made one for a stroke with the wrong color of varnish on this side you can see the whole side has been rubbing on something and is missing all of the Varney the chin rest is the worst of the worst it is so hard and uncomfortable to use my girlfriend was unable to return it do too she needed a violin right now and could not. Want for another. Now we have to live with all of these defects.
Hello! Is it possible to purchase this with a different set of strings? I'm looking at the Thomastik Dominant Violin Set-Steel-Ball E-1/2-Med (Item #: D129S 12). If yes, how much would this outfit cost? Thanks!
Hello! This instrument comes with D'Addario Prelude strings already installed. You are welcome to purchase the Dominant strings separately. Please feel free to contact us at 1-800-248-7427 if you have additional questions.


text:
Posted on January 14th, 2010 at 2:40 pm by Henry Herskovitz
Our First Offer Received
Beth Israel Congregant Victor Lieberman stepped up to the plate in his own fashion and sent Jewish Witnesses for Peace and Friends an invitation to consider his offer to approach Rabbi Dobrusin and the Board of Directors of Beth Israel. His full offering can be read here, but at the core was:
In short, there may be some opportunity for accommodation. I understand that you began your vigil because you were denied opportunity to speak to Beth Israel. I therefore propose the following. I shall urge the rabbi and board to allow you to share your views on Israel in a meeting room at the synagogue on a week night or at a weekend time of your choosing. (Addressing the congregation in the sanctuary on Shabbat is not an option, because it is not a political forum.) Your talk will be advertised, and anyone in the congregation (not the general public) wishing to attend will do so. People will not accept a one-sided presentation on so controversial a topic, so I propose that after you talk for 20 minutes or however long you want — I reply for a like period, or perhaps somewhat more briefly. We could then each have 5 or 10 minutes for final remarks, after which the discussion would be open to the floor. In return for addressing the congregation, you and your group must agree to end permanently your picketing of the synagogue.
Our response, and counter-offer, in full:
Dear Mr. Lieberman,
Jewish Witnesses for Peace and Friends appreciates your decision to reach out to us. As you may know, JWPF made offers to Beth Israel in letters to the Ann Arbor News on July 18, 2007, and July 17, 2009 (see enclosures). Our basic position was to "terminate the vigils at Beth Israel when its board of directors publicly states its full support for principles that basic human rights require." In essence, those principles were the three demands set forth in the 2005 Palestinian call for boycotts, divestment, and sanctions against Israel. In both cases, we indicated that our position was negotiable; that is still the case today.
However, you should know that JWPF members agree that the one-time, closed meeting you proposed in your earlier letter is simply inadequate inducement for us to abandon an effort that has consumed so much of our time and energy and has occasioned so much unwarranted reproach from the Jewish community. Furthermore, your proposal offers nothing in the way of changing BIC's unequivocal embrace of Zionism and the Palestinian suffering that entails. Nevertheless, we remain open to beginning negotiations with BIC with the definite goal of ending our vigils at BIC.
During these negotiations, JWPF reserves the right to continue the vigils but to jump start the process, and as an act of good faith, we offer to suspend the vigils for one week on some mutually agreed upon date in return for a pledge by BIC's board of directors to begin negotiations with us on a date certain. Feel free to share this letter with Rabbi Dobrusin and anyone else in BIC.
After receiving an initial response, which hinted at the allegedly circumscribed nature of our understanding of Apartheid Israel's historiography, Mr. Lieberman who holds a 1975 Ph. D from the University of London's School of Oriental and African Studies invited this writer to audit his Fall, 2010 class on "A History of the Arab-Israeli Conflict." This part of a new offer was immediately accepted, but his rejection of our counter came in a second response: "As regards your counter-offer, I'm afraid it would elicit no interest. Most congregants regard the vigil as no more than a trivial nuisance with which they long ago came to terms."
Victor is then hard pressed to explain, if JWPF is the trivial nuisance he claims, why the Jewish community would recognize our group in a front-page, 3,500-word article in its most recent edition of the Washtenaw Jewish News.
Ann Arbor Chronicle Reports on Palestine
One of at least two on-line news agencies remaining in Ann Arbor is the Ann Arbor Chronicle. Their co-editor is present at all City Council meetings and reports from last Monday:
Henry Herskovitz: Herskovitz described how he'd been walking with a woman down Ann Street towards Fourth Avenue on Nov. 21, 2009 when theyd been assaulted by a noise so loud that it had caused the woman to grab his arm. It had caused parents to try to reassure their children that things were okay, but the children had been inconsolable. The loud noise, he explained, had come from Michigan National Guard jets that had buzzed Michigan Stadium on the day of the UM-Ohio State football game. He reminded councilmembers that the children of Palestine experience that kind of noise on a daily basis, and that it was causing a psychological crisis in Gaza. That terror, he concluded, continued to be funded by American citizens.
http://annarborchronicle.com/2010/01/06/mixed-message-from-council-on-library-lot/
Good Protest in Ohio
At least two members of JWPF journeyed south to Toledo, Ohio to take part in a Palestine solidarity gathering and march sponsored by the Northwest Ohio Peace Coalition (NWOPC). In his speech, a new Palestinian father called upon the Jewish community to reject the ideology and subsequent crimes committed in their names. See photo below and at http://www-personal.umich.edu/~hersko/Photos/CIMG1453.JPG. Full report of NWOPC's protest here.
Six Vigillers, ten degrees
Treat the Illness, not the Symptom
One Response to "Report on Beth Israel vigil 01-02-10"
The Deir Yassin Remembered Blog » Blog Archive » Report on Beth Israel vigil 01-30-2010
February 7th, 2010 - 4:47 pm
[…] a trivial nuisance with which they long ago came to terms." This quote was contained in our "Report on Beth Israel vigil 01-02-10". Vic also added in that email exchange: "…the congregation will simply continue to put up […]


text:
\section{Introduction}
Stress conditions in plants are usually divided into two categories - biotic, induced by biological factors such as fungi or bacteria, and abiotic, caused by climatic conditions such as heat or drought.
In this paper we deal with prediction of abiotic stress, by designing an experiment for creating and analysing four separate treatments of water and fertilizer conditions for banana plantlets in a greenhouse.
The specific species is known as {\em Musa acuminata}, considered one of the earliest domesticated plants \cite{denham2003origins,perrier2011multidisciplinary,lebot1999biomolecular,de2009bananas,li2013origins}, cultivated and hybridized extensively in recent centuries for mass production of human edible clones \cite{sheperd1999cytogenetics,khayat2011genetics,fuller2009banana}.
Banana is the fourth largest fruit crop in the world, considered a staple food in many countries \cite{surendar2013water}.
However, one of the major limitations to it's productivity is water stress.
Sensitivity to reduction in soil moisture is reflected in dwindled growth due to reduced stomatal conductance and leaf size which eventually may lead to plant death \cite{kallarackal1990water}.

The experiment was aimed at analysis of banana plantlets undergoing four levels of water stress, intended for detection and early prediction of drought conditions.
The plantlets were grown in a greenhouse in northern Israel by Rahan-Meristem \cite{rahan2020website}.
Each treatment category included $30$ plants, followed daily for $17$ consecutive days, and photographed once daily with a high resolution RGB camera and a low resolution thermal (infra-red) sensor.
All plants were cloned from the same original, and the experiment was conducted during September $2018$.
All images were manually annotated with plant contours.
Image backgrounds were deliberately discarded, since they contain obstructive objects such as irrigation equipment, tagging labels, ground and cement.

It should be noted that neither laymen nor experts can notice any difference of treatment in the images, and hardly from a sequence of images.
The single cue from the agricultural growers is that the appearance rate of the newest leaf in the plant is lower as the plant gets a decreased amount of water.
In general, the RGB images display no visible difference, except appearance of new leaves once every $4$ to $6$ days.
The average temperature of the plants, extracted from the thermal images, does show a noticeable range variation, about $5.0^\circ$ between the extreme conditions.

Our main contributions include
(1) creation of a RGB-T dataset in a controlled environment, planned specifically for analysis of plant structure and development,
(2) proposing and comparing several methods for stress detection and prediction,
(3) exploiting temporal image sequences to leverage the accuracy rates, and
(4) employing shallow neural networks capable of feature extraction relevant to the issue at hand.
The following sections present related efforts in the field, description of the experiment and data acquisition, methods and strategies employed, results and conclusions.

\section{Related Efforts}
\textbf{RGB and thermal images.}
Several papers document the combined usage of RGB images, capturing light in the human visible range, and thermal images captured by infra-red (IR) or near infra-red (NIR) sensors.
Naturally, papers dealing with autonomous driving, motion tracking, and human pose estimation attract much attention in the computer vision community, hence the existence of RGB-T datasets in these fields is more common \cite{li2016learning,li2019rgb,shivakumar2019pst900,tang2019rgbt,palmero2016multi}.
In the agricultural domain, much research often rely on Unmanned Aerial Vehicles (UAV) and satellites collecting IR and NIR data for terrain mapping \cite{milella2017sensing,gago2015uavs,mulder2011use}, vegetation indexing of crop fields \cite{berni2009remote,genxu2012variability,bellvert2014mapping,raeva2019monitoring}, and canopy chlorophyll content \cite{delloye2018retrieval,gevaert2015generation,simic2018importance}.
Surveys related to UAV imagery all point to the importance of collecting and analyzing thermal and hyper spectral data for agricultural enterprises \cite{aasen2018quantitative,adao2017hyperspectral,abdullahi2015technology,barbedo2019review,kim2019unmanned,maes2019perspectives,mogili2018review}.
Simultaneous acquisition from multi-modal sensor systems is also indispensable for diagnosing plant stress at short-range \cite{chaerle2009multi,humplik2015automated}.

Thermal images are used in various agricultural applications - plant monitoring, yield estimation, crop maturity evaluation, irrigation scheduling, soil salinity detection, disease and pathogen detection,  and bruise detection \cite{ishimwe2014applications,khanal2017overview}.
Recently, several new RGB-T datasets were collected, specifically intended for applying computer vision methods to handle agricultural tasks, for example semantic segmentation of field patterns \cite{chiu2020agriculture} and estimation of weed growth stages \cite{etienne2019machine}.
Multi-modal data (LIDAR, radar, RGB, and thermal) was collected and studied for the purpose of obstacle avoidance in agricultural fields \cite{kragh2016multi,korthals2018multi}.
Other examples relying on thermal imagery include detection of tea disease \cite{yang2019tea}, detection of apple scab disease \cite{nouri2018near}, irrigation monitoring \cite{roopaei2017cloud}, and estimation of leaf water potential \cite{cohen2015crop}.

\noindent \textbf{Neural networks for plant phenotyping and stress prediction.}
Research in recent years has produced an abundance of practical implementations applying Convolutional Neural Networks (CNN) for many vision based tasks in the advancing field of precision farming and automated agriculture, usually fine-tuned for specific goals related to detection of crop traits and early stress prediction.
CNNs are by far the most popular computational tool for feature extraction relevant to crop phenotypes.
To list just a few CNN based examples, RGB-T is used for wheat ear detection \cite{grbovic2019wheat}, RGB and depth sensors for object detection intended for measurement of width and height of banana leaves, and height of banana trees \cite{vit2019length}, salt stress due to soil salinity using hyperspectral reflectance \cite{feng2020hyperspectral}, and leaf area, count, and genotype classification \cite{dobrescu2020doing}.
The importance of leaf segmentation for practically every plant phenotyping task is frequently mentioned \cite{scharr2016leaf,tsaftaris2016machine,ubbens2017deep}, leading to various tailored augmentation methods for accurate leaf segmentation, especially of Arabidopsis-thaliana \cite{kuznichov2019data,sapoukhina2019data,ward2018deep,zhu2018data}.
Reflecting on current trends, phenotyping capabilities are already upgraded by research into the structure and optimization of neural networks.
A discussion on reducing network size for flower segmentation is presented in \cite{atanbori2018towards}.
Lately, Dobrescu et al. presented a deep attempt to understand how CNNs reach decisions in leaf counting schemes \cite{dobrescu2019understanding}.
Future studies will most likely continue to adapt neural networks, acquire larger amounts of multi-modal data, and aim at further automation of data collection and analysis.

\section{Experiment Outline and Data Acquisition}
\textbf{A stress detection venture.}
The experiment was aimed at analysis of banana plantlets undergoing four levels of water treatment, and planned specifically for detection and prediction of drought conditions.
One-month old plantlets were grown in a $1L$ pot in a commercial greenhouse.
The plantlets were positioned according to the scheme in Figure \ref{fig:experiment layout}.
The four different treatments were labeled A,B,C,D for convenience, and each plant tagged with an ID, from $01$ to $30$.
During the first three days all plants received the same treatment; different water and fertilizer amounts were administered starting on the fourth day.
The plants in treatment A were watered and fertilized every day according to the normal commercial growing conditions ($100\%$), and plants undergoing treatments B, C and D were watered every day with lower amounts, $80\%$, $60\%$ and $40\%$, respectively, of the normal conditions.
On the $17^{th}$ and last day of the experiment, irrigation of all plants returned to normal conditions, to support their recovery and verify that they are in a productive state for further usage.
Data collection time was between $07{:}00$-$10{:}00$, and watering of the plants at $13{:}00$.

\noindent \textbf{Data collection.}
The RGB camera in use was a mobile Samsung SM-G930F, with pixel resolution $4032\times3024$, focal length $4mm$, and exposure time ${\sim}3.6ms$.
The IR sensor was a Opgal-ThermApp, pixel resolution $384\times288$, focal length $35mm$, and wavelength region $7.5 -14{\mu}m$ \cite{opgal2018camera}, outputting $16$ bits per pixel, each value an integer representing $100{\times^\circ}C$.
All images are top views, taken approximately $1.2m$ from the top of plants.
The temperature inside the greenhouse was measured once daily at a fixed position, being between $34$-$39{^\circ}C$.
In addition, a log was kept, recording dates of leaf buddings in all plants.
These manual measurements are displayed in Figure \ref{fig:manual measurements}.
Observing the leaf buddings turned out to be crucial; as will be explained later, our findings suggest a strong correlation between the rate of new leaf appearance and our stress prediction.

\begin{figure}[htbp]
\centering
\includegraphics[height=8.5cm]{zz_greenhouse.jpg}
\caption{Schematic representation of the experiment layout (top) and aerial view inside the greenhouse (bottom).}
\label{fig:experiment layout}
\end{figure}

\begin{figure}[htbp]
\centering
\includegraphics[height=3.2cm]{zz_leaf_growth_rate.jpg}
\includegraphics[height=3.2cm]{zz_new_leaf_count.jpg}
\includegraphics[height=3.2cm]{zz_average_daily_temperature.jpg}
\caption{Manual measurements during the experiment: Number of days between emergence of new leaves (left), count of new leaves during the entire experiment (middle), mean and standard deviation for each treatment, and measured temperature inside the greenhouse (right).}
\label{fig:manual measurements}
\end{figure}

\section{Methods and Strategies}

\noindent \textbf{Pre-processing.}
All images were manually annotated with plant contours.
A custom built annotation tool was used, and contour points were marked on all images.
Image backgrounds were deliberately discarded, since they contain obstructive objects such as irrigation equipment, iron support rails, tagging labels, ground and cement.
Figure \ref{fig:raw samples} shows a sample  of raw input images, RGB and thermal, one from each treatment category, taken at the eighth day of the experiment.
Furthermore, the thermal images were passed through an erosion phase in order to discard any parts of the background that remained after the segmentation.
A similar erosion in the RGB images was not deemed necessary, simply due to finer contour annotations, performed on the original high resolution images.

\begin{figure}[htbp]
\centering
\includegraphics[height=2.2cm]{zz_RGB_A.jpg}
\includegraphics[height=2.2cm]{zz_RGB_B.jpg}
\includegraphics[height=2.2cm]{zz_RGB_C.jpg}
\includegraphics[height=2.2cm]{zz_RGB_D.jpg}
\includegraphics[height=2.2cm]{zz_RGB_A_seg.jpg}
\includegraphics[height=2.2cm]{zz_RGB_B_seg.jpg}
\includegraphics[height=2.2cm]{zz_RGB_C_seg.jpg}
\includegraphics[height=2.2cm]{zz_RGB_D_seg.jpg}
\includegraphics[height=2.2cm]{zz_T_A.jpg}
\includegraphics[height=2.2cm]{zz_T_B.jpg}
\includegraphics[height=2.2cm]{zz_T_C.jpg}
\includegraphics[height=2.2cm]{zz_T_D.jpg}
\includegraphics[height=2.2cm]{zz_T_A_seg.jpg}
\includegraphics[height=2.2cm]{zz_T_B_seg.jpg}
\includegraphics[height=2.2cm]{zz_T_C_seg.jpg}
\includegraphics[height=2.2cm]{zz_T_D_seg.jpg}
\caption{Sample of original and segmented RGB images (top rows), original and segmented thermal images (bottom rows), from the eighth day of the experiment; A,B,C,D treatments (left to right), respectively.
}
\label{fig:raw samples}
\end{figure}

Thermal analysis commenced with measuring average temperatures on the plants, then comparing them with the average temperature of the plant contours.
Several neural network architectures were then employed to use RGB and thermal images, separately, then combining both modalities.
The main boost to accuracy rates was obtained by inspecting consecutive images of the same plant from several days, three or more.

Of the $30$ plants from each category, $26$ were used for training, the remaining used solely for testing.
Plants with ID's $05,10,15,20$ served for test purposes, and in no way were seen by the training algorithms, neither RGB nor thermal.
This separation of train/test was consistent throughout all the methods and prediction strategies.

\noindent \textbf{RGB data.}
Several network architectures were attempted, among them Resnet-50 \cite{he2016deep}, GoogLeNet \cite{szegedy2015going}, MobileNet \cite{howard2017mobilenets}, and VGG16 \cite{simonyan2014very}, or compacted versions of them, all widely used in classification problems.
A Cifar-10 \cite{keras2020cifar10} based CNN architecture, trained from scratch, proved best.
The deep pre-trained models resulted in performance far worse than training from scratch.
This leads to the conjecture that general-purpose datasets are not well suited, in a transfer learning sense, to particular agricultural data.
Our dataset is fairly small, so in order to reduce over-fitting, we used Keras built-in augmentation in our training process.
The augmentations we used include horizontal and vertical flips, width and height shifts, and rotations.

\noindent \textbf{Thermal data.}
Similar to the RGB data, we first tried to fine-tune pre-built models like the GoogLeNet and Resnet-50 architectures, but here too, the results were far inferior to results from models we trained from scratch.
We used a fairly similar model for this data compared to the RGB data model, but changed the order of convolutions because it proved to produce slightly better results.
Augmentations were applied during this learning process as well, horizontal and vertical flips, width and height shifts, and rotations.

Next, we tried training the same model with image sequences from $3$ consecutive days.
Each sequence of $3$ images was concatenated and fed to our model as a single $288\times 1152$ pixel image to which we refer to as a {\em triplet}.
We define \(triplet_n\) as constructed from images taken at days $n$-$2$, $n$-$1$, and $n$.
If $n$-$1$\(<\)$1$ or $n$-$2$\(<\)$1$ we simply duplicate the same image, so overall, we have the same amount of triplets as single images.
An example of such a triplet is shown in Figure \ref{fig:triplet}.
According to our field observations, there is a strong correlation between the plant's drought stress and the rate in which new leaves appear.
We believe that by using these triplets, our machine could deduce temporal features like leaf growth and size.

\begin{figure}[htbp]
\centering
\includegraphics[height=6.5cm]{zz_triplets.jpg}
\caption{Concatenation of images from $3$ consecutive days (treatment A, plant ID 14, days $13$-$15$). Notice the appearance of a new leaf bud at day {\it n-2} (a small cigar-looking leaf), its rapid growth between days {\it n-2} and {\it n-1}, and its final opening on day {\it n}.}.
\label{fig:triplet}
\end{figure}

\noindent \textbf{Combination of RGB and thermal data.}
Because the thermal and RGB images are not aligned and have different resolutions, a model which trains on both types of images simultaneously may not perform well.
For that reason, a unique model was trained for each type of images separately.

\noindent \textbf{Prediction model.}
To classify a plant $x$, we pass $N$ consecutive thermal images (one for each day) through the thermal data model, and the corresponding RGB images through the RGB data model.
For every image prediction, we save $P$ labels that match the thermal data model prediction, and $Q$ labels that match the RGB data model prediction, where \(\frac{P}{Q}\) is the ratio between the first model accuracy and the second model accuracy. For instance, if the thermal model's accuracy is $60$\% and the RGB model's accuracy is $90$\% then \(\frac{P}{Q} = \frac{60}{90} = \frac{2}{3} \rightarrow P = 2, Q = 3\).

To get the final prediction, we use a variation of the rolling average prediction method, meaning that our final prediction will be the highest probability (the one which appears the most times).
In a formal manner, the prediction of plant $x$ is obtained as follows.
For each RGB image, we predict the label by passing it to the RGB model.
We then duplicate the prediction $Q$ times,
\begin{eqnarray}
\mbox{pred}_{\mbox{rgb}}^* (RI_n) &=& (\mbox{pred}_{\mbox{rgb}} (RI_n),\cdots,\mbox{pred}_{\mbox{rgb}} (RI_n)) \in\mathbb{N}^Q, \;
\end{eqnarray}
where $RI_n$ is the RGB image of plant $x$ at day $n$.
Similarly, each thermal image is fed to the thermal model to obtain the prediction.
We then duplicate the prediction $P$ times,
\begin{eqnarray}
\mbox{pred}_{th}^* (TI_n)&=&(\mbox{pred}_{th} (TI_n),\cdots,\mbox{pred}_{th} (TI_n)) \in \mathbb{N}^P, \;
\end{eqnarray}
where $TI_n$ is the thermal image of plant $x$ at day $n$.
The combined prediction of this image at day $n$ is a concatenation of both label vectors
\begin{eqnarray}
\mbox{pred}^* (n)&=&\mbox{pred}_{th}^* (TI_n) \mathbin\Vert \mbox{pred}_{\mbox{rgb}}^* (RI_n) \in \mathbb{N}^{(P+Q)}, \;
\end{eqnarray}
where $\mathbin\Vert$ is the concatenation operator.
Then, we combine all prediction vectors from all $N$ images of the sequence to form a $1{\times}{N(P+Q)}$ buffer of labels,
\begin{eqnarray}
\mbox{buffer}&=&
 \mbox{pred}^* (1) \mathbin\Vert \mbox{pred}^* (2) \mathbin\Vert \cdots \mathbin\Vert \mbox{pred}^* (N) \in \mathbb{N}^{N(P+Q)}. \;
\end{eqnarray}
For each possible label we sum the number of times it appears in the vector,
\begin{eqnarray}
C(l)&=&\sum_{l^\prime \in \mbox{buffer}} I\{l^\prime = l \}, \;
\end{eqnarray}
where $l^\prime$ is the predicted label and $l$ an element in the set of $4$ treatment labels.
We choose as final prediction the label with the most occurrences,
\begin{eqnarray}
\mbox{pred}(x)&=& \mbox{argmax}_{l\in \mbox{labels}} (C(l)).
\end{eqnarray}

We also tried to apply this prediction method on each model separately and save only one label per prediction in the buffer.
This simple method enhanced the prediction accuracy on both models as can be seen in the results section.
Since the available data has a temporal quality, a Recurrent Neural Network (RNN) approach was also attempted but produced less satisfactory results.

\section{Results}
\textbf{Preliminary analysis.}
Thermal analysis commenced with extraction of average temperatures on the plants compared with the average temperature of the plant contours.
To this end, the contour temperature is defined as a narrow band of pixels between the original and slightly eroded image mask.
Prediction accuracy by average plant temperature only, and difference between plant and contour temperature, resulted in ${\sim}50\%$ and ${\sim}60\%$, respectively, amongst the four categories.
In this preliminary analysis, a simple prediction scheme was employed:
First, plant temperatures where obtained per treatment and per day from each thermal image, then averaged for each category.
Next, the predicted label of each image from the test set was defined as the treatment label (amongst A,B,C, and D) with average temperature closest to the test image.
The prediction is considered true if the predicted label is the same as the ground truth label, and false otherwise.
Finally, average accuracies were computed for each day and each treatment category.
Average plant temperatures and prediction accuracy at this stage are presented in Figure \ref{fig:basic results}.
Subtraction of the temperature measured once daily inside the greenhouse affected these results only slightly.
A note should be made that on the $17^{th}$ and last day of the experiment, irrigation of all plants was returned to normal conditions for all treatments, and the measured temperature on this day was significantly high (see Figure \ref{fig:manual measurements}), so these facts may explain the convergence of average temperatures on this day, as seen in Figure \ref{fig:basic results} (left).
The temperature drop of treatment D on the $13^{th}$ day may have been caused by a fault in the irrigation equipment.
This step already demonstrates that classification amongst the treatments can be achieved, to some degree, correlating the well known fact that plants receiving less water have a higher temperature.
By using images from three consecutive days, the prediction accuracy was improved to ${\sim}70\%$.

\begin{figure}[htbp]
\centering
\includegraphics[height=4.5cm]{zz_avg_temperatures.jpg}
\includegraphics[height=4.5cm]{zz_prediction_by_temperatures.jpg}
\caption{Average daily plant temperatures for each treatment (left), and prediction accuracy rates obtained by plant and contour temperatures (right).}
\label{fig:basic results}
\end{figure}

These results, moderately satisfactory, motivated us to apply deeper machine learning models, and integrate the temporal sequencing.
The basic scheme is a network architecture composed of two convolution and max-pooling layers, followed by two fully connected layers, supplemented by $0.5$ dropout, with $32$ feature maps in the first convolution layer, and $64$ in the second.
The input is the original thermal images {\em as-is}, the RGB down-scaled to the same $384\times288$ resolution.
The RGB and thermal network architectures are presented in Figure \ref{fig:network architectures}.
Again, implementation experience demonstrated that shallow networks trained from scratch performed better than deep pre-trained networks.

\begin{figure}[htbp]
\centering
\includegraphics[height=4.0cm]{zz_RGB_model.jpg}
\includegraphics[height=3.0cm]{zz_thermal_model.jpg}
\caption{The RGB (top) and thermal (bottom) architecture models; visualizations created by Keras visualising tool.}
\label{fig:network architectures}
\end{figure}

\noindent \textbf{Prediction by RGB images only.}
In this experiment we used a batch size of $24$ and trained for $100$ epochs using an ADAM optimizer.
As seen in table \ref{table:treatment prediction separate}, augmentation improved the results significantly, mainly because the dataset is rather small.
Also, using a sequence of $3$ images from consecutive days achieved slightly better results compared to a single image.
One should not be surprised that the deep pre-trained models result in poor prediction, since they were explicitly trained to distinguish among semantic categories such as dogs, birds, cars, airplanes, etc., possibly different plant sub-species, but not amongst similar looking plant images.
That said, the ${\sim}25\%$ prediction accuracy is even expected a-priory.
In contrast, with limited amount of raw data, a shallow network accompanied with proper augmentation is expected to learn prominent features beneficial to the specific prediction task at hand, while deep training from scratch is an overkill.

\begin{table}[htbp]
\begin{center}
\caption{Treatment prediction accuracy using RGB and thermal images, separately; comparison of network architectures.}
\label{table:treatment prediction separate}
\begin{tabular}{ |p{4cm}||p{1.6cm}|p{1.6cm}|p{1.6cm}|p{1.6cm}|  }
\hline
 & GoogLeNet RGB & MobileNet RGB & Our model RGB & Our model Thermal\\ \hline
  Without augmentation & 25 & 36 & 72 & 40\\ \hline
  With augmentation & - & - & 82 & 62.5\\ \hline
  Triplets with augmentation & 25 & 32 & {\bf84} & {\bf72}\\ \hline
\end{tabular
\end{center}
\end{table}

\noindent \textbf{Prediction by thermal images only.}
In this experiment we used a batch size of $32$ and trained for $1000$ epochs using a SGD optimizer.
Here too, augmentation improved the results significantly and applying a sequence of $3$ images from consecutive days achieved better results than using a single image by a considerable margin.
However, results are far inferior to prediction by RGB images only.
We believe there is more than one reason for that.
Firstly, the thermal data is not very consistent; there were variations in the daily measured temperature inside the greenhouse, and the manufacturer specifies a ${\pm 3.0{^\circ}C}$ accuracy at $25{^\circ}C$ \cite{opgal2018camera}.
These inconsistencies can be partially seen in Figure \ref{fig:basic results}.
Secondly, the visual information gathered from a plant may have more discriminative information than its temperature.
RGB contains $3$ channels, has much higher resolution, and also, today, low cost RGB cameras capture high quality info, while {\em quality-vs-cost} IR is still lagging behind.
For example it is easier to isolate each leaf in a RGB image than in its thermal image.
Due to these mentioned reasons, as well as less practical experience on learning from thermal images, the thermal network was trained for more epochs than the RGB network.
We found that $400$ epochs proved enough for converging on an asymptotic accuracy rate, and kept the process running in order to observe stable results before overfitting creeps in.

\noindent \textbf{Integration of RGB and thermal data.}
In this experiment we integrated the models that performed best on their respective thermal and RGB data.
The entire architecture is depicted in Figure \ref{fig:thermal rgb architecture}.
We tried to combine the triplet models with the single images models and check which combination produces the best results.
We also experimented with the sequence length $N$ to see how early it is possible to predict abiotic stress with a fairly good accuracy.
Table \ref{table:treatment prediction rolling average} shows the results of these experiments.
Each row represents a single or combined model, and each column represents the number of consecutive images fed to the models.
For example, column $8$ shows the mean prediction accuracy of the rolling average method over every sequence of $8$ consecutive images of all plants.
Figure \ref{fig:prediction by sequence length} is a graph visualization of prediction accuracy per treatment, obtained from one of the models, corresponding to the single RGB + single thermal line in table \ref{table:treatment prediction rolling average}.
Among all scenarios, best results were obtained by the single RGB + triplet thermal model.

\begin{figure}[htbp]
\centering
\includegraphics[height=3.2cm]{zz_final_architecture.jpg}
\caption{The complete architecture. $x^P$ and $x^Q$ are $P$ and $Q$ prediction labels (thermal and RGB, respectively), possibly different for the same image, because of the dropout layer. These are concatenated into the label vector, which is {\em arg-maxed} to obtain the final prediction.}
\label{fig:thermal rgb architecture}
\end{figure}

\begin{table}[htbp]
\tiny
\begin{center}
\caption{Accuracy of treatment prediction by rolling average, comparison of methods and sequence length.}
\label{table:treatment prediction rolling average}
\begin{tabular}{ |p{1.8cm}||p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|p{5mm}|}
 \hline
 \multicolumn{18}{|c|}{RGB and thermal rolling average prediction accuracy} \\
 \hline
 Sequence length N& 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17\\
 \hline
  Single RGB & 80.7 & 82.8 & 81.6 & 80.2 & 80.6 & 82.3 & 83.6 & 86 & 85.8 & 88.3 & 88.4 & 91.1 & 92 & 93.6 & 93.5 & 93.3 & 93.3\\
 \hline
 Single thermal & 69.4 & 71.1 & 67.7 & 65.7 & 68.6 & 72.5 & 78 & 81.8 & 82.6 & 83.8 & 87.3 & 83.5 & 87.3 & 85.1 & 87 & 86.6 & 80\\
 \hline
 RGB triplets & 78 & 78.2 & 80.2 & 80.6 & 81.6 & 82.8 & 83.6 & 84.6 & 85 & 86.5 & 87.3 & 87.3 & 87.3 & 87.2 & 87 & 86.6 & 86.6\\
 \hline
 Thermal triplets & 75.3 & 76.5 & 79.8 & 80.2 & 80.1 & 80.5 & 80.5 & 81.1 & 84.2 & 85.5 & 87.3 & 87.3 & 92 & 93.6 & 96.7 & 100 & 100\\
 \hline
 Single RGB + single thermal & 84.3 & 84.9 & 84.7 & 84.5 & 85.8 & 88.5 & 88.6 & 92.3 & 92.9 & 91.9 & 92.6 & 93.6 & 93.6 & 93.6 & 93.5 & 93.3 & 93.3\\
 \hline
 Triplet RGB + single thermal & 78.9 & 79.6 & 80.3 & 82 & 82.6 & 83.2 & 84 & 84.9 & 85.3 & 86.6 & 87.4 & 87.3 & 87.3 & 87.3 & 87.2 & 87 & 86.6\\
 \hline
 {\bf Single RGB + triplet thermal} & {\bf89} & {\bf89.5} & {\bf91.9} & {\bf91.8} & {\bf91} & {\bf91.4} & {\bf91.2} & {\bf92.3} & {\bf92.9} & {\bf94.6} & {\bf94.7} & {\bf100} & {\bf100} & {\bf100} & {\bf100} & {\bf100} & {\bf100}\\
 \hline
 Triplet RGB + triplet thermal & 78.8 & 80.3 & 82.5 & 84 & 84.8 & 85.7 & 86.1 & 87.4 & 87.4 & 87.4 & 87.3 & 87.3 & 87.3 & 87.2 & 87.1 & 86.6 & 86.6\\
\hline
\end{tabular
\end{center}
\end{table}

\begin{figure}[htbp]
\centering
\includegraphics[height=5.0cm]{zz_prediction_by_sequence.jpg}
\caption{Prediction accuracy by sequence length of the single RGB + single thermal framework.}
\label{fig:prediction by sequence length}
\end{figure}

\noindent \textbf{Discussion.}
One may wonder about the seemingly {\em too-good-to-be-true} results presented in Table \ref{table:treatment prediction rolling average}, especially columns $12$-$17$ for the single RGB + triplet thermal architecture, and some explanation must be provided.
It is important to notice that the whole test set is fairly small, with only $16$ plants, $4$ from each of the $4$ categories (although the entire test set consists of $272$ thermal and $272$ RGB images, $4$ plants, $4$ treatments, $17$ days).
Furthermore, as mentioned in the preliminary analysis, the na\"ive {\em average-per-treatment} prediction already reaches roughly $50\%$ accuracy, as opposed to random classification of $25\%$, amongst $4$ classes.
Another thing worth mentioning is that the models were trained on all images from all days (except the test images).
This means that the network can already be equipped to use this information for prediction while in some realistic situations we would not be able to train on {\em future} images.
As mentioned earlier, the {\em new-leaf-appearance-rate} is certainly highly correlated with plant stress.
The triplets help capture this dependency after training the thermal and RGB models.
In addition, the input to the complete architecture, which is a sequence of $N$ images from consecutive days, further increases its ability to detect high {\em new-leaf-appearance-rates}.
In some cases, as seen in table \ref{table:treatment prediction rolling average}, the {\it single-image} scheme outperformed the {\it triplets}.
We believe the reason for this is an inherent restriction on augmentations in the triplet arrangement; although a convolution based architecture is expected to be invariant to flips and rotations, the very arrangement of the triplets defies usage of horizontal flips, and rotations are also very limited, thus reducing generality of the training set.

Two other test scenarios were addressed, a binary normal {\em vs.} deficit stress, and capability of day-by-day stress prediction.
Table \ref{table:treatment prediction A vs all} presents an example of a binary dichotomy, normal treatment (A) {\em vs.} all others (B,C,D) suffering more or less from water deficit.
No surprise here, the healthy plants are easily distinguished from the stressed.
In order to address the early prediction issue, we also supply the prediction accuracy per day, as opposed to any sequence of the last $N$ consecutive days.
Results of day-by-day prediction, obtained from combinations of single and triplet models, are presented in Figure \ref{fig:prediction by day}.

\begin{table}[htbp]
\tiny
\begin{center}
\caption{Binary prediction accuracy of normal condition (treatment A), {\em vs.} all other water deficit treatments.}
\label{table:treatment prediction A vs all}
\begin{tabular}{ |p{1.6cm}||p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|p{4mm}|}
 \hline
 \multicolumn{18}{|c|}{RGB and thermal rolling average prediction accuracy} \\
 \hline
 Sequence length N& 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17\\
 \hline
 Single RGB + Single thermal & 97.0 & 98.8 & 99.6 & 99.6 & 99.5 & 100 & 100 & 100 & 99.3 & 98.4 & 99.1 & 100 & 100 & 100 & 100 & 100 & 100\\
\hline
\end{tabular}\\
\end{center}
\end{table}

\begin{figure}[htbp]
\centering
\includegraphics[height=5.8cm]{zz_predictions_by_day.jpg}
\caption{Bar graph showing the prediction accuracy of each class as well as the average of all classes.
Notice the total accuracy (in purple) rising as the day number goes up.}
\label{fig:prediction by day}
\end{figure}

\section{Conclusion}
We have shown that machine learning methods and neural networks with comparatively small architecture have the capability to detect and predict stress conditions in plants.
Combination of RGB and thermal images has improved the results, and a temporal sequence of images has proven to obtain the best results.
Our results lead to the suggestion of combining multi-modal data, and acquisition of a controlled time-lapsed sequences of images.
We hope this modest contribution will serve the plant phenotyping community, by encouraging the ongoing efforts of carefully planned data collection, applying up-to-date computer vision methods, and analysis of stress detection and prediction.

\section*{Acknowledgment}
This research was partly supported by the Israel Innovation Authority, the Phenomics Consortium.

\par\vfill\par

\clearpage

\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


Zoe, Jennifer and Nadia are three women with nothing in common. Except for the man who wants to kill them. He sends them terrifying letters, full of the intimate details of their lives - and promises that he will bring those lives to a violent, horrible end. But not before he has enjoyed himself.
After Abbie Devereaux escapes a kidnapper, no one seems to believe her. Even worse, Abbie can't remember anything prior to her abduction. Determined to prove she was kidnapped, Abbie sets out to retrace her steps--and comes face-to-face with a very real psycho-killer. Only this time, she refuses to be his victim.
When a series of murders announce the return of the serial killer that sent psychologist Frieda Klein into hiding, Frieda emerges to confront the psychopath.
The series conclusion finds psychologist Frieda Klein driven into hiding by obsessed psychopath Dean Reeve, while criminology student Lola Hayes places herself at risk to follow in Frieda's footsteps. By an internationally best-selling author. (suspense).
Psychologist and police consultant Frieda Klein investigates after a corpse is discovered beneath the floorboards of her own home and she becomes a person of interest in the case. By the author of Saturday Requiem. Original. 25,000 first printing.
After leaving her childhood hometown to escape painful memories, psychotherapist Frieda Klein never intended to return, but years later, an old classmate asks for help with her daughter, leaving Frieda no choice but to return home to face her past.
Declared a person of interest when a body is discovered under the floor of her home, London psychologist Frieda Klein realizes that a copycat killer is responsible before she finds herself caught between two obsessive murderers.
When her ex-boyfriend's body turns up in the Thames and she becomes the prime suspect in the murder, Frieda Klein goes on the run to save herself and find the man she believes is the real killer, a man who has never stopped haunting her.


text:
Foreign Relations of the United States
About the Foreign Relations Series
Current Status of the Foreign Relations Series
History of the Foreign Relations Series
Ebooks Initiative
Quarterly Releases
Administrative Timeline
Biographies of the Secretaries of State
Principal Officers and Chiefs of Mission
Travels of the Secretary of State
Travels of the President
Visits by Foreign Heads of State
World War I and the Department
Buildings of the Department of State
U.S. Diplomatic Couriers
Guide to Countries
Guide to Country Recognition and Relations
World Wide Diplomatic Archives Index
Browse Resources by Subject Tag
Developer Resources & APIs
Open Government Initiative
A Short History of the Department
Foreign Relations of the United States, 1969–1976, Volume XX, Southeast Asia, 1969–1972
6. Memorandum From Secretary of State Rogers to President Nixon 1
Washington, March 17, 1969.
Congratulatory Message to Thai Prime Minister
For reasons set forth below, I recommend that you approve the attached message of congratulation to Marshal Thanom on his reappointment as Prime Minister.2 This message includes a general restatement of our security commitment to Thailand.
On March 7 the King designated Field Marshal Thanom Kittikachorn as Prime Minister under the new Thai Constitution. A message from you congratulating him on his reappointment would be appropriate.
We believe it would be in our interest to use this message as an occasion for reaffirming the U.S. security commitment to Thailand.
We have reliable intelligence that the Thai leaders are currently in a mood of questioning and doubt with regard to the firmness of the U.S. intentions in Southeast Asia. This has been heightened by the Communist offensive in Viet-Nam (which the Thai view as a breach of the "understandings" which led to the total bombing halt) and the U.S. reaction to it. They are also apprehensive about the forthcoming Senate Foreign Relations Committee review of U.S. commitments. Many of these concerns have been reflected in recent public statements by the Thai Foreign Minister.
While initially reassured by your election, the Thai leaders are in some doubt about the policies of the new Administration. They have made it clear that they are hoping for a full discussion of where we expect to go in the war in Viet-Nam and the Paris peace talks during the SEATO and TCC meetings. But it will be risky to leave them in doubt regarding our basic intentions in the two and a half months until these meetings.
Early in the last two Administrations the President sent a message reaffirming in general terms the United States security commitment to [Page 13]Thailand, so there are precedents for such a message. Conversely, the absence of a message could be noted by Thai leaders and add to their concern in their present mood.
Source: National Archives, RG 59, Central Files 1967–69, POL 15–1 THAI. Secret. Drafted by Spear and cleared by Godley and William P. Bundy.↩
Attached but not printed.↩
Southeast Asia, 1969–1972
Note on U.S. Covert Actions
Southeast Asia (Documents 1-185)
Thailand (Documents 1-114)
Philippines (Documents 186-265)
Indonesia (Documents 266-332)
Bundy, William P.Godley, George McMurtireNixon, Richard M.Rogers, William P.Spear, Moncrieff J.Thanom Kittikachorn, Field Marshal
Abbreviations & Terms
POLRGSEATOTCC
Status of the Series
History of the Series
Foreign Relations ebooks
Other Electronic resources for U.S. foreign relations
Guide to sources on Vietnam, 1969-1975
Tips for searching the Foreign Relations Series
How to cite Foreign Relations documents
Office of the Historian, Foreign Service Institute
United States Department of State
history@state.gov


text:
Callispa ruficollis es un coleóptero de la familia Chrysomelidae.

Fue descrita científicamente por primera vez en 1889 por Fairmaire.

Referencias

Callispa

redpajama_set_name: RedPajamaWikipedia


text:
Q: Call to a member function createData() on null using middleware on Laravel Transformers So i created a controller for authentication with 2 methods (token() / native)_). Im using fractal transformer to return response. The token method works fine for me, but the loginAndroid() returns

"Call to a member function createData() on null" error.

Any help? Thank you.
class AuthController extends RestController
{
    protected $transformer = UserTransformers::Class;

    public function __construct()
    {
        $this->middleware('auth:api', ['except' => ['login', 'loginAndroid']]);
    }

    public function login(Request $request)
    {
        $credentials = $request->only(['username', 'password']);

        if (!$token = auth()->attempt($credentials)) {
            return response()->json(['error' => 'Unauthorized'], 401);
        }

        return $this->respondWithToken($token);
    }

    public function loginAndroid(Request $request)
    {
        $credentials = $request->only(['username', 'password']);

        if (Auth::attempt($credentials)) {
            //$user = Auth::user()->with(['employees']);
            $userdata = User::with(['employees', 'employees.role', 'employees.branch'])->find(Auth::id());
            //$success['token'] =  $user->createToken('MyApp')->accessToken;
            //return response()->json($userdata, 200);
            //return $userdata;
            $response = $this->generateItem($userdata);

            return $this->sendResponse($response, 201);
        } else {
            return response()->json('gagal', 401);
        }
    }
}

this is my restcontroller
abstract class RestController extends Controller
{
protected $manager;

protected $transformer;

public function __construct()
{
    $this->manager = new Manager();
}

protected function generateItem($model, $transformer = null)
{
    if (!is_null($transformer)) {
        return new Item($model, new $transformer);
    }

    return new Item($model, new $this->transformer);
}

protected function generateCollection($model, $transformer = null)
{
    if (!is_null($transformer)) {
        return new Collection($model, new $transformer);
    }

    return new Collection($model, new $this->transformer);
}

protected function sendResponse(ResourceInterface $data, $status = 200)
{
    return response()->json(
        $this->manager->createData($data)->toArray(),
        $status
    );
}

protected function sendNotFoundResponse($status)
{
    return response()->json($status, 404);
}

protected function sendIseResponse($status)
{
    return response()->json($status, 500);
}
}

A: It looks like your sendResponse() method depends on $this->manager. However, $this->manager gets set in RestController::__construct() and you've overridden the __construct() method in your AuthController::__construct(). So, in order to have $this->manager available, you should call the parent constructor from your AuthController, like this:
class AuthController extends RestController
{
    protected $transformer = UserTransformers::Class;

    public function __construct()
    {
        parent::__construct(); // call the parent constructor where
                               //  $this->manager gets initialized
        $this->middleware('auth:api', ['except' => ['login', 'loginAndroid']]);
    }

... etc


text:
A Chinese Biogas Manual
Popularising technology in the countryside
Ariane van Buren, Michael Crook
Leo Pyle
The production of biogas, which is regarded in many countries as a by-product of an efficient system of energy production, has become in China a comprehensive, controlled method of waste disposal, recycling resources, supplying energy and fertilizer and improving rural health, as the digesting of wastes in a closed container kills many of the pathogens responsible for common human diseases.
There are already approximately seven million biogas pits in opertion, with Sichuan (Szechuan) province leading the country in a movement to extend the practice throughout the countryside. This manual describes in detail how to build the pit, and covers a full range of designs to suit various soils from sandstone to sheer rock. It also includes a description of how the original manual was used.
Prelims [Acknowledgements | Introduction]
1 The Advantages of Biogas for Rural Areas
2. Basic Information about Biogas
3. Basic Principles for the Construction of a Biogas Pit
4. Different designs of Biogas Pits
5. Maintenance and Quality Appraisal of Biogas Pits
6. Scientific Management of a Biogas Pit
7. Using Biogas
8. Safety Measures in Building Biogas Pits and Using Biogas
Back Matter [ Appendix I | Appendix II]
Ariane van Buren
Ariane van Buren has worked with large pension funds, foundations and endowments on energy, climate, and water risk. She advised the UN's Principles for Responsible Investment (UNPRI), and also served as director of investor engagement for Ceres and the Investor Network on Climate Risk (INCR), where she led trustee training and action by institutional investors on investment risks and opportunities. Professor van Buren is also a faculty member at Columbia University.
Michael Crook is co-founder of the Western Academy of Beijing, an international school located in the northeast of Beijing. As the co-founder, teacher and caretaker, he not only gives lectures, but also teaches the children how to make mooncakes and dumplings. More than 1,500 students from 54 countries are registered in the academy.
Leo Pyle Leo Pyle was Emeritus Professor of Biotechnology and former Head of Department of Food Biosciences, University of Reading
Handbook of Energy for World Agriculture
https://doi.org/10.1016/B978-1-85166-349-1.50016-6 [Citations: 0]


Tests can be very stressful for students from all aspects of life. They're a major deciding factor in whether you pass or fail the class you're taking. This may seem unfair because not everyone is the best at test taking. It's not as simple as just studying. There are steps students can take to prepare for taking tests.
Read your syllabus or class schedule! Make sure you're aware of every test date and put it down in your planner or Google Calendar. It's the worst when you learn about a test a few days before the actual exam and have to cram right before.
By supplies at least three days before the test, not all professors provide you with what is needed for the exam. Whether it be pencils or scantrons, if you need it for the test, buy it.
Study! Study! Study! You should be consistently studying for the test while you're going through the material in class so that you don't have to cram the night before the test. Studying in groups can be helpful as long as you and the group are being productive.
Take 15-minute breaks while studying, your brain needs time to process the information you're taking in. Don't over study for the exam because this leads to added stress and can negatively affect your mental and physical health.
Relax before the test. Don't try and cram more information into your brain. Watch some TV, take a nap, make sure you're well–rested and not overly stressed before the test.
Good luck with midterms, finals, and all other exams. You got this, collegiettes!
Zeairah is a sophmore at George Mason University. She spends most of her time reading, writing, and watching Netflix. Her favorite color is yellow and her favorite animals are dogs. She is double majoring in marketing and management with a minor in journalism with hopes of one day studying business law. She aspires to be many things such as a legal consultant, a creative director for Disney, or a travel/lifestyle writer for a magazine.