Letter from Katherine Freeman to Mrs. [William] Keith, [1902] Nov 23.

Channing Way, Berkeley,November 23, [1902].My dear Mrs. Keith:When I told Mr. Biedenbach I asked you to write Mr. Keith he was delighted and asked me to see you and say that the success of the day depends on Mr. Muir coming to dedicate the tree, and will you please write him to this effect, and we want also to have you and Mr. Keith with us. Will send you tickets to grand stand as soon as we receive them from the printer\u27s. I called at your house yesterday morning, but you had gone to Oakland.We appreciate very much your writing this letter for us to Mr. Muir. With love, and best wishes for a favorable reply,Yours very cordially,Katherine Freeman,[Letter marked, in Muir\u27s handwriting, Katherine Freeman on planting Sequoia Berkeley High School ].0620

Letter from Katherine Freeman to Mr. & Mrs. [J] Muir, 1902 Dec 1.

[in margin: 909]Berkeley CaliforniaDecember. 1. 1902.My dear Mr & Mrs MuirArbor Day passed off very successfully- delightfully interesting - the only disappointment being the absence of the one so dear to the hearts of all Californians and to whom the beautiful tree was dedicated- We received your telephone message this morning and appreciate very much your good wishes for the day\u27s success - and felt you were03110 with us in spirit - some time when you come to Berkeley you must visit the High School grounds and see this tree and read the inscription on the plate. a paper giving an account of the exercises will be sent to you. Also a souvenir program. Hoping you will both soon recover from the prevailing colds - and thanking you for your kind letters.Yours very sincerelyKatherine Freemanfor Arbor Day Committe

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.Comment: The first two authors contributed equally to this wor

Colorectal Cancer Brochure Development for African Americans

Introduction: African Americans are more likely to die from colorectal cancer (CRC) than any other racial/ethnic group in the United States. Unfortunately, African Americans are also less likely to undergo screening for CRC than their White counterparts. Focus groups methodology was used to refine educational brochures designed to increase CRC screening among African Americans. Methods: Two series of focus groups were completed, with a total of seven groups and 39 participants. Six different brochures (stage-matched and culturally sensitive) designed to promote CRC screening among African Americans were evaluated. Results: All participants thought that the brochures motivated them to talk with their health care providers about screening. Cost, pain, medical mistrust and fear were identified as major barriers and the brochures were modified to address these concerns. Conclusions: Focus groups methodology with African Americans can be used to inform brochures designed to increase African Americans CRC screening that addresses their major concerns

Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first two authors contributed equally to this work. Project page: http://visualdynamics.csail.mit.ed

So You Want to Do Post-Intentional Phenomenological Research?

In this article, phenomenology, both in its philosophical and methodological variants, is introduced in the form of a fictional dialogue between a student justifying her interest in using a post-intentional phenomenological approach in her dissertation to her major professor. The dialogue tackles founding philosophers, notably Husserl, Heidegger, and Merleau-Ponty; contemporary researchers, including A. Giorgi, B. Giorgi, van Manen, and Vagle; several phenomenological concepts, such as intentionality, bracketing, and bridling; and provides examples of three distinct approaches to phenomenological research

John M. Hayes 1940-2017. Father of isotopes in modern and ancient biogeochemical processes, biosynthetic carbon and hydrogen isotope fractionation and compound specific isotope analytical techniques

John Michael Hayes, Professor of chemistry and geology for 26 years at Indiana University (Bloomington) until 1996, then director of the National Ocean Sciences Accelerator Mass Spectrometry facility at Woods Hole Oceanographic Institution and adjunct professor at Harvard University until 2007, died peacefully at his home in Berkeley, California, on February 3rd, 2017