58 Matching Annotations
  1. Oct 2018
    1. malignant eye,

      Birds of prey have excellent eyesight. Scientists estimate that they can see two to eight times better than humans. They also have a third eyelid for protection.

    1. I saw the movement of content across media as an enhancement of the creative process. He saw it as a distraction or corruption.

      Points to a short-sightedness and tunnel vision in sections of media. Taking a focussed view on a very narrow area of a field, as opposed to a "world view" as advocated by the author.

    1. When irrationalism, as the counterplay of rationalism, talks about the things to which rationalism is blind, it does so only with a squint.

      Heidegger: "When irrationalism, as the counterplay of rationalism, talks about the things to which rationalism is blind, it does so only with a squint" || c.f. the narrowing / dimming of the optical field implied by the squint with the optics of disclosure and the rhetoric of blindness (de Man)

  2. Sep 2018
    1. Considero que este fragmento es importante ya que demuestra la visión de algunas de las empresas mexicanas, que es no abrirse a personas ajenas a la familia. Es una idea compartida en nuestro contexto y por lo que he checado en otros artículos también de los demás países de américa latina, es un dato interesante.

  3. Aug 2018
    1. By the year 2020 Estonian Smart City Cluster is internationally known as the leading developer and exporter of smart city solutions that are based on ICT and other technologies.
  4. Jun 2018
    1. Remember, the author made a more technical report on this topic. PDF here

    2. if we ever find the translation is dominant in a direction other than forward, we simply ignore that motion.

      Remember, this is just a heuristic

    3. Most Computer Vision algorithms are not complete without a few heuristics thrown in
    4. RANSAC. It is an iterative algorithm. At every iteration, it randomly samples five points from out set of correspondences, estimates the Essential Matrix, and then checks if the other points are inliers when using this essential matrix.
    5. T his step compensates for this lens distortion.
    6. For every pair of images, we need to find the rotation matrix RRR and the translation vector ttt, which describes the motion of the vehicle between the two frames.
    7. An efficient solution to the five-point relative pose problem
    1. our job is to construct a 6-DOF trajectory

      This is "forwards/backwards" in every major axis, and rotations in the same axis.

    1. Instead, we define the goal of the growth team as accelerating the realization of LinkedIn’s vision, which is to create economic opportunity for every member of the global workforce. Keeping this vision top of mind led us to set the right growth objectives and priorities.
    1. “Rap Genius is going be the fabric of the internet,” co-founder Mahbod Moghadam said in 2014 “Rap Genius is going be the fabric of the internet,” co-founder Mahbod Moghadam said in 2014. “We’re going to have annotations on other sites, so every other site in the world like the Wall Street Journal and the New York Times are going be Genius-powered and they’re going to have our annotations on them. And then the Genius platform will take over the internet; everyone’s most important statistic that they have in life is their Genius IQ.”
  5. Apr 2018
  6. Mar 2018
  7. Dec 2017
    1. Of another parcel of 153 acres near the former, and including a considerable eminence very favorable for the erection of a future observatory.

      This excerpt is very interesting as the reservation of land for the specific purpose of constructing an observatory seems to be very peculiar when considering the primary plans for the University. However, its inclusion is very relevant, as such reservations lead to the construction of the Leander McCormick Observatory, which currently sits on the summit of Mount Jefferson, commonly referred to as Observatory Hill. Though it took nearly 70 years for such plans to be carried out, the implementation of the McCormick Observatory has proved to be a prominent addition to the University, as it has helped enhance the education and has also served as a platform for astronomical research within the Astronomy Department.

      Link: http://astronomy.as.virginia.edu/research/observatories/mccormick

  8. Nov 2017
    1. As well might it be urged that the wild & uncultivated tree, hitherto yielding sour & bitter fruit only, can never be made to yield better: yet we know that the grafting art implants a new tree on the savage stock, producing what is most estimable both in kind & degree.

      I believe this metaphor to be very profound, as it challenges the statement within the previous sentences that, "man is fixed." The excerpt vividly depicts how a tree that yields sour and bitter fruit can be changed, through artistic processes, to produce a rather sweet fruit. These artistic processes serve to represent how man can be improved and changed through the role of education. Consequently, the Rockfish Gap Report discusses how education is crucial to the positive development of man, further illustrating Jefferson’s vision of the University of Virginia, where individuals will not only continue to grow as students, but also as cordial and honorable members of society. I believe that Jefferson’s vision has been fulfilled, as the University’s academic programs have enhanced the minds of those who have been, and are currently, students, while also providing them with a foundation of integrity and honor which will stay with them throughout their lives.

  9. Sep 2017
    1. Duncan (16) found that less than 5% of the figures in a typical textbook contain data. It is no wonder that students using a traditional and passive textbook do not know how to support their answers with data.

      Absolutely. The question is how to bridge the Perry Scheme so that students are more focused on evidence than the conclusion.

    2. However, if reading the book is a key component of class time and tests, students will use the text to help them construct their own understanding of the material.

      I agree. The text should be the organizing factor.

    3. Second, students must come to class prepared for classroom activities, which also facilitates studying for exams as the semester progresses.

      Hypothes.is also helps with this too

    4. First, student work outside of class needs to be intentionally and effectively structured.

      Hypothes.is is one tool that I use to achieve this goal.

    1. he admission of enlargement to any degree to which the institution may extend in future times.

      The Commissioners founded UVa with a vision and the confidence that the university will prosper and expand. They looked forward to the future and determine to make an impact--a core value still upheld by the university as of today.

  10. Aug 2017
  11. Jun 2017
    1. Who is Mistaken?Benjamin EysenbachMITbce@mit.eduCarl VondrickMITvondrick@mit.eduAntonio TorralbaMITtorralba@csail.mit.eduFigure 1: Can you determine who has a false belief about this scene? In this paper, we study how to recognize when a person in a short sequence is mistaken. Above, the woman is mistaken about the chair being pulled away from her.TimeFigure 1:Can you determine who believes something incorrectly in this scene?In this paper, we study how to recognizewhen a person in a scene is mistaken. Above, the woman is mistaken about the chair being pulled away from her in the thirdframe, causing her to fall down. Thered arrowindicates false belief. We introduce a new dataset of abstract scenes to studywhen people have false beliefs. We propose approaches to learn to recognizewhois mistaken andwhenthey are mistaken.AbstractRecognizing when people have false beliefs is crucial forunderstanding their actions. We introduce the novel prob-lem of identifying when people in abstract scenes have in-correct beliefs. We present a dataset of scenes, each visuallydepicting an 8-frame story in which a character has a mis-taken belief. We then create a representation of characters’beliefs for two tasks in human action understanding: pre-dicting who is mistaken, and when they are mistaken. Ex-periments suggest that our method for identifying mistakencharacters performs better on these tasks than simple base-lines. Diagnostics on our model suggest it learns importantcues for recognizing mistaken beliefs, such as gaze. We be-lieve models of people’s beliefs will have many


  12. Sep 2016
    1. The Tesla accident in May, researchers say, was not a failure of computer vision. But it underscored the limitations of the science in applications like driverless cars despite remarkable progress in recent years, fueled by digital data, computer firepower and software inspired by the human brain.

      Testing annotations. Interesting statement.

  13. Jul 2016
    1. Bird eyes have had eons longer to optimize. Along with their higher cone count, they achieve a far more regular spacing of the cells.
    1. Bird eyes have had eons longer to optimize. Along with their higher cone count, they achieve a far more regular spacing of the cells.
    1. Unsupervised Learning of 3D Structure from Images Authors: Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess (Submitted on 3 Jul 2016) Abstract: A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.

      The 3D representation of a 2D image is ambiguous and multi-modal. We achieve such reasoning by learning a generative model of 3D structures, and recover this structure from 2D images via probabilistic inference.

    1. When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning as standard practice for improved new task performance.

      Learning w/o Forgetting: distilled transfer learning

  14. Jun 2016
    1. Dynamic Filter Networks

      "... filters are generated dynamically conditioned on an input" Nice video frame prediction experiments.

    1. Atl=xtifl= 0MAXPOOL(RELU(CONV(Etl1)))l >0(1)^Atl=RELU(CONV(Rtl))(2)Etl= [RELU(Atl^Atl);RELU(^AtlAtl)](3)Rtl=CONVLSTM(Et1l;Rt1l;Rtl+1)(4)

      Very unique network structure. Prediction results look promising.

  15. May 2016
  16. Dec 2015
  17. May 2015
    1. il fait peur non pas parce qu’il commence à ressembler à l’humain, mais parce qu’au contraire, il se montre inflexible, aveugle au contexte, et, qu’il ait raison ou qu’il ait tort, capable de soumettre les hommes aux lois qu’ils ont eux-mêmes créés.
  18. Mar 2015
    1. ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node. We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures.
  19. Jan 2014
    1. An effective data management program would enable a user 20 years or longer in the future to discover , access , understand, and use particular data [ 3 ]. This primer summarizes the elements of a data management program that would satisfy this 20-year rule and are necessary to prevent data entropy .

      Who cares most about the 20-year rule? This is an ideal that appeals to some, but in practice even the most zealous adherents can't picture what this looks like in some concrete way-- except in the most traditional ways: physical paper journals in libraries are tangible examples of the 20-year rule.

      Until we have a digital equivalent for data I don't blame people looking for tenure or jobs for not caring about this ideal if we can't provide a clear picture of how to achieve this widely at an institutional level. For digital materials I think the picture people have in their minds is of tape backup. Maybe this is generational? New generations not exposed widely to cassette tapes, DVDs, and other physical media that "old people" remember, only then will it be possible to have a new ideal that people can see in their minds-eye.