59 Matching Annotations
  1. Dec 2023
    1. de novo variant (c.2738_2739delAT [p.Tyr913∗])

      Case: DiseaseAssertion: FamilyInfo: CasePresentingHPOs: CaseHPOFreeText: CaseNOTHPOs: CaseNOTHPOFreeText: CasePreviousTesting: GenotypingMethod: PreviouslyPublished: as applicable SupplementalData: as applicable Variant: ClinVarID: A curator only needs to include either a ClinVarID or CAID, not both. CAID: d gnomAD: VariantEvidence: Only use if applicable

  2. Mar 2023
    1. Die Auswertung solcher Materialmengen erwies sich als prekär, und im Falle der häufigsten Wörter, z.B. mancher Präpositionen (allein das Wort m "in" ist über 60.000 Mal belegt) oder elementarer Verben mußte man vor den Schwierigkeiten kapitulieren und das Material aussondern.

      The preposition m "in" appears more than 60,000 times in the corpus, a fact which becomes a bit overwhelming to analyze.

  3. Oct 2022
    1. You do not reallyhave to study a topic you are working on; once your areinto it, it is everywhere. You are sensitive to its themes;you see and hear them everywhere in your experience,especially, it always seems to me, in apparently unrelatedareas. Even the mass media, especially bad movies andcheap novels and picture magazines and night radio, aredisclosed in fresh importance to you.
  4. Jul 2022
    1. We also tend to preferinformation we have seen more recently to informationwe learned a long time ago.

      Does this effect have a name? references?


      Apparently called the recency bias: https://en.wikipedia.org/wiki/Recency_bias which may be entangled with availability bias or heuristic.


      Are both recency and availability biases the foundations for causing the Baader–Meinhof phenomenon or frequency bias?

  5. Mar 2022
  6. Jun 2021
  7. May 2021
  8. Dec 2020
  9. Sep 2020
  10. Aug 2020
  11. Jun 2020
  12. Dec 2019
    1. So if you create one backup per night, for example with a cronjob, then this retention policy gives you 512 days of retention. This is useful but this can require to much disk space, that is why we have included a non-linear distribution policy. In short, we keep only the oldest backup in the range 257-512, and also in the range 129-256, and so on. This exponential distribution in time of the backups retains more backups in the short term and less in the long term; it keeps only 10 or 11 backups but spans a retention of 257-512 days.
  13. Jun 2019
    1. MelNet: A Generative Model for Audio in the Frequency Domain

      本文的主要贡献如下:

      • 提出了 MelNet。一个语谱图的生成模型,它结合了细粒度的自回归模型和多尺度生成过程,能够同时捕获局部和全局的结构。

      • 展示了 MelNet 在长程依赖性上卓越的性能。

      • 展示了 MelNet 在多种音频生成任务上优秀的能力:无条件语音生成任务、音乐生成任务、文字转语音合成任务。而且在这些任务上,MelNet 都是端到端的实现。

    1. operating in the frequency domain can offer considerable advantages over steady-state designs in terms of information gathering and transmission
    1. less sensitive to environmental noise and exposure time than systems based on steady-state signals
  14. Nov 2018
    1. Interpretable Convolutional Filters with SincNet

      一篇值得我高度关注的 paper,来自 AI 三巨头之一 Yoshua Bengio!其背后的核心是将数字信号处理DSP中卷积的激励函数(滤波器)进行了重新设计,不仅会保留了卷积的特性(线性性+时间平移不变性)还在滤波器上添加待学习参数来学习合适的高低频截断位置。

    2. Unifying Probabilistic Models for Time-Frequency Analysis

      文章涉及好些自己还没搞懂的概念和方法:

      • 时频分析
      • Gaussian processes
      • ...

      文中的 review 给的是很不错的~

  15. Jul 2018
    1. ...

      This punctuation mark appeared quite a lot in the maid's narrative. The loss of her Lady was unbearable to her. Her whole world used to revolve around her Lady. And a frequency comparison of '...' in this and other short stories of Katherine would probably show the maid's awkwardness in her monologue.

    2. Out came the thin, butter-yellow watch again, and for the twentieth—fiftieth—hundredth time he made the calculation.

      The watch is a recurring motif in this story. And time after time the 'thin', 'butter-yellow' aspects of the watch were underscored. And here Katherine demonstrated a peculiar use of the token '----' , maybe she used it in consistence with her other stories such as The Garden Party.

    3. At that she threw back her coat; she turned and faced me; her lips parted. “Good heavens—why! I—I don’t mind it a bit. I—I like waiting.” And suddenly her cheeks crimsoned, her eyes grew dark—for a moment I thought she was going to cry. “L—let me, please,” she stammered, in a warm, eager voice. “I like it. I love waiting! Really—really I do! I’m always waiting—in all kinds of places... “ Her dark coat fell open, and her white throat—all her soft young body in the blue dress—was like a flower that is just emerging from its dark bud.

      Could go in detailed analysis with all the striking colors. 'crimson','dark',;white', 'blue' and 'gold' appeared quite frequently in the story. Perhaps the colors were a symbolism of her status and her pensive nature. A young girl that was always waiting, in all kinds of places.

    4. There came a little rustle, a scurry, a hop.

      There have been some interesting verbs in the narrative thus far, especially this little cluster here. While the use of verbs has been frequent, the verbs them self have been some what gentle and not aggressive or assertive. I would be interested in performing a word frequency analysis to gather all the verbs, followed by a sentiment analysis to see if they are congruent with this theme of gentle submission / obedience which arises in the text.

    5. caring for the smell of lavender.

      Even this early in the narrative there have a variety of plants and characters introduced (almost like a garden of characters as well as plants). I would be interested in doing a comparison of the variety of characters mentioned, including those mentioned only by description compared with the plants. We perform a word frequency analysis and also look for Ngrams.

    1. The negative connotations of long stretches of waiting time also become apparent once we examine the symbolic implications of the frequency at which social contacts occu

      Frequency of activity symbolizes social commitment.

      Quotes Durkheim as indicating that frequency can be a measure of "moral density" in relationships.

  16. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. There is here, moral, if not legal, evidence, that the murder was committed by the Indians.

      This is a very interesting take on "evidence" as being moral if not legal by Sergeant Cuff. It makes me question exactly what he means by that if there is a way to use computational analysis to find out. We could perhaps start by parsing out "evidence" throughout the text with a machine learning algorithm to help he define evidence and then, going forward, device a way (maybe with sentiment analysis) to determine moral evidence from legal evidence.

    2. The chance of searching into the loss of the Moonstone, is the one chance of inquiry that Rachel herself has left me.”

      Throughout his narrative Blake repeatedly links the mention of Rachel to the mention of the Moonstone / Diamond. I would be interested in running a word colocation / frequency analysis to see how often this happens in Blake's narrative and throughout the rest of the text. It may also be worth while to do a sentiment analysis and see what the tone is for each mention based on which narrative it came occurred in.

    3. Having heard the story of the past, my next inquiries (still inquiries after Rachel!) advanced naturally to the present time. Under whose care had she been placed after leaving Mr. Bruff’s house? and where was she living now?

      Blake's account of Rachel is clearly distinct form the other narrators because of their romantic past. He mentions her frequently throughout his narrative. I would like to run a frequency count the number of times he mentions Rachel compared tot he rest of the narratives in the book. I wonder if it is possible to isolate the discussions of Rachel in each character's narrative and then do some topic modeling with the extracted texts to examine how Rachel is discussed by each character.

    4. It distressed me, it did indeed distress me, to hear her say that. She was so young and so lonely–and she bore it so well!

      Bruff's impression of Rachel is very different from Miss Clack, but similar to the affectionate tone of Betteredge. I would be interested in running a word frequency count on all of the ways Rachel is described by the different narrators and do a comparison between the words used by the different narrators and also which words they share in her description.

    5. Early on that memorable day, our gifted Mr. Godfrey happened to be cashing a cheque at a banking-house in Lombard Street

      Miss Clack has made several mentions of wealth, poverty and other financial concerns. It seems that she correlates her narrative with economic status or financially related events, such as chasing a check. I would be interested in doing a frequency count to see how often these types of terms are used in her narrative compared to the others.

    6. “This is a miserable world,” says the Sergeant.

      Sergeant Cuff is total downer. His dialogue tends to be really negative. I would be interested in doing a text analysis of the words his character uses to see the frequency of words with a negative connotation in comparison to the neutral and positive words used.

  17. Oct 2016
  18. Nov 2015
    1. Frequency

      Frequency of the note A_x: $$f(A_x) = 440 \cdot 2^{x - 4}$$

      Inverse (octave x of the note A_x from frequency f): $$x = \log_2{\frac{f}{440}} + 4 = \log_2{f} + 4 - \log_2{440}$$

  19. Sep 2015
    1. Rostral anterior cingulate gyrus

      SUMS Query: Anterior Cingulate

    2. Anterior rostral cingulate cortex

      SUMS Query: Anterior Cingulate

    3. Anterior Cingulate Gyrus

      SUMS Query: Anterior Cingulate

  20. May 2015
    1. "Thesethemesarehighlightedforthrereasons:(1)they represent the experiential clusters that emerged most frequentlyacrosage,clas,gender,ethnicityandwork seting,(2)theyarespokenofwithparticularpasion"