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"We need to transcend the de facto dichotomy of empiricism and rationalism that is immanent in embedded and embodied entities. Hybrid media is one trajectory to make a rapture for becoming or to draw a line of flight that breaks the mold of the preconceived hylomorphic notion of existentialism, but there are still uncharted territories and new areas for creative exploration, particularly in terms of the affect-driven epistemology of perception." Ryota Matsumoto As an artist, designer, and architect, Ryota Matsumoto is internationally recognized as one of the progenitors of the postdigital art movement. Born in Tokyo, he was raised in Hong Kong and Japan. He received a Master of Architecture degree from University of Pennsylvania in 2007 after his studies at Architectural Association in London and Mackintosh School of Architecture, Glasgow School of Art in early 90’s. Over the years, he has studied with Manuel DeLanda, Vincent Joseph Scully Jr., Cecil Balmond, and Giancarlo De Carlo, among others. Matsumoto has previously collaborated with a cofounder of the Metabolist Movement, Kisho Kurokawa, and with Arata Isozaki, Peter Christopherson, and MIT Media Lab. Matsumoto's multidisciplinary projects have been exhibited recently at Meadows Gallery University of Texas at Tyler, S. Tucker Cooke Gallery University of North Carolina Asheville, Sebastopol Center for the Arts, National Museum of Korea, Czong Institute for Contemporary Art, Van Der Plas Gallery, ArtHelix Gallery, Caelum Gallery, Limner Gallery, the Cello Factory, University of the District of Columbia, Lux Art Gallery, Studio Montclair, Manifest Gallery, Tenerife Espacio de las Artes, Art Basel Miami, ISEA International, FILE Sao Paulo, Nook Gallery, and Arts and Heritage Centre Altrincham. He had solo exhibitions at BYTE Gallery Transylvania University (2015), Los Angeles Center of Digital Art (2016) and Alviani ArtSpace, Pescara (2017). https //www.ryotamatsumotostudio.com 美術 デザイン アーバニズム
https://w.atwiki.jp/taichik15/pages/37.html
browserbot プロパティ・関数一覧 abortXhr allowNativeXpath altKeyDown baseUrl browserbot buttonWindow controlKeyDown currentWindow currentWindowName ignoreResponseCode isSubFrameSelected isXhrDone isXhrSent locationStrategies metaKeyDown modalDialogTest newPageLoaded nextConfirmResult nextPromptResult openedWindows pageLoadError permDeniedCount pollingForLoad proxyInjectionMode recordedAlerts recordedConfirmations recordedPrompts shiftKeyDown shouldHighlightLocatedElement topFrame topWindow uniqueId windowPollers xhr xhrOpenLocation xhrResponseCode xhrStatusText xpathEvaluator XXXreschedulePoller _fireEventOnElement _getFrameElement _getFrameElementByName _getFrameFromGlobal _getTargetWindow _handleClickingImagesInsideLinks _handleClosedSubFrame _isHTASubFrame _isSameDocument _isSamePage _maybeDeleteCookie _modifyElementTarget _modifyWindow _namespaceResolver _recursivelyDeleteCookie _recursivelyDeleteCookieDomains _registerAllLocatorFunctions _selectFirstNonTopWindow _selectTopWindow _selectWindowByName _selectWindowByTitle _selectWindowByWindowId _windowClosed abortXhrRequest addSelection bodyText cancelNextConfirmation checkMultiselect checkedOpen clickElement close contextMenuOnElement deleteCookie doModalDialogTest doesThisFrameMatchFrameExpression doubleClickElement evaluateXPathCount findAttribute findElement findElementBy findElementOrNull findElementRecursive getAllButtons getAllCookieNames getAllFields getAllLinks getCookieByName getCurrentPage getCurrentWindow getDocument getNextAlert getNextConfirmation getNextPrompt getNonTopWindowNames getReadyState getTitle getUserWindow getWindowByName getWindowNameByTitle getXPathEngine goBack goForward hasAlerts hasConfirmations hasPrompts highlight isNewPageLoaded isPollingForLoad locateElementByAlt locateElementByClass locateElementByCss locateElementByDomTraversal locateElementById locateElementByIdentifier locateElementByLinkText locateElementByName locateElementByUIElement locateElementByXPath modifySeparateTestWindowToDetectPageLoads modifyWindowToRecordPopUpDialogs onXhrStateChange openLocation openWindow pollForLoad recordPageLoad recursivelyDeleteCookie refresh relayBotToRC relayToRC removeSelection replaceText reschedulePoller resetPopups runScheduledPollers selectElements selectElementsBy selectFrame selectOption selectPopUp selectWindow setAllowNativeXPath setIFrameLocation setIgnoreAttributesWithoutValue setNextPromptResult setOpenLocation setShouldHighlightElement setXPathEngine submit triggerMouseEvent
https://w.atwiki.jp/constlangmajuuyuen/
Index About this page What is Majuuyu Majuuyu Characters Pronunciation Grammar Prospects as an international auxiliary language IME Verb animation feature proposal Notes About this page This is the official wiki of "Majuuyu" which is the language constructed by N.koduke (N.こづけ). Already known Majuuyu Words Majuuyu Vocabulary Answers for some questions from comments of the YouTube video Q and A If you have some ideas, please leave some comments here Comment Field 日本語バージョンの説明を見たい方は下のリンクへどうぞ Also you can read the wiki in Japanese from below. →https //w.atwiki.jp/constlangmajuuyu/pages/1.html Note We are not so good at English, so this page could have something wrong about English grammar or something. What is Majuuyu "Majuuyu" is pronounced like mah-zhoo-yoo. Majuuyu, an artificial language, is an international auxiliary language constructed based on the concept that is "easy to learn". Features Majuuyu words are composed by simple ideographic characters called Bases, which makes it it easy to create words and to be visually understandable. Explanatory video by the creator (but in Japanese) Majuuyu Characters As discribed above, Majuuyu words are composed by simple ideographic characters called Bases. The following is lists of Bases. Bases may be added or be removed in the future. Ideograph Bases (for words) Phonetic Bases (for grammar) ・The size of Bases Majuuyu has the rule about size of words. And each Base has particular width and height to make it easy to represent size of words. In this section, the length of the scale of the accommodating compartment described below is set to 1. Hereinafter, they are written in the order (width, height). The height and width of ideographic Bases (width, height) (2, 2) (2, 2) (3, 1) (2, 2) (2, 2) (1.5, 3) (2, 2) (1.5, 3) (2, 2) (3, 1) (3, 1) (3, 1) (2, 2) (3, 1.5) (2, 2) (1, 1) (2, 2) The height and width of phonetic Bases (2, 3) (2, 2) Note The size of Bases which is representing part-of-speech are not conciderd. Special Base (0,0) Space Base This is a shapeless Base with only sound. It is mainly used for expressing the layout in text communicaton. For example, is described like that "There are a Space Base at the top and → at the bottom." And is described like that "There are → at the top and a Space Base at the bottom." These differences are easy to recognize visually but they are difficult to describe verbally. So the Space Base is used. The sound of it is "mu" (temporaly). They are placed according to the placement pattern table below, and do not have a shape, but affect the placement. ・Rules about creating words The size of words A character must be placed so that it fits into the above compartment (7 horizontal x 6 vertical). This means that the sum of the vertical values of the entire character must be less than 6 and the sum of the horizontal values must be less than 7. If this assumption is met, the entire character will be enlarged to the limit of the compartment (for improve readability). Maximum number of usable bases The number of bases that can be used per character is limited to 7. Any more than this, and the placement patterns will become too many and the pronunciation will become redundant (though it is redundant at the moment). The outermost base of the base in which the "entering" is happening is equally counted as one. The direction of bases Some bases have a direction. To distinguish the directions verbally, bases of the same kind with different directions have different pronunciations. At the moment, there are only two or four directions, but there is a possibility that there will be eight directions in the future. The following is a base depicting the directions. entering In majuuyu, a base can go inside a base. I call it "entering" when a base goes inside a base. The base outside of the letter where the entering is occurring is called the outermost base, and the base inside is called the inside base. ↓example of words↓ To pronounce orally the letter in which entering is occurring, use "Su + (outermost base sound) + (inside bases sound) + Su". Therefore, when entering occurs in →, it is pronounced as "suyasu. When entering occurs at the ←, the pronunciation is "sujasu". Entering can only be done up to one inside. This means that triple circles, etc. are not possible. The bases in which entering is occurring have a special way of calculating parcels. If the inside base is a single "・", the size is counted as (2,2). Otherwise, the size is calculated using only the inside bases. However, the outermost base is also counted as one for the number of bases used. The following is a list of possible outermost bases and the letter form of outermost base when the content is blank. When the inside is empty, it is pronounced as "Su + (outermost base sound) + Su". Layering In majuuyu, there are times when a base overlaps under a base. I call it "layering" when a base comes under another base. In addition, the base where the layering occurs is called the layering base, and the base that comes under the layering base is called the under bases. Note Creator is not good at English, so the naming is just perfunctory. Please don t be offended if I m wrong. If you want, please comment in the comments section with a better name. ↓example of words↓ When orally pronouncing the letters in which layering is occurring "Re + (layering base sound) + (under bases sound) + Re". Therefore, when layering occurs in →, it is pronounced "reyare". And so, when layering occurs in ←, it is "rejare". In layering, only one base can be placed on top, while under bases can be placed up to the entire compartment. The way to calculate the size of the base where layering is occurring is (the size of layering base)≤(the total size of under bases) then the total height and width of the under bases is counted as the size of the word, and (the size of layering base) (the total size of under bases) then the size of the layering base is counted as the size of the word. The number of bases used is the sum of one layering base and the number of under bases. Distance between bases In Majuuyu, bases may be attached to bases without any spaces between them. For example, in the word "majouille," from which this artificial language is derived, each base is completely attached to each other. I am still working on how to verbally distinguish this. It is also expected to have some effects on production. Pronunciation There are many pronunciations that need to be examined, but for now, here are some tentative pronunciations. re reh ka kah ki ge kih geh ku kuh me meh mi pi mih pih ra rah sa sah se seh shi shih te da teh dah ya ja yah jah yo jo yoh joh yu juh to ni o ta n Grammar There are still a lot of undecided items, so I ll write about them once the main idea is decided. The grammar will probably be similar to Japanese. I m thinking that it might be a good match for Chinese tense expressions. Prospects as an international auxiliary language I d like to aim for a language that has the position of "sometimes you see it on the information board at the airport...". I d also like to see comments on the Internet that are written in Majuuyu, or something like that. IME In the future, Majuuyu would like to be able to input directly in IME. Direct input here means something like the Hangul IME, which automatically combines the elements. In addition to direct input, I recently started thinking that it would be nice to be able to convert from other existing languages. For example, if you type "person" in English, "i" will appear as a conversion candidate, and if you type "people", "iii" will appear as a conversion candidate. The creator is not an expert in programming. If I want to develop an IME, I will have to study it by myself or ask someone else to do it. In any case, I need to think about how to input data. The following is a list of current ideas. Draft of the process Receive input from keyboard ↓ Call up placement patterns based on the number of characters entered (up to 7). ↓ Assign the value of the length and width of the bases to the called placement pattern. ↓ Based on the assigned values, find the sum of the length and width of each matrix, and find the maximum value. ↓ Exclude patterns where (maximum horizontal value) 7 or (maximum vertical value) 6 ↓ For the remaining placement patterns, output images so that the coordinate point "・" is at the center of each base. If there is more than one candidate for conversion, machine learning or other methods will be used to prioritize the most frequent words for display. The following are the placement patterns (6 and 7 are still under construction) Number of bases 1 Number of bases 2 Number of bases 3 Number of bases 4 Number of bases 5 Number of bases 6 Verb animation feature proposal I think the verbs in Majeure are rather intuitive and easy to understand (I m proud of that). Wouldn t it be much easier to understand if the verbs were animated and the characters really moved? Wouldn t it be easier to understand? If we take into account the fact that we will be spreading the word on the Internetit should not be technically impossible to create a language in which letters move. Even in the days of Galakay, it was possible to send moving pictograms, so I don t see why it can t be done with Majeure. The following videos are simulations of what happens when you animate a verb. It looks really good, doesn t it? I can see a future where Majuuyu is used on electronic billboards ) (I recommend right-clicking on the video and playing it in a loop.) Notes I m trying to create a placement pattern for the IME. There are so many, I m dying. Someone help me! I ve never heard of such an easy way to go over 100 patterns...
https://w.atwiki.jp/macrossuf/pages/187.html
BATLLE FRONTIER バトルフロンティア 新マクロス級では第25次、通算では第55次の超長距離移民船団の旗艦「マクロス・フロンティア」の前部に位置するバトル級可変ステルス攻撃宇宙空母。 巡航時は居住艦である「アイランド1」の先端にドッキングしている。船体各所に25のマーキングがあり、 司令室の球形のホログラムスクリーンには「バトル25」と表示されている。ただ作中では船団名同様「バトル・フロンティア」と呼ばれている。 バトル7と同形艦と思いきや全長が200mほど伸び、重量は2倍以上に増していたりする。 艦体は6隻の戦闘艦が組み合わさる構造で（指揮空母1隻（胴体）・空母2隻（両腕）・突撃戦艦2隻（両脚）・重砲撃艦1隻（主砲）となっている）、 分離後も各個が独立展開・フォールド航行可能という高性能艦。従来艦のバトル級同様にトランスフォーメーション機能を持つ。 強攻型（人型形態）に変形した時は右肩に主砲マクロスキャノンが接続されるが、空母形態のままでも主砲の使用が可能。 （劇中ではバトル・ギャラクシーの攻撃で主砲を破壊されたため、強攻型での発射シーンは無いが） 強攻型に変形後は、マクロスのダイダロスアタックと同様のマクロスアタックで攻撃をすることができる。 カスタムカラーの元ネタ カスタムカラー1 カスタムカラー2 カスタムカラー3 性能 通常装備 チューンpt 30925 HP 8900/9500 射撃防御 85/94 格闘防御 76/96 スピード 65/85 ブースト 82/99 バランサー 21/41 旋回 16/36 レーダー性能 87/99 兵装 名称 威力 命中 連射数 有効範囲 弾速 リロード 弾数 連装誘導対艦重ビーム砲 553/625 - 1 42/62 22/42 20/40 21/27 接近防御用マイクロミサイルファランクス 48/52 34/54 1 32/52 41/61 52/72 5/6 対艦用反応弾 801/903 - 1 14/34 09/29 18/38 32/48 マクロスキャノン 1520/1724 30/50 1 52/72 46/66 18/38 1 格闘 252/301 45/65 - - - - - ※兵装のパラメータは連射数、弾数のみ実際の値 SPA：マクロスキャノン 機体特徴 ファイター ガウォーク バトロイド 機体テンプレート Ver.1.03
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https://w.atwiki.jp/homogeneous/pages/17.html
Timとは何なのか？ これは人類が未解決の問題である。 その正体については諸説ある。 しかし、ATGはTimくんにカ、キャフェテリアで知り合ったということ、ATGに対して唯一攻め側の人間であることはほぼ確かであると言われている。 ATG曰く、電話で彼と話す時は、日本人と間違えるほど日本語が流暢である。 大学時代にはATGとカ、キャフェテリアで一緒にバイトしていた。 Timくんには顔文字があるのでみんな使ってあげよう (TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM)(TiM) Tim君に関する学説 ATGの濃厚白濁ミルク説 最初に提唱された説である。ATGに対して何故攻める側になることが出来るのか？ という疑問を解消するためこのような説が提唱された。 これなら、授業中にも一緒にいれるということで当初は多数の賛成支持を得られていた。 概念説 Timくんは形而下の実態を持った存在などでなく、形而上の概念的存在であるとする説。 現在最も有力な説である。 八百万の神々説 古来、日本人がまだ狩猟生活をしていた時代 賢者Timは嘆いていた 我にふさわしき猛者はおらんものか、と 人間は未だに獣を犯すことしか脳にない実に愚かなことだ 早く我を見てくれんものかと、我を犯そうとするものはおらんのかと日々懊悩していた そんな時だ、彼を見たのはーー 一目惚れだった 一目見たその時から彼の虜になってしまった 雪の降る夜、雄雄しく猛々しく猛威をふるっていた彼 その気高き、優艶とした腰の振り ああ、なんと美しきことか 我も混ぜてくれえええええええ 慟哭する だが彼に届くことはない なぜなら我は八百万の神々 決して人間と交わることは許されぬ そうだ、種をさづけよう その種が芽吹いた時、彼は神にも等しき力を手にするだろう だから、その種がどうか空の向こうで咲きますように ‘ ’
https://w.atwiki.jp/oyaji/pages/163.html
URBAN A-ban 衣食住と娯楽、ワーキングを一つの区画に纏めようとした政府企画(西遠市メガンティプロジェクト)の象徴である巨大建造物。 建築作業が内装を残すのみという段階で、プロジェクト責任者と建築業者の間で癒着問題が発覚し、作業がストップしてしまっている。 ふゆほたるを巡る怜治とかなたの戦闘や、漁夫の利を狙った茶深とオゥルの策謀のせいで全面炎上・一部半壊してしまった。
https://w.atwiki.jp/robotictooluse/pages/17.html
S. Ioffe and C. Szegedy, “Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint arXiv 1502.03167, 2015. https //arxiv.org/abs/1502.03167 Abstract Training Deep Neural Networks is complicated by the fact thatthe distribution of each layer s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon asinternal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch.Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some caseseliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters. まとめ 1. どんなもの DNNの学習では，浅い層のパラメータ変化によって深い層への入力が変化してしまう。このような現象を内部共変量シフトという。内部共変量シフトが生じると学習が難しくなったり不安定化したりする。その影響は層が深いほど深刻になる。 この問題は，各層に入力されるデータの分布を揃えることで低減できる。提案手法のBatch Normalization（以下，BN）では，ミニバッチ単位でデータの分布（平均と分散）を揃えることで，内部共変量シフトを低減する。 2. 先行研究と比べてどこがすごい 従来は学習率を低くしたりDropoutを適用したりして学習させてきたが，いずれの手法も学習速度を低下させてしまう。それに対してBNでは，学習速度を低下させる要素が存在しない。 他にも以下のような利点がある： BNは内部共変量シフトの影響を低減するので，学習率を大きくできる。実験では既存モデルの30倍まで学習率を大きくしても学習できた。 データの分布を活性関数の非飽和領域に揃えれば，勾配消失問題が起こらない。ReLUのような非飽和の活性関数じゃなくても学習できる。実験ではシグモイド関数を使ってDNNの学習に成功した。 正規化処理によって非線形変換のヤコビ行列の特異値が1に近づくので，損失勾配が効率的に伝播するようになる。 ミニバッチ集合を毎回シャッフルすることで正規化処理も微妙に変化する。これは正則化に近い役割を果たすので，BNがあればDropoutやL2正則化などがなくても過学習しにくい。 DropoutもReLUも深層学習の黎明期を拓いた重要な技術である。Batch Normalizationはその双方を置き換えうる手法ということで，深層学習の分野に大きなインパクトを与えた。 3. 技術や手法の肝はどこ？ 各次元ごとにミニバッチを正規化（平均0，分散1に変換）した後に，バイアスとスケーリング処理を加える。バイアスベクトルとスケーリングベクトルは学習によって最適化する。 計算を各次元で独立に行うことで，少ない計算量で白色化の近似を行う。 バイアスとスケーリングによって，所望の平均と分散に変換できる。 バイアスとスケーリングが学習データセットの平均と分散に一致すれば，正規化処理を打ち消すこともできる。 データセット全体の平均・分散の近似として，ミニバッチの平均・分散を用いる。 分布の平均と分散を計算するための計算量が削減できる。 ミニバッチの分布にわずかな偏りが生じることで，正則化の効果も期待できる。 4. どうやって有効だと検証した？ BNを適用したCNNを使って，ImageNet（画像認識タスク）を学習させた。既存モデルの1/14の学習ステップ数で同等の正解率に到達し，“4.9% top-5 validation error (and 4.8% test error)”で当時のSoTA。 5. 議論はある？ NIPS 15の時点で“If you aren t using batch normalization you should [http //codinginparadise.org/ebooks/html/blog/ten_deep_learning_trends_at_nips_2015.html]”と言われるくらいには広く使われている。 RNNに適用するとメモリ消費が大きくなる。各時刻の処理に対して別個にBNを学習させる必要があるようだ。 これに対処する手法がLayer Normalization。 6. 次に読むべき論文は？ [Shimodaira 00] covariate shift（共変量シフト）を定義した論文。 [杉山 06] 共変量シフトに関する日本語の資料。「共変量シフトとは，与えられた入力に対する出力の生成規則は訓練時とテスト時で変わらないが，入力（共変量）の分布が訓練時とテスト時で異なるという状況である．」とのこと。 [Ba+ 16] Layer Normalization; RNNに適用可能な正規化手法。 7. その他 序論にて，ミニバッチ学習の利点が紹介されている： ミニバッチに対する損失の平均はデータセット全体に対する損失の平均の推定値となる。よって，ミニバッチサイズが増えるほど学習の質が上がる。 ミニバッチ内の各計算は並列化できるので，ミニバッチサイズが大きいほど並列化処理の恩恵が受けられる。
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【検索用 morbidmorsel 登録タグ PV師 作M 作MM-P 作り手 作詞家 絵師】 + 目次 目次 特徴 リンク 曲 CD コメント 特徴 作り手名：『morbid-morsel』(もーびっと もーぜる) フィリピン人の作詞家・絵師・PV師。 「TravelBySheep」とも名乗る。 リンク DeviantArt Newgrounds 曲 100年の暗闇の中で In Verbatim The Logical Pink Gizmo and The Gingerbread Man 万華鏡の霧 CD まだCDが登録されていません。 コメント 名前 コメント
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