All – 第 3 頁 – 人工智慧技術暨全幅健康照護聯合研究中心

Symbolic-neural learning involves deep learning methods in combination with symbolic structures. A “deep learning method” is taken to be a learning process based on gradient descent on real-valued model parameters. A “symbolic structure” is a data structure involving symbols drawn from a large vocabulary; for example, sentences of natural language, parse trees over such sentences, databases (with entities viewed as symbols), and the symbolic expressions of mathematical logic or computer programs.

Symbolic-neural learning has an innovative feature that allows to model interactions between different modals: speech, vision, and language. Such multimodal information processing is crucial for realizing research outcomes in real-word.

For growing needs and attention to multimodal research, SNL workshop this year features researches on “Beyond modality: Researches across speech, vision, and language boundaries.”

Topics of interests include, but are not limited to, the following areas:

- Speech, vision, and natural language interactions in robotics
- Multimodal and grounded language processing
- Multimodal QA and translation
- Dialogue systems
- Language as a mechanism to structure and reason about visual perception
- Image caption generation and image generation from text
- General knowledge question answering
- Reading comprehension
- Textual entailment

Deep learning systems across these areas share various architectural ideas. These include word and phrase embeddings, self-attention neural networks, recurrent neural networks (LSTMs and GRUs), and various memory mechanisms. Certain linguistic and semantic resources may also be relevant across these applications. For example, dictionaries, thesauri, WordNet, FrameNet, FreeBase, DBPedia, parsers, named entity recognizers, coreference systems, knowledge graphs and encyclopedias.

www.am.sanken.osaka-u.ac.jp/SNL2021/index.html

【活動訊息轉發】「2021科研發光」系列講座歡迎踴躍報名參加！

2021-03-23/在： All, 相關訊息

「2021科研發光」系列講座歡迎踴躍報名參加！

《科學人雜誌》與師範大學合作於2021年舉辦「2021科研發光」系列講座，今年將於北中南三地進行AI、5G，以及精準醫療三個不同主題之演講座談，三場活動分別邀請了學界與產業界的重量級講師來與學子及社會大眾分享其科學研究與最新應用。

誠摯邀請貴會會員共同參與、交流。

2. 個人線上報名：

■ 台北場/AI智慧闖入未來新生活：https://sa.ylib.com/event/20210411/EDM.html

■ 台中場/推動5G智慧新世代：https://sa.ylib.com/event/20210417/EDM.html

■ 台南場/精準醫療新藍海：

https://sa.ylib.com/event/20210424/EDM.html

【活動訊息轉發】ACM ICMR 2021 Grand Challenge: PAIR Competition歡迎踴躍報名參加！

2021-01-11/在： All, 相關訊息

ACM ICMR 2021 Grand Challenge: PAIR Competition歡迎踴躍報名參加！

Challenge Title:

Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

Registration URL:

https://aidea-web.tw/icmr

Competition Start Date:

01/04/2021

Challenge Description:

Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.

In this competition, we encourage the participants to design object detection models that can be applied in Taiwan’s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.

Regular Awards

According to the points of each team in the final evaluation, we select the highest three teams for regular awards.

Champion: $USD 1,500
1^st Runner-up: $USD 1,000
3^rd-place $USD 750

Special Awards

Best accuracy award – award for the highest mAP in the final competition: $USD 200;
Best bicycle detection award – award for the highest AP of bicycle recognition in the final competition: $USD 200;
Best scooter detection award – award for the highest AP of scooter recognition in the final competition: $USD 200;

All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.

👇👇👇

ACM ICMR 2021 Grand Challenge: PAIR Competition

2020台灣醫療科技展，本中心與臺大醫院智慧急診共同舉辦之「集點填問卷抽大獎」活動，中獎名單出爐囉!

2020-12-11/在： All, 相關訊息

2020台灣醫療科技展，本中心與臺大醫院智慧急診共同舉辦之「集點填問卷抽大獎」活動，中獎名單出爐囉!

📌首獎：ASUS筆記型電腦ROG Strix G15 G512LU一台（市價44,900元）一台
中獎人：

陳O甄 09762**577

📌二獎：ASUS智慧手錶VivoWatch SP（市價10,900）五支
中獎人：

陳O燊 09201**895
洪O滿 09211**618
孫O娜 09117**827
鄧O文 09800**268
林O玲 09128**206

📌三獎：臺大醫院及臺灣大學紀念品（市價1,080元）25份
中獎人：

宋O德 09197**049
游O雯 09211**133
王O萱 09191**486
林O尚 09160**312
陳O豪 09108**705
黃O筱雯 09752**958
陳O銘 09203**423
張O靜 09890**582
廖O淇 09050**365
林O潔 09756**217
張O瑋 09833**773
黃O珊 09191**329
顏O恆 09207**133
謝O博 09192**848
林O任 09396**510
何O月 09386**790
施O仁 09087**751
陳O睿 09369**657
蘇O國 09633**812
韓O勳 09650**285
李O琦 09762**369
覃O喆 09563**347
林O淵 09220**045
李O增 09398**743
洪O婷 09729**140

恭喜以上中獎人!
本中心將後續以Email與簡訊或電話通知，敬請務必回覆並提供相關所需資料，以利獎品領取及相關稅務事宜。
另提醒，本活動進行當下，您已同意以下事項：

敬請於12/21 23:59前回覆，逾期將放棄得獎資格，不得異議。
若有任何問題，請聯繫
張先生: yoyuchang@ntu.edu.tw

抽獎注意事項：

主辦單位將於展後(12月11日14時)於線上公開抽獎，當日於臺大AI中心官網(https://aintu.morcept.tw)公佈得獎者名單，後續並以電話或 e-mail方式聯繫得獎者。詳細時間與流程請隨時注意臺大AI中心官網公告。
參與者需以可用身份證件證明之姓名抽獎，領獎需核對身份證件，並配合主辦單位規定之領獎方式。若填寫資料不正確，或無法聯繫到本人，以及無法在規定時限內回覆，則視為喪失領獎資格不得異議，獎項亦不另外補發。
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參與者於送出資料參加本活動之同時，即同意受本活動辦法及注意事項之規範，如有違反本活動辦法及注意事項者，主辦單位得取消其參加或兌換資格，並對於任何破壞本活動之行為保留法律追訴權。
獎品以最後主辦單位實際提供為準，不得要求更換或兌換現金。
依中華民國稅法規定，中獎價值在新台幣 NT$2,000(含)元以上者，贈與物品的價值將併入得獎者個人綜合所得稅申報，請中獎人配合繳交身分證明及填寫資料作為申報依據。
獎品一經寄送後，如有遺失、損毀等情事發生，主辦單位不負補發之責。
本活動獎品寄送範圍僅限台、澎、金、馬地區，恕不寄送國外地址。
主辦單位保有最終解釋及隨時取消、終止、修改、暫停本活動之權利。

【媒體報導】台大發展「智慧急診」電子檢傷只要2分鐘

2020-12-07/在： All, 媒體報導

📢【媒體報導】

台大發展「智慧急診」電子檢傷只要2分鐘 (自由時報 2020/12/3)

「台大醫院與台灣大學發展「智慧急診」系統，台大資工系教授傅立成、台大醫院急診醫學部主任黃建華、台大醫院急診醫學部醫師蔡居霖共同研發電子檢傷系統，透過人臉辨識，就能進行檢傷中的疼痛分類，準確率達8成以上，預計該系統最快會在2022年正式導入台大醫院急診室。」
全文詳見：http://mahc.ntu.edu.tw/news_view.php?id=114

台灣醫療科技展台大擬將AI導入急診診斷 (中央社 2020/12/3)
「人工智慧導入醫療診斷已成國際趨勢，台大醫學影像與數據人工智慧實驗室（MeDA）主持人王偉仲與台大急診團隊研發出「PXR即時危險自動偵測系統」。王偉仲說，急診患者正因情況緊急，醫師必須在短時間內消化大量資訊、做出診斷，但以往光是照X光到判讀完畢，至少就得花40到60分鐘。
王偉仲指出，未來有了這套系統，醫護人員只要利用可攜式X光機拍下患者影像，就能透過無線網路直接傳送到醫師隨身攜帶的行動裝置，同時透過AI判讀是否為鼻胃管、氣管內管、中央靜脈導管錯置，還是有肺結核、氣胸或肺炎，讓醫師診斷更快更精準，預計明年中旬前將導入臨床使用。」