At the corner的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Railway Accidents and Incidents in South-East England

為了解決At the corner的問題，作者D’Enno, Douglas 這樣論述：

This unique volume is a startling record of memorable, sometimes tragic, happenings culled from the railway history of three counties in south-east England: Sussex, Surrey and Kent. Among the extraordinary incidents in West Sussex was the damage to Bognor station by gale force winds in 1897, foll

owed just two years later by a disastrous fire caused by a porter drying his coat on a stove. At Cocking, in 1951, a locomotive plunged into a gully, resulting in the end of goods services from Chichester to Midhurst. In neighbouring Surrey, a drunken driver smashed into the wooden booking office at

Tattenham Corner in 1993. Bramley and New Malden stations were subjected to direct enemy aircraft fire in the Second World War. Tales of murders include the unsolved 1905 case of Mary Sophia Money, thrown to her death from a moving train in Merstham Tunnel, Surrey. In Kent, an employee at Dover Pri

ory Station, Thomas Wells, shot dead his intolerable boss, Edward Walsh, on 1 May 1868. He received the death sentence - and was the first criminal in the country to be hanged privately behind prison walls. With a wealth of terrific images, Douglas d’Enno explores these fascinating, often disastrous

, moments in railway history.

At the corner進入發燒排行的影片

感統體適能課程對學齡兒童視覺動作及反應能力之探究

為了解決At the corner的問題，作者鄭依露 這樣論述：

本研究旨在於感統體適能課程對於學齡兒童視覺動作及反應能力之探究，在不同性別、不同年級之變項中，是否與視覺動作及反應能力表現有差異及相關性。研究受試者為新竹某區30位上過感統體適能課程之ㄧ、二年級學齡兒童；研究工具以拜瑞－布坦尼卡視覺－動作統整發展測驗第六版（VMI）以評估視動統整、視知覺、動作協調等向度，並以STEAMA注意力評估APP來評估視覺注意力及聽覺注意力表現；數據統計分析採用量化研究中描述性統計、獨立樣本t檢定進行分析，找出不同性別、不同年級與視動統整、視知覺、動作協調及注意力之差異程度；採用皮爾森相關積差找出不同性別、不同年級與視動統整、視知覺、動作協調及注意力之相關程度。研究結

果分為以下五點說明:1、 不同性別分析結果得知在MC（動作協調）原始分數則有達顯著差異。在兒童注意力SNAP中的衝動行為、對立違抗及SNAP全量有達顯著差異，視覺正確率加總、視覺錯誤率加總及視覺反應時間數值則有達顯著差異。2、 不同性別在注意力評估APP與視覺-動作統整VMI之相關比較摘要表可知在注意力評估APP與視覺-動作統整VMI之相關比較摘要表可知VMI（視動統整）原始分數與VP（視知覺）原始分數及MC（動作協調）原始分數則為高度相關。視覺動作統整發展VMI與兒童注意力SNAP間則有高度相關。視覺動作統整發展VMI與兒童注意力APP在聽覺正確率加總、聽覺錯誤率加總、視覺反應時

間及視覺衝動次數則有高度相關。3、 不同年級分析結果在VMI（視動統整）原始分數、VP（視知覺）原始分數及原始分數MC（動作協調）未達顯著差異，但在兒童注意力SNAP的各向度中皆因年級增加而使表現有所提升。4、 不同年級在注意力評估APP與視覺-動作統整VMI之相關比較摘要表可知VMI（視動統整）原始分數與VP（視知覺）原始分數及MC（動作協調）原始分數則為高度相關。。視覺動作統整發展VMI與兒童注意力SNAP間則有高度相關。在兒童注意力SNAP與兒童注意力APP間在視覺衝動次數及視覺反應時間則有高度相關。5、 在SNAP加總分數與注意力APP之淨相關數值呈現得知在聽覺正確率

加總與聽覺衝動次數數值為高度相關。聽覺正確率加總與視覺反應時間數值為高度相關。聽覺錯誤率加總與聽覺衝動次數數值為高度相關。聽覺錯誤率加總與視覺反應時間數值為高度相關。

Bali Pocket Precincts: A Pocket Guide to the Island’’s Best Cultural Hangouts, Shops, Bars and Eateries

為了解決At the corner的問題，作者Pace, Alison 這樣論述：

Bali Pocket Precincts is your curated guide to Bali’s best cultural, shopping, spa and dining experiences. Bali is known for its tumbling rice paddies, colorful culture and world-class surf. But delve deeper and you’ll discover that every area of the island has its own distinctive personality. Ar

tistic Ubud is home to centuries-old temples, yoga shalas galore and some of the most inventive plant-based restaurants in the world. On the south coast, Seminyak is all about designer boutiques and vibey beach clubs with swim-up bars and sunset soundtracks. Head north and volcano bagging, rice-terr

ace trekking and exceptional scuba diving all become possibilities. Yet on top of all this, Bali’s popularity as a holiday destination is due, at least in part, to the remarkably warm nature of the locals. Their magical living culture is palpable around every corner, from the incense-infused offerin

gs to the elaborate temple ceremonies. And tourists are welcomed with open arms. As well as detailed reviews and maps for major attractions through to hidden gems, Bali Pocket Precincts includes a selection of field trips that encourage you to get off the beaten path and visit areas further afield i

ncluding North Bali and the neighboring island of Lombok. Slip this guide into your pocket and head off on an adventure, experiencing the most fascinating sights and surrounds like a local.

範例選擇與適應性對比損失函數的利用基於孿生網路之離線混合簽名辨識系統

為了解決At the corner的問題，作者呂宗祐 這樣論述：

本篇論文展示一個基於孿生網路、利用混合架構的離線簽名辨識系統。我們採用殘差網路的結構擔當特徵擷取的模型，使WI模式下的特徵能更有效率的學習。另外，我們使用單層的孿生網路結構擔任WD模式下的分類器，藉此節省儲存空間。為了減少簽名的內在變異度與確保網絡能夠更有效的學習，我們提出選擇範例的方式作為孿生網絡的其中一個輸入，並利用選擇範例過程中得到的資訊，引入範例選擇項來提升模型的準確度。透過使用提出的範例選擇與適應性對比損失函數的系統，比起未使用的系統提高了5.9%。基於GPDS簽名資料庫，此系統能夠得到比當前最新技術還要好的94.61%正確率。