マルチアームグラフィックロンT

両アームに左右非対称のグラフィックを落とし込んだロングスリーブTシャツ。トレンド感のあるデザインがポイント。 素材:コットン 100% サイズ:XS,S,M,L,XL,XXL カラー:BLACK,WHITE,GRAY
着丈袖丈身幅袖口幅
XS: 66cm 58cm 42cm 9.75cm
S: 67cm 60cm 45cm 10cm
M: 68cm 61cm 48cm 10.2cm
L: 70cm 63cm 51cm 10.5cm
XL: 72cm 65cm 54cm 10.8cm
XXL: 73cm 66cm 57cm 11m

 (約/cm)

ゴールドエンブレムロンT

ラメプリントが特徴のロングスリーブTシャツ。 イタリア感のあるシールド型エンブレムがインポート感を演出。 素材:コットン 100% サイズ:XS,S,M,L,XL,XXL カラー:BLACK,WHITE,RED
着丈袖丈身幅袖口幅
XS: 66cm 58cm 42cm 9.75cm
S: 67cm 60cm 45cm 10cm
M: 68cm 61cm 48cm 10.2cm
L: 70cm 63cm 51cm 10.5cm
XL: 72cm 65cm 54cm 10.8cm
XXL: 73cm 66cm 57cm 11m

 (約/cm)

ホログラムグラフィックロンT

ホログラムプリントが特徴のロングスリーブTシャツ。 素材:コットン 100% サイズ:XS,S,M,L,XL,XXL カラー:BLACK,WHITE,NAVY
着丈袖丈身幅袖口幅
XS: 66cm 58cm 42cm 9.75cm
S: 67cm 60cm 45cm 10cm
M: 68cm 61cm 48cm 10.2cm
L: 70cm 63cm 51cm 10.5cm
XL: 72cm 65cm 54cm 10.8cm
XXL: 73cm 66cm 57cm 11m

 (約/cm)

ボンディングトラックジップフーディー

防風性の高いボンディングトラックジップフーディ。2枚の生地を貼り合わせたボンディング生地は防風効果が高い。袖のロゴラインプリントがインパクト大。 同素材のパンツと合わせたセットアップスタイルがオススメです。 素材:ポリエステル 100% サイズ:XS,S,M,L,XL,XXL カラー:BLACK,NAVY
着丈裄丈身幅袖口幅
XS: 62cm 83.5cm 46cm 9.75cm
S: 63cm 84.5cm 49cm 10cm
M: 64cm 85.5cm 52cm 10.5cm
L: 66cm 87.5m 55cm 11cm
XL: 68cm 89.5cm 58cm 11.5cm
XXL: 69cm 90.5cm 61cm 12cm

 (約/cm)

ボンディングトラックパンツ

2枚の生地を貼り合わせたボンディング生地は防風効果が高く、タイト目なシルエットが特徴。 右前ポケットにはファスナー付。 同素材のトップスアイテムとセットアップスタイルがオススメ。 素材:ポリエステル 100%, サイズ:XS,S,M,L,XL,XXL カラー:BLACK,NAVY
ウエストヒップ股上股下
XS: 66cm 97cm 27cm 69cm
S: 69cm 100cm 28cm 70cm
M: 72cm 103cm 29cm 71cm
L: 75cm 106cm 30cm 73cm
XL: 78cm 109cm 31cm 75cm
XXL: 81cm 112cm 32cm 76cm

 (約/cm)

ボンディングトラックジャケット

防風性の高いボンディングトラックジャケット。2枚の生地を貼り合わせたボンディング生地は防風効果が高い。袖のロゴラインプリントがインパクト大。 同素材のパンツと合わせたセットアップスタイルがオススメです。 素材:ポリエステル 100% サイズ:XS,S,M,L,XL,XXL カラー:BLACK,NAVY
着丈裄丈身幅袖口幅
XS: 62cm 83.5cm 46cm 9.5cm
S: 63cm 84.5cm 49cm 10cm
M: 64cm 85.5cm 52cm 10.5cm
L: 66cm 87.5m 55cm 11cm
XL: 68cm 89.5cm 58cm 11.5cm
XXL: 69cm 90.5cm 61cm 12cm

 (約/cm)

New AI-Driven Discovery Experience

We love the Web because it is an open and distributed network that offers everyone the freedom and control to publish and follow what matters to them.

We also love the web because it has enabled a new generation of content creators (Ben Thompson, Bruce Schneier, Tina Eisenberg, Seth Godin, Maria Popova, etc.). Those independent thinkers continuously explore the edge of the known and share insightful and inspiring ideas with their communities.

Connecting people to the best sources for the topics that matter to them has been core to our mission since the very start of Feedly.

But discovery is a hard problem. The web is organic, a reflection of the global community’s changing needs and priorities. There are millions of sources across thousands of topics and we all have a different appetite when it comes to feeding our minds.

About twelve months ago, we created a machine learning team to see if the latest progress in deep learning and natural language processing could help us crack this nut.

Today, we are excited to give you a preview of the result of that work with the release of the new discovery experience in the Feedly Lab app (Experience 06).

Two thousand topics

The first discovery challenge is to create a taxonomy of topics.

You can think of Feedly as a rich graph of people, topics, and sources. To build the right taxonomy, we started with the raw data on all of Feedly’s sources. We had to create a model to clean, enrich, and organize that data into a hierarchy of topics. Learn more about the data science behind this.

The result is a rich, interconnected network of two thousand English topics. And it’s mapped well with how people expect to explore and read on the Web.

Some topics are broad: tech, security, design, marketing. Some are very niche: augmented reality, malware, typography, or SEO.

On the discovery homepage, we showcase thirty topics based on popular industries, trends, skills, or passions. You can access all of the topics in Feedly via the search box.

The fifty most interesting sources

The second discovery challenge is to find the fifty most interesting sources someone researching any topic might want to follow.

Ranking sources is hard because not all sources are equal. In tech as an example, you have mainstream publications like The Verge or TechCrunch, expert voices like Ben Thompson, and lots of B-list noisy sources which don’t add much value.

In addition, for niche topics like virtual reality, some sources are specific to VR while others cover a range of related topics.

To solve this challenge, we created a model which looks at sources through three different lenses:

  • follower count
  • relevance (how focused is the source on the given topic)
  • engagement (a proxy for quality and attention)

The outcome is new search result cards. You can explore the fifty most interesting sources for a given topic and sort them using the lens that is most important to you.

Neighborhoods

One of the benefits of the new topic model is that the 2,000 topics are organized in a hierarchy. This makes it easy for you to zoom in or out and explore many different neighborhoods of the Web.

For example, from the cybersecurity topic, you can jump to a list of related topics that let you dig deeper into malware, forensics, or privacy.

One more thing…

We have done a lot of research over the last four years to understand how people discover new sources. One insight we learned is that people often co-read certain sources. For example, if you are interested in art, design, and pop culture and you follow Fubiz, there is a high chance that you also follow Designboom.

With that in mind, we spent some time creating a model that learns what sources are often co-read. The idea is that a user could enter a source that they love and discover another source they could pair it with.

You can learn more about the machine learning model (we call it feed2vec) powering this experience through the article Paul published here.

As a user, you can access this feature by searching in the discover page for a source you love to read. The result will be a list of sources which are often co-read with that source.

Thank you!

I would like to thank Paul, Michelle, Mathieu, and Aymeric for the great research work they did to take this project from zero to one. People who have tried to tackle discovery know that it is a very hard challenge and the results of this project have been very impressive.

We would also like to thank the community for participating in the Battle of the Sources experiment. Your input was key in helping us learn how to model the source ranking. We are going to continue to invest in discovery and we look forward to continuing to collaborate with you.

We would also like to thank Dan Newman, Daron Brewood, Enrico, Joey, Lior, Paul Adams, Ryan Murphy, and Joseph Thornley from the Lab for reviewing an earlier version of this article.

名医が勧める「男性ホルモン値を上げる10カ条」とは?

2018年7月21日、東京・六本木で人生100年時代のための最新健康・美容情報体感イベント「スマートリィ・エイジングEXPO」(主催・日経ヘルス、日経グッデイ、日経BP総研)が開催された。そこで発表された講演の中から、順天堂大学大学院医学研究科泌尿器外科学教授の堀江重郎さんによる「ホルモンを味方につける男性のためのアンチエイジング入門」をお届けする。

EDになったら心臓疾患に注意を

日経Goodayの男性読者なら誰もが気になる「前立腺がん」。『医者がマンガで教える 日本一まっとうながん検診の受け方、使い方』の著者であり、マンガも描ける医師である近藤慎太郎さんに解説していただきました。