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@arXiv_csSE_bot@mastoxiv.page
2025-09-09 09:29:02

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity
Zhiyuan Zou, Bangchao Wang, Peng Liang, Tingting Bi, Huan Jin
arxiv.org/abs/2509.05585

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity
In the field of software traceability link recovery (TLR), textual similarity has long been regarded as the core criterion. However, in tasks involving natural language and programming language (NL-PL) artifacts, relying solely on textual similarity is limited by their semantic gap. To this end, we conducted a large-scale empirical evaluation across various types of TLR tasks, revealing the limitations of textual similarity in NL-PL scenarios. To address these limitations, we propose an approac…

@kexpmusicbot@mastodonapp.uk
2025-08-09 13:37:11

🇺🇦 #NowPlaying on KEXP's #90TEEN
Tears for Fears:
🎵 Head Over Heels
#TearsforFears
mijorem.bandcamp.com/track/tea
open.spotify.com/track/0aF9m87

@arXiv_csSD_bot@mastoxiv.page
2025-07-09 08:42:22

Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning
Zengjing Chen, Lu Wang, Chengzhi Xing
arxiv.org/abs/2507.05900

Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning
This study addresses the problem of stable acoustic relay assignment in an underwater acoustic network. Unlike the objectives of most existing literature, two distinct objectives, namely classical stable arrangement and ambiguous stable arrangement, are considered. To achieve these stable arrangements, a laser chaos-based multi-processing learning (LC-ML) method is introduced to efficiently obtain high throughput and rapidly attain stability. In order to sufficiently explore the relay's decisio…

@radioeinsmusicbot@mastodonapp.uk
2025-06-10 12:06:41

🇺🇦 Auf radioeins läuft...
Tristan Brusch:
🎵 Monster
#NowPlaying #TristanBrusch
open.spotify.com/track/2TrcJyf

@netzschleuder@social.skewed.de
2025-09-09 11:00:04

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005). 1706 nodes, 6207 edges. https://networks.skewed.de/net/interactome_stelzl

interactome_stelzl — Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.

@kexpmusicbot@mastodonapp.uk
2025-07-10 00:26:40

🇺🇦 #NowPlaying on KEXP's #DriveTime
Tears for Fears:
🎵 Head Over Heels / Broken (live)
#TearsforFears
dirtyneedlz.bandcamp.com/track
open.spotify.com/track/3IixjNR

@netzschleuder@social.skewed.de
2025-07-09 19:00:04

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005). 1706 nodes, 6207 edges. https://networks.skewed.de/net/interactome_stelzl

interactome_stelzl — Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.

@kexpmusicbot@mastodonapp.uk
2025-06-10 14:08:44

🇺🇦 #NowPlaying on KEXP's #MorningShow
Tears for Fears:
🎵 Head Over Heels / Broken (live)
#TearsforFears
dirtyneedlz.bandcamp.com/track
open.spotify.com/track/4EYDUxc

@netzschleuder@social.skewed.de
2025-07-10 04:00:04

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005). 1706 nodes, 6207 edges. https://networks.skewed.de/net/interactome_stelzl

interactome_stelzl — Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.

@netzschleuder@social.skewed.de
2025-07-10 02:00:04

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005). 1706 nodes, 6207 edges. https://networks.skewed.de/net/interactome_stelzl

interactome_stelzl — Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.