#PatternMatching in #Java looks clean—until legacy switch rules get in the way. Subtle edge cases, null handling, and type quirks can turn elegance into bugs. @cayhorstmann breaks down what to watch out for.
Avoid the traps and write safer code: https://javapro.io/2026/03/24/effective-pattern-matching-2026-edition/
Parsing and evaluating expressions in #Java doesn’t have to be messy. Balkrishna Rawool shows how to simplify expression trees using ADTs and #PatternMatching. Clean, idiomatic, and readable.
Read the full Guide: https://javapro.io/2025/11/11/algebraic-data-types-and-pattern-matching-java/
It’s Friday and in an attempt to remember what it was like when I did music cognition research here is possibly the article I am proudest of: it was a huge challenge to work on but I think it was a pretty good stab at better understanding a complex human behaviour AND testing some algorithmic approaches to solving a musical problem. Hank and I nearly came to blows because of our radically different backgrounds and personality types but all was well in the end. Fortunately Peter was good at keeping us on track ;-)
https://link.springer.com/article/10.3758/BF03200827#preview
(no paywall)
#music #InformationProcessing #PatternMatching #algorithms #modeling #CognitiveScience #MusicTechnology #memories
In order to study aspects of music performance, one has to find correspondences between the performance data and a score. Locating the corresponding score note for every performance note, calledmatching, is therefore a common task. An algorithm that automates this procedure is called amatcher. Automated matching is difficult because performers make errors, performers use expressive timing, and scores are frequently underspecified. To find the best match, most matchers use information about pitch, temporal order, and the number of matched notes. We show that adding information about the musical structure of the score gives better results. However, we found that even this information was insufficient to identify some types of performance errors and that a definition of best match based only on the number of matched notes is sometimes problematic. We provide some suggestions about how to achieve greater improvements.
Still wasting time on constructor workarounds just because super() must come first? #Java25 finally removes that 30-year limitation. @bazlur_rahman shows what it means for cleaner initialization. Worth the upgrade?
Match Expression vs Switch: Pattern Matching Showdown
PHP 8's match expression vs JavaScript switch. Which language's control flow is more elegant? You won't believe this!
#php #javascript #phpvsjavascript #matchexpression #switchstatement #patternmatching #php8 #programmingcomparison #codecomparison #controlflow #viralcoding
#Java developers have complained about boilerplate for decades. #Java25’s compact source files aim to change that — making simple programs truly simple. @bazlur_rahman explains what’s new. Curious if this improves your workflow?
Read more: https://javapro.io/2026/02/24/javas-productivity-trifecta-compact-sources-flexible-constructors-and-advanced-pattern-matching/
#PatternMatching
fly51fly (@fly51fly)
SoftMatcha 2: 조(트릴리언) 단위 규모 코퍼스에서 동작하는 빠르고 유연한 패턴 매처를 제안하는 연구. M. Yoneda 등(University of Tokyo, Kyoto University 등 공동연구, 2026, arXiv). 초대형 말뭉치에서의 효율적 검색·패턴 매칭을 목표로 한 도구/알고리즘 발표.
Java 17: Die relevanten Features der neuen LTS-Version
RE: https://mastodon.social/@h4ckernews/115923499147477767
A very interesting article on how LLMs might actually work. I am sceptic about the overextension of the work to cognitive sciences but the authors make a good point. I wonder what the actual cognitive scientists might say.
#llm #ai #science #cognitiveScience #cognition #arxiv #preprint #patternMatching
We report on an astonishing ability of large language models (LLMs) to make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings, e.g., translating "He dwushed a ghanc zawk" to "He dragged a spare chair". This result addresses ongoing controversies regarding how to best think of what LLMs are doing: are they a language mimic, a database, a blurry version of the Web? The ability of LLMs to recover meaning from structural patterns speaks to the unreasonable effectiveness of pattern-matching. Pattern-matching is not an alternative to "real" intelligence, but rather a key ingredient.
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