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Market intelligence platform Kaiko Analytics reports that new competitors are chipping away at Tether’s (USDT) stablecoin dominance. In a new report titled “Tether Loses Market Share,” Kaiko Analytics says that the stablecoin issuer’s market share over centralized exchange platforms (CEXs) has dipped 13% year-to-date (YTD) due to the growth of rival dollar-pegged digital assets, such […] The post New Competitors Eating Away At Tether’s USDT Stablecoin Dominance: Kaiko Analytics appeared first on The Daily Hodl.

:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: [email protected]); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. ::: Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 6 Conclusion In this paper, we introduce Multi-EuP, a novel dataset for multilingual information retrieval across 24 languages, collected from European Parliament debates. The demographic information provided by the Multi-EuP dataset serves a dual purpose: not only does it contribute to multilingual retrieval tasks, but it also holds significant potential for advancing research in the realm of fairness and bias. This dataset can play a pivotal role in investigating issues of equitable representations and mitigation of biases within document ranking settings. \ \ Multi-EuP facilitates diverse information retrieval (IR) scenarios, encompassing one-vs-one, one-vs-many, and many-vs-many settings. We demonstrated the utility of Multi-EuP as a benchmark for evaluating both monolingual and multilingual IR. Our study reveals the presence of language bias in multilingual IR when employing BM25. We further validate the effectiveness of mitigating this bias through the strategic implementation of whitespace as a language tokenizer. \ We propose to conduct future work in three main areas. First, we intend to expand our investigation of language bias to encompass a broader range of ranking methods, including neural methods such as mDPR (Zhang et al., 2021), mColBERT (Lawrie et al., 2023) and PLAID-X(Santhanam et al., 2022). Second, we will expand the dataset by developing an automated API to retrieve data published by the European Parliament (EP), thereby ensuring realtime synchronization of our dataset. Lastly, our current experiments have explored language bias only, but we plan to further investigate gender bias, age bias, and nationality bias. Limitations The limitations of the Multi-EuP dataset are notable but navigable. Primarily, the temporal coverage of the dataset is confined to the past three years. This temporal constraint arises due to the fact that, preceding 2020, documents released by the EU were predominantly available in mono-lingual versions only. However, a potential remedy lies in the amalgamation of the Europarl (Koehn, 2005) collection, enabling a more comprehensive and holistic MultiEuP dataset. \ Furthermore, it is worth noting the domain skew of the dataset, in that Multi-EuP inevitably centers on political matters. While this presents challenges, particularly in terms of the intricate nuances of political language, it inherently serves as an excellent foundational stepping stone for delving into the intricacies of multilingual retrieval. We believe, however, that this dataset can serve as a launching pad for broader explorations encompassing crossdomain and open-domain transfer learning scenarios, thus contributing to the broader landscape of language understanding and retrieval. Ethics Statement The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. [8] Acknowledgements This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. We would like to thank George Buchanan for providing valuable feedback. References Luiz Henrique Bonifacio, Israel Campiotti, Roberto de Alencar Lotufo, and Rodrigo Frassetto Nogueira. 2021. mMARCO: A multilingual version of MS MARCO passage ranking dataset. CoRR, abs/2108.13897. \ Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. 2021. MultiEURLEX - a multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6974–6996, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. 8 https://eur-lex.europa.eu/cont \ Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the Association for Computational Linguistics, 8:454–470. \ Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for opendomain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics. \ Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR, abs/2004.12832. \ Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Machine Translation Summit X: Papers, pages 79–86, Phuket, Thailand. \ Dawn Lawrie, Eugene Yang, Douglas W. Oard, and James Mayfield. 2023. Neural approaches to multilingual information retrieval. arXiv cs.IR 2209.01335. \ Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, JhengHong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. https://github.com/ castorini/pyserini. \ Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. CoRR, abs/1611.09268. \ Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized machine translation: Preserving original author traits. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics. \ Razieh Rahimi, Azadeh Shakery, and Irwin King. 2015. Multilingual information retrieval in the language modeling framework. Information Retrieval Journal, 18:246–281. \ Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM ’22, page 1747–1756, New York, NY, USA. Association for Computing Machinery. \ Jörg Tiedemann and Santhosh Thottingal. 2020. OPUSMT – building open translation services for the world. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. \ Eva Vanmassenhove and Christian Hardmeier. 2018. Europarl datasets with demographic speaker information. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, page 391, Alicante, Spain. \ Denny Vrandeciˇ c and Markus Krötzsch. 2014. ´ Wikidata: A free collaborative knowledge base. Communications of the ACM, 57:78–85. \ Konrad Wojtasik, Vadim Shishkin, Kacper Wołowiec, Arkadiusz Janz, and Maciej Piasecki. 2023. BEIRPL: Zero shot information retrieval benchmark for the Polish language. arXiv cs.IR 2305.19840. \ Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1253–1256. \ Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. 2021. Mr. TyDi: A multi-lingual benchmark for dense retrieval. arXiv cs.CL 2108.08787. A. Appendix \ \ [8] https://eur-lex.europa.eu/content/ legal-notice/legal-notice.html

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:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: [email protected]); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. ::: Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 5 Language Bias Discussion In light of our findings in a one-vs-many setting, we were keen to delve further into the underlying causes of the disparity between languages. 5.1 Bias Detection Language bias is likely if the query language aligns better with one document language than another. As mentioned earlier, Pyserini supports different tokenizers, specifically language-specific tokenizers or simple whitespace tokenization. Therefore, in the one-vs-many setting, we analyze the composition of the top-100 rankings for the 100 topics. During indexing of the document collection, we used the simple whitespace tokenizer, given the multilingual nature of the collection. However, over the queries during retrieval, we employed two different tokenizers — a language-specific tokenizer, and the whitespace tokenizer. \ We conducted a correlation analysis between the language of the topics and the language of the top 100 relevant documents. From Table 2, we can see that relevance judgments in our test cases are consistent across languages, ensuring uniformity in the correlation matrix within the test set. However, Figure 2 reveals that both approaches generate strong language bias. In both cases, the query language aligns better with documents in its own language than others. The right plot appears to show that languages from the same family has strong correlation (e.g., PL, CS) and (IT, ES) since they may have some shared vocabulary. 5.2 Collection Distribution Factors Initially, we hypothesized that the disparity for each language may be a contributing factor to this bias. Figure 3 presents the regression line between the number of documents in a given language and MRR, which explains much of the variation across languages. \ However, note the outlier above the regression line (Polish: PL), which has a substantial number of documents but surprisingly low MRR performance. We refer to this phenomenon as a “BM25 unfriendly” language. According to Wojtasik et al. (2023), the main reason for the low performance of Polish lies in its highly-inflected morphology, giving rise to a a multitude of word forms per lexeme, including inflections of proper names, and complex morphological structure. In such cases, lexical matching is less effective than in other morphologically-simpler languages. Furthermore, LUCENE 8.5.1 API does not have a language-specific tokenizer for Polish. Conversely, languages below the regression line can be termed “BM25 friendly” languages, as they require fewer documents to achieve higher MRR in retrieval. 5.3 Language Tokenizer Factors Secondly, we speculated that the choice of language-specific Analyzer in LUCENE might be a contributing factor, as it influences word tokenization, token filter, synonym expansion and other processing. [7] To investigate this, we conducted a controlled experiment in the one-vs-many setting. When indexing the collection, given the multilingual nature of the collection, we employed whitespace as the tokenizer. However, over the queries, we experimented with either a language-specific tokenizer or whitespace tokenizer. We then compared the linear regression of MRR against the number of documents in Figure 3. On the right side of the plot, we can see a strong correlation when using whitespace tokenization for both the collection and the queries, reducing language bias. \ Furthermore, when transitioning from languagespecific tokenizers to whitespace tokenizers, the overall MRR across all languages declined modestly, from 15.02 to 14.18. That is, the original performance level was largely preserved, but language bias was diminished in using simple whitespace tokenization. \ \ [7] https://lucene.apache.org/core/800/core/ org/apache/lucene/analysis/package-summary.html# package.description

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:::info This paper is available on arxiv under CC 4.0 license. Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: [email protected]); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. ::: Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 4 Experiments and Findings \ We conduct preliminary experiments in both one-vs-one and one-vs-many settings, as described above. \ Methods We base our experiments on BM25 with default settings (k1 = 0.9 and b = 0.4), a popular traditional information retrieval baseline. Our implementation is based on Pyserini (Lin et al., 2021), which is built upon Lucene (Yang et al., 2017). Notably, the latest LUCENE 8.5.1 API offers language-specific tokenizers, [6] covering 19 out of the 24 languages present in Multi-EuP. For the remaining languages — namely Polish (PL), Croatian (HR), Slovak (SK), Slovenian (SL), and Maltese (MT) — we use a whitespace tokenizer. \ \ \ MRR@k denotes MRR computed at a depth of k results. Note that the higher the number the better, and that a perfect retriever achieves an MRR of 1 (assuming every query has at least one relevant document). The choice of setting k = 100 aligns with prior endeavors over MS MARCO (Nguyen et al., 2016). 4.1 Monolingual IR (one-vs-one) Experimental Setup We first present results over Multi-EuP in a monolingual setting across the 24 different languages. Specifically, we evaluate single-language queries against documents in the same language. In this configuration, we partitioned our original collection of 22K documents into 24 distinct language-specific sub-collections. Table 2 presents the results broken down across languages. \ Results and Findings Table 2 presents the MRR@100 results for BM25 on Multi-EuP. There are two high-level findings: \ First, Multi-EuP is a relatively easy benchmark for monolingual information retrieval, as the MRR@100 is always around 40 or greater (meaning that the first relevant document is in the top-3 results on average). Indeed, the average MRR across the 24 test languages is 49.61. While direct comparison is not possible, it is noteworthy that for Mr. TYDI, the average MRR is 32.1 across 11 languages. Part of this difference can be attributed to the fact that our relevance judgments are not as sparse as theirs. \ \ \ Second, similar to Mr. TYDI, direct comparison of absolute scores between languages is not meaningful in a monolingual setting, as the document collection size differs. 4.2 Multilingual IR (one-vs-many) Experimental Setup In contrast to Mr. TYDI (Zhang et al., 2021), Multi-EuP supports one-vs-many retrieval, and allows us to systematically explore the effect of querying the same document collection with the same set of topics in different languages. This is because we have translations of the topics in all languages, documents span multiple languages, and judgments are cross-lingual (e.g., English queries potentially yield relevant Polish documents). For this experiment, we use the default whitespace tokenizer in the Pyserini library. \ Results and Findings Table 2 presents the MRR results for BM25 for multilingual information retrieval on 100 topics from the Multi-EuP test set. It’s worth noting that these topics have translationequivalent content in the different languages. Consequently, the one-vs-many approach allows us to analyze language bias. We made several key observations: \ First, unsurprisingly, having more relevance judgments tends to improve ranking accuracy. Therefore, when comparing English topics with other languages, English exhibits notably better MRR performance. \ Second, despite there being consistency in the topics, document collection, and relevance judgments, there is a significant disparity in MRR scores across languages, an effect we investigate further in the next section. \ \ [6] Provided by the Anaylzer package in LUCENE. https: //lucene.apache.org/core/851/analyzers-common/ index.html

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Coinbase, the renowned cryptocurrency exchange headquartered in the United States, has recently made waves in the digital currency market with its decision to incorporate QCAD into its platform. This strategic move underscores Coinbase’s commitment to expanding its asset offerings, providing its users with a wider array of trading options. What sets QCAD apart from the […]

BlockDAG distinguishes itself in the competitive blockchain technologies. By leading among the top 5 most eco-friendly cryptocurrencies, this innovative platform offers user-friendly tools designed to minimize ecological footprints. Alongside BlockDAG, other notable cryptos like Solana, Cardano, Algorand, and Nano each contribute uniquely to the sustainable blockchain ecosystem with their energy-efficient solutions. Investing in these technologies means […] The post Top 5 Most Eco-Friendly Cryptocurrencies: BlockDAG Stands Out Amidst SOL, ADA, ALGO & Nano With Its Energy-Efficient Solutions appeared first on Live Bitcoin News.

As May 1st started, Bitcoin (BTC) faced a new correction that made the price stumble under the $60,000 support level. The flagship cryptocurrency has seen several retraces during this bull cycle, with BTC swiftly recovering the crucial support zones each time. However, in the past 24 hours, the largest cryptocurrency by market capitalization has struggled to regain its momentum. Some analysts believe Bitcoin’s bottom this cycle might be in as this correction officially became its deepest retrace. Related Reading: If History Repeats, This Is How Bitcoin Price Will Perform In The Next 6 Months Is The Bitcoin Bottom Here? In the early hours of Tuesday, Bitcoin started to plunge from the $64,000 price range. As the day continued, BTC prolonged its fall to briefly trading around $59,958 – $59,191 before recovering. This time, the recovery didn’t last long as Bitcoin’s price resumed its downward trajectory to $57,000. In an X thread, crypto trader Milkybull examined some data suggesting the bottom might finally be in. According to the analyst, BTC is “following the 2017 PA.” This would suggest that “either the bottom is in or close.” Moreover, he urged investors to remember that while good news “usually signals the top,” bad news signals a bull market’s bottom. In the thread, the trader pointed out that the Bitcoin Bull Market Support Band indicator historically serves strong support during BTC’s bull cycles. As a result, he considers that the flagship cryptocurrency might “wick through the support and bounce back.” 2024's Bitcoin Bull Market Support Band indicator resembling 2017's. Source: MilkybullCrypto on X According to the analyst, Bitcoin and global liquidity are also strongly correlated, with BTC currently at a level it has previously bounced back from. These bouncebacks initiated “huge rallies across the crypto market” in October 2022 and October 2023. Lastly, the thread highlighted that Bitcoin “is at a critical decision point for the local bottom.” The trader considers that while some call for a $48,000 bottom, the $51,000 support level might be where BTC bounces back to resume its cycle to this cycle’s top. Bitcoin’s Deepest Retrace This Cycle According to crypto analyst and trader Rekt Capital, this correction has officially been the deepest BTC retrace this cycle. Per the post, today’s -23.64% retrace surpasses the -22.91% retrace seen in February 2023. The analyst also compared this cycle’s “Post-Halving” pullback to 2016’s. Moreover, the trader considers that this bullish cycle might be more similar to the 2016 one than investors think. #BTC We are here (orange circle) And we've seen Post-Halving pullbacks like this in the past (e.g. 2016)$BTC #Bitcoin #BitcoinHalving pic.twitter.com/OvOcmJTzFk — Rekt Capital (@rektcapital) May 1, 2024 Previously, Rekt Capital listed three reasons these two cycles might be similar. Per the trader, the resemblances include the “Pre-Halving Re-Accumulation Range Breakout,” the “Pre-Halving Retrace Beginning,” and the “Similar Initial Reaction after the beginning of the Pre-Halving Retrace.” After today’s retrace, the analyst added the “Continued downside in the three weeks after the Halving” as a fourth similarity between the 2016 and 2024 cycles. Like eight years ago, Bitcoin faces an “additional downside below the Range Low of its Re-Accumulation Range” in the three-week window after Bitcoin’s “Halving.” Moreover, the analyst suggests that the current price development comes “as no surprise,” as it mirrors 2016’s “post-Halving Danger Zone.” As of this writing, the flagship cryptocurrency is trading at $57,794.89. This correction represents a 6.2% drop in the past 24 hours. Similarly, BTC is registering 13.4% and 17.7% price decreases in the weekly and monthly timeframes. Related Reading: Bitcoin Price Nosedives, Can Bulls Save The Key Support at $60K? Bitcoin's price performance in the weekly chart. Source: BTCUSDT on TradingView Featured Image from Unsplash.com, Chart from TradingView.com

The price of bitcoin is tumbling in the wake of record highs and the “halving” event. The value of the cryptocurrency has now fallen 17 per cent ...

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Acala Network, a leading platform within the Polkadot ecosystem, unveils its groundbreaking Sinai Upgrade, marking a significant milestone in its Acala 2.0 initiative.  This upgrade is poised to reshape the decentralized finance (DeFi) landscape by expanding the platform’s multichain capabilities, heralding a new era of interoperability across blockchain networks. At the core of the Sinai […]

As the cryptocurrency market anticipates the next Bitcoin (BTC) halving, a significant event meant to reduce the block reward and potentially increase the coin’s price, some are shifting focus. The spotlight is turning towards promising altcoins like Ethereum (ETH) and the innovative Algo token, Option2Trade (O2T), gaining traction as potentially more attractive assets in the […]

Audible Tests Lower-Priced Plan in Possible Response to Spotify  Bloomberg

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Huawei Lab Barred by US Regulators as Part of Crackdown on China  Bloomberg

BONK becomes the fifth-largest memecoin, overtaking Floki Inu in market valuation.

Tesla (TSLA) Interns Are the Latest Target After Elon Musk’s Layoffs  Bloomberg

Record Immigration Levels Collide With Housing Shortages  Bloomberg

Oregon joins five other states that have delicensed Binance.US after former Binance CEO Changpeng Zhao pleaded guilty to felony charges.

Bitcoin’s prolonged correction is driven by a sharp decrease in demand for nearly all investor cohorts.

Fed Day: Jerome Powell Should Listen to Gandalf About 2% Inflation  Bloomberg

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