BOE Meeting May 2024: Investors Now Bet on Even Chance of June Rate Cut  Bloomberg

Barry Sternlicht Says Miami Is Hamstrung by 'Impossible' School Access  Bloomberg

FINTRAC has imposed a substantial fine on cryptocurrency exchange Binance for allegedly violating anti-money laundering regulations.

VanEck MEMECOIN index tracks six top meme coins, including Shiba Inu, Dogecoin, Dogwifhat, Bonk, Pepe, and Floki.

NOAA Predicts Summer 2024 Will Be Hot; Small Businesses Are Bracing  Bloomberg

Trump SPAC Investor Convicted in Insider-Trading Case  Bloomberg

Private Equity Is Too Big to Be So Secretive  Bloomberg

CoinGecko recently disclosed that the most crypto-degen country is the United States which has 16.8% of world interest in low-cap crypto on on-chain DEX trackers. It shows that there is a strong burning passion in the US for such coins, which are usually referred to as “shitcoins” or memecoins. Next in line is the United […]

How the Buyout Business Is Coming Up With IPO Alternatives  Bloomberg

Blockchain technology, the backbone of digital currencies like Bitcoin, presents a promising frontier. HB 3594 seeks to make Oklahoma a sanctuary for ...

Nuclear Power Company Holtec International at Center of Revival  Bloomberg

Affirm's Levchin Says BNPL Risks Are 'Exaggerated'  Bloomberg

Here's the reason behind memecoins' $50.8 billion market cap and 1300% returns.

Max Keiser predicts a significant rise in Bitcoin's value, even as current market trends show some bearish signs.

The spike in RNDR’s social dominance and whale accumulation has led to a double-digit rally in its price.

As the clock ticks closer to the end of today, May 3, the cryptocurrency market braces itself for potential upheavals, with roughly $2.4 billion worth of Bitcoin and Ethereum options set to expire. This significant event could catalyze notable shifts in market dynamics, steering the trajectory of Bitcoin and Ethereum prices in the near term. Notably, Options contracts in the crypto sphere allow traders to hedge against price volatility or speculate on future price movements without directly holding the assets. Typically structured as either calls or puts, these contracts enable buying (call) or selling (put) at predetermined prices within a specified timeframe. Related Reading: Bitcoin’s Make-Or-Break Moment: Trading Guru Predicts Rally Amid Market Uncertainty As the expiry date approaches, movements within these contracts tend to introduce heightened volatility into the market, given the adjustments traders make to hedge their positions or capitalize on anticipated price movements. Market Mechanics And Sentiment Indicators The mechanics of options trading offer insights into market sentiment, primarily through analyzing the put/call ratio. This ratio gauges the market’s bullish or bearish stance, depending on whether the volume of call options (betting on price rises) outweighs put options (betting on price drops) or vice versa. Presently, the put-to-call ratio for Bitcoin stands at a relatively low 0.5, suggesting a bullish sentiment as more traders bet on rising prices with the maximum pain point—a price level causing maximum trader losses—at about $61,000 and a notional value of $1.4 billion. In contrast, Ethereum’s options market is also teeming with activity, marked by the upcoming expiry of contracts valued at around $1 billion. With a put-to-call ratio of 0.37, the sentiment leans even more bullish than Bitcoin, indicating stronger trader confidence in Ethereum’s price performance. Ethereum’s designated maximum pain point sits at $3,000, aligning with key psychological and technical support levels. May 3 Options Data 23,000 BTC options are about to expire with a Put Call Ratio of 0.49, a Maxpain point of $61,000 and a notional value of $1.4 billion. 330,000 ETH options are due to expire with a Put Call Ratio of 0.36, Maxpain point of $3,000 and notional value of $1… pic.twitter.com/mEA4PV98C3 — Greeks.live (@GreeksLive) May 3, 2024 Implications And Bitcoin Insights Historically, the expiration of such a voluminous cache of options has precipitated abrupt price fluctuations in the spot markets for Bitcoin and Ethereum. This is attributed to the large-scale repositioning by institutional and retail investors in anticipation of or in response to the expiry outcomes. Related Reading: Bitcoin Hits ‘Danger Zone’: Peter Schiff Warns Of ‘Do or Die’ Scenario These strategic movements are particularly pivotal when both cryptocurrencies recover from recent pullbacks. GreeksLive noted: The current point of sustained sideways trading is unlikely, no rebound is bound to be a downward relay, the giant whale on the lack of confidence in the market, Block trading is worth strengthening attention. Meanwhile, Bitcoin appears to be recovering from the recent downturn with a 5.4% increase in the past day, momentarily piercing the $60,000 mark, signaling a potential resumption of its upward march. Similarly, Ethereum has shown resilience, climbing above the $3,000 threshold with a modest 3% gain. These upticks coincide with broader market analyses like that of Marco Johanning, a well-known crypto analyst and founder of The Summit Club, suggesting that foundational bullish sentiments remain intact despite recent corrections. Featured image from Unsplash, Chart from TradingView

Solana-based tokenization platform AgriDex raises $5 million, plans token airdrop  The Block

AI Investment Opportunities on Cooper, Data Centers Seen by Experts  Bloomberg

Former Bed Bath & Beyond Sues Hudson Bay for $300 Million in Trading Profits  Bloomberg

The U.S. SEC has continued its crypto crackdown this year, but the agency’s current leverage may also be its biggest weakness in months to come. 

[PRESS RELEASE – New York, New York, May 9th, 2024] Arch Raises $7M Led By Multicoin Capital To Build The First Bitcoin-Native Application Platform ArchVM Enables Bridgeless Applications On Bitcoin, The World’s Largest And Most Secure Blockchain HONG KONG, MAY 8, 2024 — Arch Labs, the inventors of Arch, a Bitcoin-native application platform that unlocks […]

Espresso partners with Polygon Labs to develop AggLayer, an interoperability solution across rollups using proof aggregation and transaction ordering guarantees. The post Espresso and Polygon Labs partner to address rollup interoperability appeared first on Crypto Briefing.

Big Take DC: The Harsh Economics of Extreme Heat  Bloomberg

May 9, 2024 (Investorideas.com Newswire) Investorideas.com, a go-to platform for big investing ideas, reports on trading for Canopy Growth Corporation (TSX: WEED) (NASDAQ: CGC), a leading North American cannabis and consumer packaged goods company dedicated to unleashing the power of cannabis to improve lives.

As self-driving and autonomous vehicle technologies advance, they should make driving safer. But all available data suggest a dangerous road to that future.

:::info Authors: (1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. ::: Table of Links Abstract and Intro Related Works DIALSTORY Dataset Proposed Tasks Methodology Experiments Discussion Future Work Conclusion Limitations and References 6 Experiments 6.1 Masked Dialogue Generation Implementation Details To conduct experiments on the masked dialogue generation task, we decide the hyper-parameters based on the performance of the validation set. We train Shao et al. (2021)’s BART model for 4.6 epochs with a 1e-4 learning rate for 1 day, and for our model, we train it for 5.6 epochs with a 1e-4 learning rate for 1 day. All baselines and our model are trained using the Adam optimizer. \ \ \ During the training process for our method, we computed the selection coverage of characters within a single story. And it showed that in every 1000 training steps, the coverage of different characters ranged from 98.64% to 99.00%, which meant nearly all the characters are selected during training, and all the characters contributed to the generated dialogue. It further proved that the argmax in Eq. 3 operation doesn’t break the gradient progress when training for this task. \ Automatic Evaluation Following previous works, we use several standard, widely used automatic evaluation metrics. We use BLEUn (Papineni et al., 2002) to measure the average word overlap between each generated and groundtruth dialogue turn (n=1,2), and Distinct-n (Li et al., 2015) to evaluate n-gram diversity of generated dialogue turns (n=2,3,4). \ \ To be more specific, for the coherence classifier, we construct the training and validation sets by randomly shuffling the order of dialogue turns and keeping other content in the correct order. We regard the perturbed story as a negative example and the original story as a positive example. We sample another 195k stories (except those in DIALSTORY) from the novels of Guan et al. (2022) to construct the training set (190k examples) and the validation set (5k examples). We train the model for 4 epochs with a 2e-5 learning rate and a 16- batch size, using the Adam optimizer. During the evaluation, we consider an example coherent when the probability of being coherent predicted by the classifier is greater than 0.5. We use the ratio of outputs (along with the input) that are classified as coherent by the classifier to all generated outputs as the coherence score. \ \ The result of the automatic evaluation is presented in Table 3. According to the table, compared to the BART baseline, our model consistently generates more word overlaps with ground truth and achieves better diversity under the guidance of character representations, which means our model can generate more diverse but not commonplace responses. \ Figure 3 plots the coherence score varying with the number of masked dialogue turns. The result shows that our model gets a higher coherence score than BART when required to generate more than seven turns of dialogue in one story. \ Manual Evaluation We conduct a pairwise comparison between our model and the BART baseline. We randomly select 100 examples from the test set. For each pair of outputs along with the input, we ask three annotators to give a preference (win, lose and tie) in terms of fluency, coherence, and informativeness. All the annotations are native Chinese speakers. We adopt majority voting to make final decisions among the annotators. The three aspects of manual evaluation are as follows: \ Fluency: Grammatical correctness and intra-sentence linguistic quality. \ Coherence: Inter-sentence relatedness, causal and temporal dependencies. We judge the coherence between the story and a dialogue turn by following the criterion in Table 5. We add the scores of all the generated dialogue turns in a story to get the overall coherence score of the story, which is then used to compare with each other. \ Informativeness: Interesting, diverse and rich details. \ \ As shown in Table 4, all the results show moderate (κ > 0.4) agreement, which shows our model outperforms the BART baseline significantly in dialogue informativeness and coherence. \ Case Study Figure 5 showed two examples to investigate how learning character representations can help our model generate more coherent dialogue. We found that our model can better model the relationship between different characters and the direction of the storyline. For example, in the first case, we can see that the BART’s generation confuses different characters’ fathers, while our model captures the relationship between different characters, and generates proper responses for the corresponding characters, which also moves the plot forward. And in the second case, we can see that BART’s generation is commonplace and contradicts the plot development. In contrast, our model captures the intentions of the speaker and the development trend of the plot, generating an appropriate and coherent response. Since these two models use the same pretrained weight, we can infer that the character modeling module leverages the coherent and reasonable generation. \ We also summarize four error types of the generated dialogue turn for the DialGen task: (1) Intersentence Contradiction; (2) Inter-sentence Repetition; (3) Intra-sentence Contradiction; (4) Intrasentence Repetition. We show the typical corresponding cases in Figure 4. We conducted a quantitative analysis of those 4 error types on our model’s generation. We analyzed 20 stories with 103 dialog turns and the results are shown in Figure 6. We found that both our model and BART suffer from these errors, suggesting that there is still space for model improvement, especially in the inter-sentence repetition. \ \ 6.2 Dialogue Speaker Recognition Implementation Details To conduct experiments on the speaker recognition task, we decide the hyper-parameters based on the performance of the validation set. For the BART baseline and our approach, we insert a mask token before each dialogue needed to be predicted, and a person id token before and after each character name span. Then, we insert all the unique person id tokens before the input stories as different options, and make predictions based on the cos similarity of option tokens and mask tokens. We train Shao et al. (2021)’s BART model for 30 epochs with a 5e-5 learning rate for 3 days, For encoder-only baselines, we implemented BERT, RoBERTa, and MacBERT and trained them for 15 epochs wity a le-5 learning rate for 2 days. For our model, we train it for 22 epochs with a le-6 learning rate for 2 days. All baselines and our model are trained using the Adam optimizer. \ \ Metrics We evaluate the DialSpk task using two automatic metrics including dialogue-level accuracy (DAC) and story-level accuracy (SAC). DAC is calculated as the ratio of the correct predictions to the total number of specifies dialogue turns, while SAC is the ratio of the number of stories where all dialogue turns are correctly predicted to the number of all test examples. These two metrics provide the evaluation for dialogue understanding with different granularities. \ \ Results As shown in Table 7, our model outperforms all the baselines significantly (p< 0.01, Wilcoxon signed-rank test) on both DAS and SAC scores, suggesting the benefit of learning character representations. We tested the accuracy of automatic training set annotations, and the DAC/SAC scores are 86.78%/67.80%. Together with the model’s performance on the test set, we can see the automatic annotation for the training set is of good quality. We also conducted the human prediction experiment, and the DAC/SAC scores are 97.90%/90.70%, which are much higher than the best model. So there is much room for further improvement for machine-based approaches. \ \ \ :::info This paper is available on arxiv under CC 4.0 DEED license. ::: [2] https://huggingface.co/fnlp/ bart-base-chinese \ [3] https://huggingface.co/bert-base-chinese \ [4] https://huggingface.co/hfl/ chinese-roberta-wwm-ext \ [5] https://huggingface.co/hfl/ chinese-macbert-base

\ \ Every year, people around the world find themselves unable to cover basic medical costs. In fact, about half of U.S. adults currently have trouble affording healthcare. This is caused by issues like the high cost of hospital care and a lack of pharmaceutical price transparency, resulting in a faulty system that continues to leave millions out. \ However, industry experts like Mandhir seek to address these issues. As a senior lead engineer at Elevance Health, Mandhir has been instrumental in using patient data to create a unique, AI-powered system that helps users access personalized medical services. He’s also worked to establish a broader vision of patient care that not only considers physical factors but also takes into account social, behavioral, and mental elements. \ Learn more about Mandhir’s work and how he’s worked to make medical costs more affordable for millions of people worldwide. The Current Challenges in Healthcare Most problems in the American healthcare industry can be traced to a lack of transparency. Under the current system, patients have no way of knowing the full cost of their treatments until the bill is already due, whether it’s hospital fees, prescription drugs, or insurance coverage. This leaves patients susceptible to surprise medical bills that can quickly wipe out savings. \ Additionally, most facilities don’t have the proper infrastructure to store and organize the massive amounts of patient data that the healthcare industry generates every day. This makes it difficult for providers to access vital records, but it also means they can get inconsistent or incomplete data, making it harder to get the full picture of a patient’s health problems. \ Without a unified communication system among providers that keeps data synchronized and all in one place, patient care suffers as doctors inaccurately assess their needs (due to a lack of good data). It also leads to conflicting reports and unnecessary additional expenses, significantly contributing to a healthcare system that’s already rife with disparities in price and quality. Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Mandhir has understood the importance of healthcare accessibility since he was a child. Raised in a remote village in Punjab, India, he saw firsthand how families around him struggled to afford basic health treatments, even going as far as to sell their property. When he moved to the United States, he was impressed by many advancements in healthcare, but he was even more surprised that healthcare was almost just as inaccessible despite its innovations. \ After receiving his Bachelor of Technology from the Punjab Technical University, he worked for over 13 years as a technology architect for the second-largest Indian IT company, Infosys. During this time, he worked on claim adjudication systems for Anthem, one of the largest health companies in the US, and gained great insight into the inner workings of the country’s healthcare sector. \ This experience eventually led Mandhir to work directly for Anthem, now called Elevance Health. As a senior lead engineer, he’s used his position and talents over the years to spearhead significant developments in data-driven healthcare technology. How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance Fueled by a passion rooted in personal experience, Mandhir began addressing healthcare’s shortcomings by incorporating concepts like machine learning and artificial intelligence into Elevance Health’s patient data systems. \ Some of the ways he’s contributed to a better healthcare system include: \ The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. \ The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. \ Thanks to these efforts, Mandhir has worked toward making medicine more affordable. For example, the insights gained from data-powered health monitoring allow patients to start precautionary treatments earlier and reduce unwanted expenses like hospital stays. They also help providers calculate costs more accurately based on a patient’s health plan. Whole Health: A Holistic Approach to Healthcare Data Improving the quality of patient health records was only the first step toward making quality medical services more accessible to patients. \ Mandhir has tailored his machine learning systems to fit the “whole health” approach to healthcare. Whole health is a mindset that recognizes that a patient’s health consists of more than just their physical condition — it includes their mental well-being, behavioral conditions, and social factors like their living situation. Additionally, according to this philosophy, all of these factors should be considered when addressing a patient’s access to health services. \ To this end, Mandhir assisted in developing the Whole Health Index, a tool that gathers patient data from multiple sources (like health records, surveys, and community organizations) and creates a thorough overview of a patient’s health profile and financial situation. \ This holistic approach has been key to enhancing patient-provider communication. By understanding what the patient can and can’t afford, both parties can work to find a cost-effective, high-quality treatment option. Not only does that build patient trust in the healthcare industry, but it also leads to improved health outcomes. Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery By seizing the latest developments in emerging technology like AI, Mandhir plans to reinforce data security and privacy in platforms like Health OS to build further trust with providers. He also aims to make the whole health approach a fixed industry standard to change the public perception of the healthcare system. Mandhir also keeps an open dialogue with policymakers and stakeholders to advocate for price disclosure and accessibility as a broader societal change. \ Mandhir believes that healthcare should not be a privilege but a right. And with his technical expertise, in-depth knowledge of the healthcare system, and personal life experience, he’s dedicated his career to turning this vision into a reality. :::info This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author ::: \

Embrace being a generalist by accepting the challenges of juggling multiple interests and skills. Prepare for sacrifices in time and identity struggles. Learn to balance creativity with practicality and don't fear jumping into new experiences. Be ready for potential burnout and aim for a long-term approach to managing your diverse strengths and weaknesses.

Remote work has gained popularity, but not all U.S. states offer equal opportunities. New data reveals the top 10 WFH states, led by Maryland, and the lowest WFH figures, with Mississippi at the bottom. Factors like industry composition impact WFH rates. Visit the HackerNoon Job Board to explore remote job opportunities in tech roles across the U.S.

:::info Authors: (1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. ::: Table of Links Abstract and Intro Related Works DIALSTORY Dataset Proposed Tasks Methodology Experiments Discussion Future Work Conclusion Limitations and References 5 Methodology We propose to learn representations of different characters and exert them on decoding masked dialogue turns or predicting speakers. In this section, we describe the details of our model. Figure 2 shows the model overview for the DialGen task. \ 5.1 Character Representation Learning 5.2 Character Representation Utilization \ \ \ \ :::info This paper is available on arxiv under CC 4.0 DEED license. ::: \

The article discusses common communication problems at work, emphasizing the importance of clear expectations, active listening, repetition of key information, asking questions, clarifying assumptions, avoiding blame games, addressing conflicts promptly, and keeping ego in check to enhance workplace communication and productivity.

This article explores the Mathematical details of least squares estimator in an unbiased and biased settings due to model specification errors.

In this issue, we have invited Mr. Gao Jun, a PMC member of the community, to record a demo tutorial in the video which themed How to sync data from MySQL to Doris.

:::info Authors: (1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. ::: Table of Links Abstract and Intro Related Works DIALSTORY Dataset Proposed Tasks Methodology Experiments Discussion Future Work Conclusion Limitations and References 4 Proposed Tasks We aim to measure model’s ability to understand and generate dialogue in a story. To this end, we design the dialogue generation task Masked Dialogue Generation and dialogue understanding task Dialogue Speaker Recognition. We show the task definitions, targets, dataset construction and statistics below. 4.1 Masked Dialogue Generation \ Dataset Construction We use the following constraints to construct the DialGen dataset based on DIALSTORY: \ • We randomly mask 30% of the dialogue turns in each story. \ • We do not mask the first 50 tokens to provide sufficient background information for the story. \ • We do not mask the last 30 tokens to provide ending information for that story. \ • We ensure that each input story (i.e. with masked dialogue turns) mentions at least five characters. \ Table 2 shows the detailed statistics. 4.2 Dialogue Speaker Recognition \ Dataset Construction We randomly sampled 20k stories from DIALSTORY and automatically annotate the speaker for each dialogue turn for training, and resorted to manual annotation for validation and testing. For manual annotation, we first ask one annotator to label the characters in a story and the speaker of each dialogue turn. Then we asked another two annotators to check the correctness of the annotations, e.g., whether all mentioned characters are annotated, and whether each dialogue speaker is correct. We require the first annotator to re-annotate those examples that another two annotators do not agree on, and repeat the above process until all annotators agree on the examples. We also sampled 100 stories in the training set for manual annotation to investigate the accuracy of automatic annotation, which we will discuss in Section 6.2. Table 2 shows the detailed statistics. \ \ :::info This paper is available on arxiv under CC 4.0 DEED license. ::: \

Crypto data APIs are revolutionizing blockchain development by enabling AI integration and driving innovation across industries like finance, compliance, economic simulation, asset discovery, gaming, and the metaverse. These APIs act as critical connectors, unlocking the potential of decentralized data for building the future of technology and digital experiences.