Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.

SCI COMMUN### Astrophysics The team that in 2019 used a global network of radio telescopes to reveal the first image of a black hole has offered a new twist on that iconic view: the same black hole in polarized light. The thin lines spiraling in toward the black hole's shadow (above) show areas of light that differ in their polarization—the direction in which the light waves vibrate. The light, from plasma near the black hole's edge, was polarized by magnetic fields, and so the new image, described last week in The Astrophysical Journal by the Event Horizon Telescope team, indicates their structure. Researchers hope to learn how the fields help accreting black holes funnel matter and energy into jets emanating from their poles. 69% —Percentage of postdoctoral researchers surveyed in October 2020 by the U.S. National Institutes of Health who anticipate the COVID-19 pandemic will negatively affect their careers. For researchers at all levels, the figure was 55%. ### Conservation Despite the antienvironmental policies of its current leadership, Brazil has become the 130th country to ratify the Nagoya Protocol, a part of the Convention on Biological Diversity that lays out measures to protect countries' biodiversity claims, the CBD announced last week. The ratification, first proposed by a previous administration in 2012, had languished until 2019, when rampant deforestation led pro-environment leaders to push for approval. The current government is seen as having consented because the protocol allows nations to impose rules on the international trade in its plant and animal products; by legitimizing the sales, the regulations are expected to increase exports and tax revenues. For example, money from sales of native plants such as açai ( Euterpe oleracea ) and Brazil nut ( Bertholletia excelsa ) could be returned to help Indigenous communities that use and harvest them. Observers question whether the ratification alone will protect Brazil's biodiversity, perhaps the world's greatest—but hailed the step as helpful. ### Public health The United States and 13 other countries this week criticized a report by a World Health Organization panel that had visited China to investigate how the COVID-19 pandemic started. The 300-page document says the most likely cause was a bat coronavirus that infected another, unidentified animal and then moved to humans, but it recommends further research. The report's most definitive conclusion is also its most controversial: that it is “extremely unlikely” that SARS-CoV-2 came out of a Chinese laboratory. Scientists from China made up half of the 34-member international panel. A joint statement by other countries complained that the investigation was “significantly delayed and lacked access to complete, original data, and samples.” It called for a transparent, “rapid, independent, expert-led, and unimpeded evaluation of the origins.” ### Funding The science committee in the U.S. House of Representatives wants to more than double the budget of the National Science Foundation (NSF) in the next 5 years, from $8.5 billion to $18.3 billion. A sizable chunk of the extra money—$5 billion by 2026—would go to a new directorate, Science and Engineering Solutions, that would accelerate the conversion of basic research into new technologies and products. Last year, Senate Majority Leader Chuck Schumer (D–NY) proposed growing NSF to $100 billion over 5 years, with roughly one-third of that money going to a new technology directorate. Schumer's vision for NSF is part of still-evolving draft legislation affecting many federal agencies that pinpoints key technologies needed to address economic and security threats posed by China's growing technological prowess. In contrast, the House bill is limited to NSF's programs and is aimed at strengthening basic research across all disciplines that NSF supports. The House and Senate would need to agree on a vision for NSF, and other legislation would be needed to appropriate the money. ### Astronomy Light pollution from space junk and satellites may have already robbed the entire Earth of the dark skies best for sensitive astronomical observations, an analysis has found. Researchers estimated the size and shininess of tens of thousands of objects in orbit as of 2020, before an onslaught of thousands more satellites that companies plan to launch in the coming years. Even at Earth's darkest sites, the sky glows from natural sources such as ionized particles; but the existing orbiting objects reflect and scatter about 10% more of this diffuse light back into the atmosphere, the research team calculates in a paper accepted this week by the Monthly Notices of the Royal Astronomical Society . That extra amount violates an International Astronomical Union standard for observing sites and could compromise observations of the dimmest galaxies, which scientists study for clues about the physics of galaxy formation and the nature of dark matter. To gather such data, astronomers already need long exposures on the biggest telescopes at the darkest available sites. ### Ethics Harvard University last week penalized quantitative biologist Martin Nowak for his connections with disgraced financier Jeffrey Epstein. Epstein had donated $6.5 million for Nowak's research in 2003; after being convicted in 2008 of soliciting prostitution from a minor, Epstein introduced Nowak to donors who provided an additional $7.5 million. Nowak's actions after 2008—repeatedly hosting Epstein on campus, promoting Epstein on his program's web page, and providing false information about Epstein's support in a grant application—violated Harvard policies, and other actions showed “blameworthy negligence and unprofessional behavior,” Claudine Gay, dean of arts and sciences, wrote in an email last week to faculty members. Nowak will continue at Harvard as a math professor, but his Program for Evolutionary Dynamics will be shut down and he will be barred for at least 2 years from serving as a principal investigator on grants. “I regret the connection I was part of fostering between Harvard and Jeffrey Epstein,” Nowak said in a statement last week. Epstein died by suicide in 2019. ### Archaeology Chinese archaeologists last week reported unearthing more than 500 artifacts, including gold ornaments, bronze heads, ivory and jade tools, and a gold mask dating back about 3000 years at the Sanxingdui archaeological site in southwestern Sichuan province. Sanxingdui, then ruled by the Shu kingdom, has already yielded thousands of bronze relics unlike anything found elsewhere in China, including at sites of the contemporaneous Shang dynasty in the Yellow River region. The new finds, retrieved from what are thought to be sacrificial pits, may shed light on how the Shu kingdom contributed to Chinese civilization. VACCINE LEADER FIRED Moncef Slaoui, who headed COVID-19 vaccine development during the Trump administration, has been fired as chairman of a medical research firm controlled by manufacturer GlaxoSmithKline after he was accused of sexual harassment. The company said an outside investigation substantiated the allegation by a female employee about Slaoui's behavior several years ago when he worked there. Slaoui also stepped down from leadership roles at two other pharmaceutical companies and issued a statement in which he apologized to the woman and his family. RETURNING LOOTED ART Museums in Germany have pledged to return hundreds of artifacts, including bronze statues, looted during the colonial era from the kingdom of Benin in what is now Nigeria. The British Museum and others face growing pressure to join them. PARDON SOUGHT The Australian Academy of Science issued a statement saying a court ignored new genetic evidence when it denied last week an appeal by a woman convicted of killing her four young children. Tests point to a natural cause of the deaths: Two of the children carried a mutation in the CALM2 gene that is associated with sudden death by cardiac failure in infants and children. Prosecutors had accused Kathleen Folbigg of smothering the children but have not presented medical evidence that supports that position. Academy members have signed a petition asking New South Wales's governor to pardon her. AI IN MEDICINE The Broad Institute has received $300 million to study how machine learning can improve the prevention and treatment of disease. Half the sum is coming from a foundation of Wendy and Eric Schmidt, a member of Broad's board and former CEO of Google, and the rest from the Broad Foundation. R&D SPENDING RISE The United States spent more than 3% of gross domestic product on R&D in 2019 for the first time. The 3.07% share is a record and met a goal set by former President Barack Obama a decade ago. Israel led globally with 4.9%, the Organisation for Economic Co-operation and Development said. Total U.S. spending was more than any other country's.

JCU's Phoebe Arbon was presented with a Science and Innovation Award for Young People in Agriculture, Fisheries and Forestry last night in Canberra. "I'll use the grant to develop, train and validate an AI model to identify, count and measure abalone from an image. So, very basically, the AI model will learn to predict the weight and size of abalone from images," she said. Ms Arbon said the technology is already in existence but needs specific instructions and application to work within the abalone industry. "Currently, assessing abalone is done manually which can cause harm to the abalone, and costs each farm about $25,000 a year," she said.

Most experts have settled on a description of Artificial Intelligence as being the scientific endeavor of building computers that mimic the capabilities of the human brain. To put that into perspective, we know that Human Intelligence started to evolve 7–8 million years ago when our oldest ancestors had a brain volume of about 450 cubic centimeters. In the next 3.5 million years our ancestors' brain volume increased to about 1350 cubic centimeters. Modern humans (average brain volume of about 1200 cubic centimeters) evolved from the Homo Sapiens species during a period of dramatic climate change 300,000 years ago. Like other early humans that were living at this time, they gathered and hunted food, and evolved behaviors that helped them respond to the challenges of survival in unstable environments.

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.

Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated into deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this paper, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be utilized to improve artificial neural networks.

We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nash equilibrium in constant time, and therefore is -- to the best of our knowledge -- the first such formal solution to this game. Surprisingly, despite all this, our implementation of AlphaChute remains relatively straightforward due to domain-specific adaptations. We provide the source code for AlphaChute here in our Appendix.

Modelers use automatic differentiation of computation graphs to implement complex Deep Learning models without defining gradient computations. However, modelers often use sampling methods to estimate intractable expectations such as in Reinforcement Learning and Variational Inference. Current methods for estimating gradients through these sampling steps are limited: They are either only applicable to continuous random variables and differentiable functions, or can only use simple but high variance score-function estimators. To overcome these limitations, we introduce Storchastic, a new framework for automatic differentiation of stochastic computation graphs. Storchastic allows the modeler to choose from a wide variety of gradient estimation methods at each sampling step, to optimally reduce the variance of the gradient estimates. Furthermore, Storchastic is provably unbiased for estimation of any-order gradients, and generalizes variance reduction techniques to higher-order gradient estimates. Finally, we implement Storchastic as a PyTorch library.

Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existing alphas can be categorized into two classes: Formulaic alphas are simple algebraic expressions of scalar features, and thus can generalize well and be mined into a weakly correlated set. Machine learning alphas are data-driven models over vector and matrix features. They are more predictive than formulaic alphas, but are too complex to mine into a weakly correlated set. In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes. The new alphas predict returns with high accuracy and can be mined into a weakly correlated set. In addition, we propose a novel alpha mining framework based on AutoML, called AlphaEvolve, to generate the new alphas. To this end, we first propose operators for generating the new alphas and selectively injecting relational domain knowledge to model the relations between stocks. We then accelerate the alpha mining by proposing a pruning technique for redundant alphas. Experiments show that AlphaEvolve can evolve initial alphas into the new alphas with high returns and weak correlations.

We present a parallel machine translation training corpus for English and Akuapem Twi of 25,421 sentence pairs. We used a transformer-based translator to generate initial translations in Akuapem Twi, which were later verified and corrected where necessary by native speakers to eliminate any occurrence of translationese. In addition, 697 higher quality crowd-sourced sentences are provided for use as an evaluation set for downstream Natural Language Processing (NLP) tasks. The typical use case for the larger human-verified dataset is for further training of machine translation models in Akuapem Twi. The higher quality 697 crowd-sourced dataset is recommended as a testing dataset for machine translation of English to Twi and Twi to English models. Furthermore, the Twi part of the crowd-sourced data may also be used for other tasks, such as representation learning, classification, etc. We fine-tune the transformer translation model on the training corpus and report benchmarks on the crowd-sourced test set.