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.

BERLIN – A turning point for Rafael Yuste, a neuroscientist at New York's Columbia University, came when his lab discovered it could activate a few neurons in a mouse's visual cortex and make it hallucinate. The mouse had been trained to lick at a water spout every time it saw two vertical bars, and researchers were able to prompt it to drink even with no bars in sight, said Yuste, whose team published a study on the experiment in 2019. "We could make the animal see something it didn't see, as if it were a puppet," he said in a phone interview. "If we can do this today with an animal, we can do it tomorrow with a human for sure." Yuste is part of a group of scientists and lawmakers, stretching from Switzerland to Chile, who are working to rein in the potential abuses of neuroscience by companies from tech giants to wearable startups.

Out of a hundred trials, how many errors does your speaker verifier make? For the user this is an important, practical question, but researchers and vendors typically sidestep it and supply instead the conditional error-rates that are given by the ROC/DET curve. We posit that the user's question is answered by the Bayes error-rate. We present a tutorial to show how to compute the error-rate that results when making Bayes decisions with calibrated likelihood ratios, supplied by the verifier, and an hypothesis prior, supplied by the user. For perfect calibration, the Bayes error-rate is upper bounded by min(EER,P,1-P), where EER is the equal-error-rate and P, 1-P are the prior probabilities of the competing hypotheses. The EER represents the accuracy of the verifier, while min(P,1-P) represents the hardness of the classification problem. We further show how the Bayes error-rate can be computed also for non-perfect calibration and how to generalize from error-rate to expected cost. We offer some criticism of decisions made by direct score thresholding. Finally, we demonstrate by analyzing error-rates of the recently published DCA-PLDA speaker verifier.

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time consuming solution that poses strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

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.

Its widely accepted that artificial intelligence (AI) technologies will add trillions to global GDP in the next 20 years, making it the one of the world's most powerful technology trends on par with the disruption and opportunities being created by cloud computing and blockchain. So is Africa getting a slice of the lucrative artificial intelligence pie and what are the current AI adoption trends in the region? Although Africa's AI industry is still relatively small compared to the US, Europe and Asia, this has not stopped some of the continent's most innovative start-ups from developing solutions that demonstrate how promising the technology can be for the African economy. However, AI innovation in Africa is often ignored or overlooked because the number of patents applied for and the amount of research funding available is not well aligned with local contexts, data is missing, and the map still looks essentially bleak. That said, the prospects for AI in Africa are positive, as the potential for innovation and growth in artificial intelligence (AI) adoption is increasing.

The procedure of tossing quantum coins and dice is described. This case is an important example of a quantum procedure because it presents a typical framework employed in quantum information processing and quantum computing. The emphasis is on the clarification of the difference between quantum and classical conditional probabilities. These probabilities are designed for characterizing different systems, either quantum or classical, and they, generally, cannot be reduced to each other. Thus the L\"{u}ders probability cannot be treated as a generalization of the classical conditional probability. The analogies between quantum theory of measurements and quantum decision theory are elucidated.

There have been a number of techniques that have demonstrated the generation of multimedia data for one modality at a time using GANs, such as the ability to generate images, videos, and audio. However, so far, the task of multi-modal generation of data, specifically for audio and videos both, has not been sufficiently well-explored. Towards this, we propose a method that demonstrates that we are able to generate naturalistic samples of video and audio data by the joint correlated generation of audio and video modalities. The proposed method uses multiple discriminators to ensure that the audio, video, and the joint output are also indistinguishable from real-world samples. We present a dataset for this task and show that we are able to generate realistic samples. This method is validated using various standard metrics such as Inception Score, Frechet Inception Distance (FID) and through human evaluation.

This paper surveys state-of-the-art methods and models dedicated to time series analysis and modeling, with the final aim of prediction. This review aims to offer a structured and comprehensive view of the full process flow, and encompasses time series decomposition, stationary tests, modeling and forecasting. Besides, to meet didactic purposes, a unified presentation has been adopted throughout this survey, to present decomposition frameworks on the one hand and linear and nonlinear time series models on the other hand. First, we decrypt the relationships between stationarity and linearity, and further examine the main classes of methods used to test for weak stationarity. Next, the main frameworks for time series decomposition are presented in a unified way: depending on the time series, a more or less complex decomposition scheme seeks to obtain nonstationary effects (the deterministic components) and a remaining stochastic component. An appropriate modeling of the latter is a critical step to guarantee prediction accuracy. We then present three popular linear models, together with two more flexible variants of the latter. A step further in model complexity, and still in a unified way, we present five major nonlinear models used for time series. Amongst nonlinear models, artificial neural networks hold a place apart as deep learning has recently gained considerable attention. A whole section is therefore dedicated to time series forecasting relying on deep learning approaches. A final section provides a list of R and Python implementations for the methods, models and tests presented throughout this review. In this document, our intention is to bring sufficient in-depth knowledge, while covering a broad range of models and forecasting methods: this compilation spans from well-established conventional approaches to more recent adaptations of deep learning to time series forecasting.

The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras have been successfully used to get the environment's features to perform SLAM, which is referred to as visual SLAM (VSLAM). However, classical VSLAM algorithms can be easily induced to fail when either the motion of the robot or the environment is too challenging. Although new approaches based on Deep Neural Networks (DNNs) have achieved promising results in VSLAM, they still are unable to outperform traditional methods. To leverage the robustness of deep learning to enhance traditional VSLAM systems, we propose to combine the potential of deep learning-based feature descriptors with the traditional geometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments conducted on KITTI and Euroc datasets show that deep learning can be used to improve the performance of traditional VSLAM systems, as the proposed approach was able to achieve results comparable to the state-of-the-art while being robust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding parameter tuning for specific datasets with an adaptive approach while evaluating how transfer learning can affect the quality of the features extracted.