In recent years we have witnessed an increase in cyber threats and malicious software attacks on different platforms with important consequences to persons and businesses. It has become critical to find automated machine learning techniques to proactively defend against malware. Transformers, a category of attention-based deep learning techniques, have recently shown impressive results in solving different tasks mainly related to the field of Natural Language Processing (NLP). In this paper, we propose the use of a Transformers' architecture to automatically detect malicious software. We propose a model based on BERT (Bidirectional Encoder Representations from Transformers) which performs a static analysis on the source code of Android applications using preprocessed features to characterize existing malware and classify it into different representative malware categories. The obtained results are promising and show the high performance obtained by Transformer-based models for malicious software detection.

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training purely with synthetically generated theorems, without any human data aside from axioms. We use these theorems to train a neurally-guided saturationbased prover. Our neural prover outperforms the state-of-the-art E-prover on this synthetic data in both time and search steps, and shows significant transfer to the unseen human-written theorems from the TPTP library, where it solves 72% of first-order problems without equality. Most work applying machine learning to theorem proving takes the following approach: 1) pick a dataset of formalized mathematics, such as Mizar or Metamath, or the standard library of a major proof assistant such as HOL-Light or Coq; 2) split the dataset into train and test; 3) use imitation learning or reinforcement learning on the training set to learn a policy; and finally 4) evaluate the policy on the test set (Loos et al. (2017), Bansal et al. (2019), Yang & Deng (2019), Han et al. (2021), Polu & Sutskever (2020)). Such methods are fundamentally limited by the size of the training set, particularly when relying on deep neural networks (Kaplan et al., 2020). Unfortunately, unlike in computer vision and natural language processing, theorem proving datasets are comparatively tiny.

During the last years, many advances have been made in tasks like3D model retrieval, 3D model classification, and 3D model segmentation.The typical 3D representations such as point clouds, voxels, and poly-gon meshes are mostly suitable for rendering purposes, while their use forcognitive processes (retrieval, classification, segmentation) is limited dueto their high redundancy and complexity. We propose a deep learningarchitecture to handle 3D models as an input. We combine this architec-ture with other standard architectures like Convolutional Neural Networksand autoencoders for computing 3D model embeddings. Our goal is torepresent a 3D model as a vector with enough information to substitutethe 3D model for high-level tasks. Since this vector is a learned repre-sentation which tries to capture the relevant information of a 3D model,we show that the embedding representation conveys semantic informationthat helps to deal with the similarity assessment of 3D objects. Our ex-periments show the benefit of computing the embeddings of a 3D modeldata set and use them for effective 3D Model Retrieval.

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.

Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling such intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups based on race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at github.com/bhavyaghai/WordBias.

The no free lunch theorem states that no model is better suited to every problem. A question that arises from this is how to design methods that propose optimizers tailored to specific problems achieving state-of-the-art performance. This paper addresses this issue by proposing the use of meta-learning to infer population-based black-box optimizers that can automatically adapt to specific classes of problems. We suggest a general modeling of population-based algorithms that result in Learning-to-Optimize POMDP (LTO-POMDP), a meta-learning framework based on a specific partially observable Markov decision process (POMDP). From that framework's formulation, we propose to parameterize the algorithm using deep recurrent neural networks and use a meta-loss function based on stochastic algorithms' performance to train efficient data-driven optimizers over several related optimization tasks. The learned optimizers' performance based on this implementation is assessed on various black-box optimization tasks and hyperparameter tuning of machine learning models. Our results revealed that the meta-loss function encourages a learned algorithm to alter its search behavior so that it can easily fit into a new context. Thus, it allows better generalization and higher sample efficiency than state-of-the-art generic optimization algorithms, such as the Covariance matrix adaptation evolution strategy (CMA-ES).

With the emergence of the COVID-19 pandemic, there have been global calls for the implementation of “social distancing” to control transmission. Throughout the world, some have resisted this requirement with the unfounded argument that it is unnecessary or ineffective. Social distancing, however, is a natural consequence of disease across animals, both human and nonhuman. Stockmaier et al. reviewed responses to disease across animal taxa and reveal how these responses naturally limit disease transmission. Understanding such natural responses and their impacts on pathogenic transmission provides epidemiological insight into our own responses to pandemic challenges. Science , this issue p. [eabc8881][1] ### BACKGROUND Contagious pathogens can trigger diverse changes in host social behaviors, rewiring their social networks and profoundly influencing the extent and pace of pathogen spread. Although “social distancing” is now an all too familiar strategy to manage COVID-19, nonhuman animals also exhibit a suite of pathogen-induced changes in social interactions, either as precautionary measures by healthy hosts or as physiological consequences of infection in sick individuals. These diverse changes in the social behaviors of both healthy and infected hosts in response to pathogens are widespread across taxa, but we still have much to learn about their underlying mechanisms and epidemiological and evolutionary consequences. Studies of social distancing behaviors in nonhuman animals have the potential to provide important and unique insights into ecological and evolutionary processes relevant to human public health, including pathogen transmission dynamics and virulence evolution. ### ADVANCES We synthesize the literature on pathogen-induced changes in sociality in nonhuman animals and in humans. These include active and passive changes in pathogen-exposed and -unexposed group members occurring both before and after individuals develop an active infection. Behavioral changes that reduce social interactions—and thus pathogen spread—include changes driven by infectious hosts, such as sickness behaviors and active self-isolation, as well as changes driven by healthy hosts, including active avoidance or exclusion of infectious individuals and proactive social distancing in the face of pathogenic threats. Although species have evolved behavioral social distancing because it reduces infection risk, these behaviors also incur costs by limiting access to the many benefits of group living, such as protection against predators and cooperative food finding. Thus, many species appear to have evolved the ability to alter the expression of these behaviors in ways that maximize benefits and minimize costs. The most susceptible individuals of some species show the strongest avoidance of sick conspecifics, and social distancing behaviors are sometimes foregone in interactions with close relatives. Pathogen-induced changes in sociality also apply important selection pressures on pathogens. Because social distancing reduces transmission and thus fitness, pathogens may evolve lower levels of virulence, presymptomatic transmission, or the ability to disguise cues that enable hosts to recognize their presence. Finally, pathogen infection can also increase social interactions when healthy individuals lend aid to pathogen-contaminated or sick conspecifics. Helping sick individuals is a major part of human and eusocial insect societies but is less commonly observed in other, nonhuman animals. Whether pathogens can evolve to elicit helping behavior in hosts, thus augmenting their own transmission, remains unknown. ### OUTLOOK The structure and dynamics of social contact networks fundamentally determine the fate of disease outbreaks, that is, how fast and far they spread and who will be infected. In the race to combat the COVID-19 pandemic, numerous studies have begun to address the public health utility of unprecedented social distancing efforts. Nonhuman animal systems, particularly those with social structures similar to those of humans, present unique opportunities to inform relevant public health questions such as the effectiveness, variability, and required duration of social distancing measures. Further, the experimental tractability of nonhuman animal systems allows study of the coevolutionary dynamics generated by social distancing behaviors, which themselves have public health implications. Selection for or against social distancing behaviors has the potential to create a conflict of interest and could incentivize selfish behaviors that are not in the best interest of everyone. ![Figure][2] Social distancing in humans and nonhuman animals. ( A ) Pathogen-exposed forager ants self-isolate and their nestmates increase social distance to each other (image: Timothée Brütsch). ( B ) People social distance during COVID-19 (image: Forest Simon). ( C ) Sick vampire bats reduce grooming non-close kin (image: Gerald Carter). ( D and E ) Under certain conditions, Trinidadian guppies avoid parasitized individuals (D), (image: Sean Earnshaw, University of St. Andrews) and house finches avoid sick conspecifics (E) (image: Jeremy Stanley). Spread of contagious pathogens critically depends on the number and types of contacts between infectious and susceptible hosts. Changes in social behavior by susceptible, exposed, or sick individuals thus have far-reaching downstream consequences for infectious disease spread. Although “social distancing” is now an all too familiar strategy for managing COVID-19, nonhuman animals also exhibit pathogen-induced changes in social interactions. Here, we synthesize the effects of infectious pathogens on social interactions in animals (including humans), review what is known about underlying mechanisms, and consider implications for evolution and epidemiology. [1]: /lookup/doi/10.1126/science.abc8881 [2]: pending:yes

SCI COMMUN### COVID-19 Johnson & Johnson (J&J) last week became the third COVID-19 vaccinemaker to receive emergency use authorization for its product from the U.S. Food and Drug Administration. In contrast to the two-dose vaccines from Moderna and Pfizer authorized earlier, the J&J vaccine—a harmless virus delivering the gene for the spike protein from SARS-CoV-2—proved safe and effective with a single dose. The company intends to deliver 20 million doses to the United States this month, 80 million more by the end of June, and more than 1 billion doses worldwide this year. A placebo-controlled trial that took place in eight countries and involved more than 43,000 participants found that the single shot had 66% efficacy against moderate to severe COVID-19 after 28 days and 85% protection against severe disease. This is below the approximately 95% efficacy against mild disease achieved by the Pfizer and Moderna vaccines, which produce the spike protein using messenger RNA (mRNA)—but the J&J trial included locations in South Africa and Brazil where SARS-CoV-2 variants that may escape vaccine-induced antibodies are now common. (The mRNA vaccine results came before their spread.) No one who received the J&J vaccine in any country was hospitalized or died from COVID-19. The White House also brokered a deal with Merck, a major vaccine producer that dropped its own COVID-19 candidates because of poor performance, to help make the J&J product. ### Archaeology A detailed excavation in what was once a Roman port city has helped archaeologists identify what may be the oldest known pet cemetery. The remains of nearly 600 cats and dogs had been laid in prepared pits and covered with pieces of pottery and textiles, and some wore collars and other adornments. Researchers discovered the graveyard in 2011 outside the ancient city of Berenice, which today lies in Egypt. Features of some skeletons indicated the animals had lived with debilitating injuries and illnesses and survived into old age, indicating the animals were cared for, the researchers reported recently in World Archaeology . ### Art and science “I'm the first author, you're just et al. ,” raps this year's winner of Science 's annual “Dance Your Ph.D.,” a contest that challenges scientists to explain their research through dance. The first place video by Jakub Kubečka, a doctoral student at the University of Helsinki, features an original rap song and choreography, performed by him and two friends (above), explaining the search for atmospheric molecular clusters—groups of atoms that stick together and encourage water vapor to condense into clouds. Kubečka beat out 39 competitors for the $2000 top prize, sponsored by the artificial intelligence firm Primer. The winning entry is at . ### COVID-19 The U.S. National Institutes of Health (NIH) last week announced a multipronged research effort to better understand and treat Long COVID, in which people suffer lingering effects after infection by the pandemic coronavirus. Symptoms include lung problems, heart abnormalities, and enduring fatigue. In December 2020, Congress gave NIH $1.15 billion over 4 years to study the perplexing condition. The agency is inviting applications for research on its natural history, prevalence, and underlying biology and plans an expansive biorepository for samples from volunteers. Last week, the World Health Organization released a policy document estimating 10% of patients remain unwell 12 weeks after being infected. Explanations for the lasting effects have been elusive ( Science , 7 August 2020, p. [614][1]). ### Climate change Countries are drastically lagging in the fossil fuel cuts needed to reach the goals of the Paris climate agreement, a new analysis suggests. Globally, emissions were up by an average of 0.21 billion tons of carbon dioxide (CO2) per year from 2016 to 2019 compared with 2011–15, the Global Carbon Project reported this week in Nature Climate Change . Although 64 countries—most of them wealthy ones that have contributed the most to climate change—cut their CO2 emissions by a collective 0.16 billion tons per year during this time, their reductions must increase 10-fold, to some 1 billion tons annually, to meet the Paris goal of limiting global warming to 2°C. Although the pandemic caused a 7% drop in emissions in 2020, the report says, evidence from previous economic crises suggests emissions will rebound to previous levels unless recovery plans aggressively push decarbonization. ### Immigration U.S. President Joe Biden last week ended a policy, imposed last year by then-President Donald Trump, that had barred most noncitizens not already in the United States from seeking permanent residency and work permits, or green cards. Trump had said issuing new green cards didn't make sense given unemployment caused by the COVID-19 pandemic. But industry groups had challenged the policy, in part because they said it prevented companies from hiring needed scientists and skilled technical workers. In revoking the ban, Biden said it had harmed U.S. businesses “that utilize talent from around the world.” A Trump ban on temporary work permits remains in place but is set to expire on 31 March. ### Climate policy U.S. President Joe Biden's administration last week raised the government's benchmark for the “social cost of carbon,” the estimate it uses in cost-benefit analyses of regulations and other policies to represent the burden that global warming places on present and future generations. The figure will rise to $51 per ton on an interim basis; former President Donald Trump's administration had set it as low as $1. The revised standard restores the level set under former President Barack Obama, adjusted for inflation. The Biden administration may further increase the figure in an update due in January 2022 to reflect increased damages from heat waves and other disasters made worse by global warming. ### Publishing A study of more than 5000 biomedical journals found a pattern of apparent favoritism: In 206 journals, a single author was responsible for between 11% and 40% of the papers published between 2015 and 2019. Of 100 of these “nepotistic” journals given closer scrutiny, the prolific author was the editor-in-chief for about one-quarter and on the editorial board for more than 60%. Prolific authors also enjoyed faster peer reviews, according to a preprint of the study posted last month on the bioRxiv server. A research team did the analysis after scrutinizing publications by microbiologist Didier Raoult of Aix-Marseilles University, who has promoted hydroxychloroquine as a COVID-19 treatment, although most other studies have found no evidence of benefit. Raoult, who now faces disciplinary action by a French medical regulator, appears as an author on one-third of the 728 papers at the journal New Microbes and New Infections , where some of his collaborators serve as editors. ### Science and art An artificial intelligence (AI) program for the first time has written a play, which was staged by actors in Prague's Švanda Theater and premiered online last week. The script, depicting a robot's journey trying to understand humans, was generated by a widely available AI system called GPT-2. Researchers at Charles University helped it start to write the play by feeding it two sentences of dialogue about human experiences, and the software generated more, using related information drawn from the internet. Dramatist David Košt'ák, who tweaked about 10% of the resulting script to ensure it followed a coherent storyline, called its style “abstract.” But AI: When a robot writes a play showcases what the evolving technology can now do, specialists say. Judge the 60-minute play for yourself at . ### Conservation The population of monarch butterflies overwintering in Mexico showed another big drop this year. Researchers counted 2.1 hectares of occupied habitat, down 26% from last year and more than 80% from 2 decades ago, the Center for Biological Diversity said. Six hectares is the minimum considered necessary to avoid a risk of extinction. DINO TRACKS IN PERIL Ongoing mining threatens to destroy China's largest site of dinosaur tracks, researchers reported online on 27 February in Geoscience Frontiers . In 1994, the first of the tracks, 145 million to 120 million years old, were uncovered in a copper mine in China's southwestern Sichuan province. Paleontologists have identified 1928 individual footprints from dozens of individuals, representing ornithopods, theropods, sauropods, and pterosaurs. By 2012, mining had led one of the track-bearing rock faces to collapse, before it was fully studied. NEW MINE HELD UP The Biden administration has delayed a huge Arizona copper mine opposed by archaeologists and Native tribes, who say it will destroy cultural treasures. In 2014 Congress approved giving 970 hectares of federal land at Oak Flat to a mining firm. But this week officials said they want to review an environmental study needed for the transfer. Mine opponents have asked Congress and the courts to kill the project. A WIN AGAINST MALARIA El Salvador last week became the first country in Central America to be certified free of malaria by the World Health Organization. The country became eligible after recording no home-grown cases of the mosquito-borne disease since 2017. Globally, 38 countries and territories have reached this milestone. BIG CANCER FUND A new foundation will provide $250 million for cancer research, one of the largest such gifts ever. Break Through Cancer was financed by a Richmond, Virginia, businessperson whose son died of cancer in 2020. The funding will support research teams drawn from five prominent U.S. university cancer centers that will study cancer types that are difficult to treat and have high mortality rates, including pancreatic and ovarian cancer, glioblastomas, and acute myelogenous leukemia. NO PLACE LIKE HOME Americans value space research aimed at protecting Earth over sending astronauts to other bodies, a survey by Morning Consult says. Sixty-three percent of respondents called monitoring Earth's climate a top or important priority. Just 33% voiced that level of backing for launching astronauts to Mars or the Moon. ### Should peer reviewers be paid? Reviewing journal articles can seem a thankless task. Scientists do the work for free, even as as journals publish ever more papers and some publishers make sizable profits. Even before the COVID-19 pandemic led to a blizzard of submissions, journal editors were reporting that “reviewer fatigue” was making it harder to find volunteers. At the Researcher to Reader conference on scholarly publishing last week, two teams debated a provocative question: Should peer reviewers be paid? Here are some of their arguments. (See a fuller version at .) YES: “There is no downward pressure on the endless use of academic labor. And the easiest way to exert that pressure is to value the task not [only] with recognition, but with the traditional way to support skilled labor in every other industry, which is money.” James Heathers, a former research scientist, now chief scientist at a technology startup NO: “A 2018 survey found that only 17% of respondents selected cash or in-kind payment as something that would make them more likely to accept review requests.” (Nearly half said more explicit recognition of reviewing work from their universities or employers would inspire them to do it.) Alison Mudditt, CEO of PLOS, a nonprofit publisher of open-access articles NO: It could cost $3960 per accepted paper to cover the cost of reviewing, if each reviewer was paid $450, each manuscript received 2.2 reviews, and the journal accepted 25% of submissions. “Surely that money would better spent on the research itself and on solving our most pressing global challenges.” Tim Vines, a publishing consultant YES: “What might very well happen is fewer papers get submitted, because the costs go up. … A contract provides much needed certainty around the time frame, the quality, and the predictability of the review received.” Brad Fenwick, senior vice president at Taylor & Francis, a for-profit publisher NO: “It's completely unrealistic to expect that anybody is going to have either the time or the expertise or the scale to be able to manage and monitor hundreds of thousands of additional new contracts across the publishing system. Just not gonna happen.” A.M. [1]: http://www.sciencemag.org/content/369/6504/614

"By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." Today, AI is an indispensable part of our daily lives. To define it scientifically, it is the field of study of "intelligent agents"; any device that can mimic a human task, perceive its environment, and takes actions that maximize its chance of successfully achieving its goals. AI has helped numerous industries develop novel approaches to solve challenging problems. While all this is enthralling, there is a catch, the black box dilemma.

It is believed that each company must embrace the AI revolution from the smallest local businesses to the largest global players, and recognize how artificial intelligence can have the greatest impact on their business. The boundary for mistake is quickly diminishing, as global supply chains increase in complexity. With the rising rivalry in a linked digital environment, optimizing productivity by reducing uncertainties of all sorts becomes even more important. The increase of supersonic speed and efficiency standards among suppliers of all kinds further emphasizes the need for AI Solutions Company to harness Artificial Intelligence skills in both supply chains and logistics. Artificial Intelligence has experienced a long, zigzagging evolution to get to this opinion of application in logistics.