This versatile composition of research derivatives pertaining to diverse concurrent developments in the global Big Data and Machine Learning in Telecom market is poised to induce forward-looking perspectives favoring unfaltering growth stance. The new research report assessing market developments in the global Big Data and Machine Learning in Telecom market is a 360 degree reference guide, highlighting core information on holistic competitive landscape, besides rendering high voltage information on market size and dimensions with references of value- and volume based market details, indispensable for infallible decision making in global Big Data and Machine Learning in Telecom market. Understanding Big Data and Machine Learning in Telecom market Segments: an Overview: The report is aimed at improving the decision-making capabilities of readers with due emphasis on growth planning, resource use that boost growth trajectory. Additional insights on government initiatives, regulatory framework, growth policies and resource utilization have all been highlighted for healthy growth journey. Besides understanding the revenue generation potential of each of the segments, the report also takes note of the multifarious vendor initiatives towards segment betterment that play a crucial role in growth enablement.

Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators. New methods are typically evaluated on a set of environments that have now become standard, such as Atari 2600 games. While these benchmarks help standardize evaluation, their computational cost has the unfortunate side effect of widening the gap between those with ample access to computational resources, and those without. In this work we argue that, despite the community's emphasis on large-scale environments, the traditional small-scale environments can still yield valuable scientific insights and can help reduce the barriers to entry for underprivileged communities. To substantiate our claims, we empirically revisit the paper which introduced the Rainbow algorithm [Hessel et al., 2018] and present some new insights into the algorithms used by Rainbow.

Lisbon musician Moullinex has shared an exclusive short music video showing an endlessly changing landscape of brutalist buildings drawn up by a generative design algorithm with Dezeen. Moullinex, whose real name is Luís Clara Gomes, created two videos that use artificial intelligence (AI) to imagine a series of brutalist buildings. The first video, which the artist shared on his Facebook page, is based on 200 photographs of modernist, concrete buildings. These images acted as the dataset, which was used to train a generative network via the machine learning tool StyleGAN2, to create a string of entirely non-existent buildings with similar characteristics. "It's akin to showing thousands of pictures of a cat to a child and then asking them to draw a brand new cat based on what they now know are cat-like characteristics," Gomes told Dezeen.

CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [ www.sciencemag.org/content/370/6519/925.3 ][1] Charting what the pioneering neuroanatomist Santiago Ramón y Cajal called the “impenetrable jungle” of the brain ([ 1 ][2]) presents one of biology's greatest challenges. How do billions of neurons, wired through trillions of connections, work together to produce cognition and behavior? Like an orchestra, wherein many instruments played simultaneously produce a sound greater than the sum of its parts, thought and behavior emerge from communication between ensembles of molecularly distinct neurons distributed throughout vast neural circuits. Although we know much about the properties of individual genes, cells, and circuits (see the figure, panel A), a vast gap lies between the function of each brain component and an animal's behavior. Bridging this gap has proven technically and conceptually difficult. Inspired by the fact that the development of high-throughput DNA sequencing led geneticists to shift focus from individual genes to the entire genome, I wanted to develop approaches that could simultaneously link multiple levels of the brain, from molecules to neurons to brain-wide neural networks. My goal was to capture a global perspective while maintaining the high resolution and specificity necessary to understand the function of individual components at each level. This new viewpoint, I hoped, would reveal how the collective properties of the brain's building blocks give rise to behavior. During my doctoral studies at Stanford University with Karl Deisseroth and Liqun Luo, I developed new methods to map the architecture and activity of mammalian neural circuits. I applied these approaches to understand the neural basis of thirst, a fundamental regulator of behavior ([ 2 ][3]). Need-based motivational drives, such as hunger and thirst, direct animals to satisfy specific physiological imperatives important for survival ([ 3 ][4]). Despite decades of research, at the beginning of my studies it was unclear how the activity of neurons that sense these needs causes an animal to engage in specific motivated behaviors (e.g., eating or drinking) to maintain homeostasis ([ 3 ][4]). Thirst, a relatively simple yet important drive, thus seemed the perfect model system for investigating multiple levels in the brain. I first traced thirst motivational drive from cellular gene expression to a circuit mechanism. Using a new version of targeted recombination in active populations (TRAP2), a tool to genetically label neurons according to their activity, I found that neurons in the median preoptic nucleus (MnPO) of the hypothalamus became activated in thirsty mice ([ 4 ][5]) (see the figure, panel C). Single-cell RNA sequencing revealed that these neurons formed a single molecularly defined cell type. Artificial activation of these neurons caused mice to drink water within seconds, whereas their inhibition prevented mice from drinking, which suggested that these MnPO neurons were master regulators of thirst. Drinking water also gradually reduced the activity of these neurons. Finally, activation of these neurons was aversive. Together, these results suggested a surprising “drive reduction” model of thirst motivation: Genetically hard-wired thirst neurons become active when mice need hydration, which causes mice to drink water. This ability to ascribe specific functional relevance to genetically defined neurons inspired me to develop new techniques to map cells within their native tissue architecture in even greater molecular detail. To this end, I co-developed STARmap, an approach for highly multiplexed in situ RNA sequencing to measure the expression of hundreds of genes simultaneously within a brain section at the level of single mRNA molecules ([ 5 ][6]) (see the figure, panel B ). In combination with genetic markers of activity, this technique powerfully describes the molecular identity of behaviorally activated neurons and their neighbors at single-cell resolution. ![Figure][7] New large-scale, high-resolution approaches to bridging multiple levels of brain function A new approach to brain function mapping. (A) An illustration of the levels of brain function and how they are interlinked. (B to D) New approaches to bridging levels: (B) STARm ap amplicons barcoding 1020 RNA species simultaneously with single-molecule resolution in the mouse visual cortex. (C) Genetic labeling of neurons according to activity reveals thirst neurons in the median preoptic nucleus of the hypothalamus, used to identify the motivational mechanism of thirst drive. (D) Brain-wide activity map of the response of thousands of neurons across dozens of brain regions to a water-predicting sensory cue, in thirsty or sated mice, reveals widespread broadcasting of thirst state. GRAPHIC: N. DESAI/ SCIENCE FROM W. ALLEN, WANG ET AL . ([ 5 ][6]), ALLEN ET AL . ( 4 ), ALLEN ET AL . ([ 9 ][8]) Despite these insights, a question remained: How do thirst-sensitive neurons deep in the brain coordinate activity in distributed circuits spanning sensory perception, cognition, and motor output to produce motivated behavior? I found that MnPO thirst neurons projected to many brain regions potentially serving different behavioral roles ([ 4 ][5]), but the gap between individual neurons and brain-wide networks was daunting. Earlier in graduate school, I had developed several new microscopy techniques to characterize brain-wide ([ 6 ][9]) or neocortex- wide ([ 7 ][10]) activity, which revealed that global neural activity was present during even simple motivated behaviors. However, because of the mammalian brain's opacity, these approaches were limited in their ability to record fast neural activity throughout the brain at the scale required to understand thirst motivation. Fortunately, however, developments in microelectronics enabled me to construct global maps of neuronal activity with microsecond-level temporal resolution. Using advanced “Neuropixels” probes ([ 8 ][11]), thin silicon needles that can be acutely inserted into the brain to record the electrical signals of hundreds of neurons simultaneously, I developed an experimental approach to record the activity of huge neuronal ensembles across the brain and reconstruct the anatomical location of each recorded cell ([ 9 ][8]). Applying this technique, I mapped the brain-wide flow of activity through ∼24,000 single neurons during thirst-motivated behavior ([ 9 ][8]) (see the figure, panel D). My experiments revealed that this simple behavior produced an unexpectedly global coordination of activity throughout the brain. By observing how activity changed as mice drank water, as well as directly stimulating hypothalamic thirst neurons, I showed that this activity wave was dependent on the animal's motivational state. Surprisingly, the activity of a few hundred thirst neurons instantly modulated the state of the entire brain. Even more surprisingly, I found many neurons, distributed throughout the brain, that directly encoded thirst. These results suggest that even simple behaviors, such as thirst, are emergent properties of the entire brain. I hope these new approaches will at last enable us to comprehend the rules that transform distributed patterns of electrical activity in neural circuits into thoughts, emotions, and perceptions. Understanding how molecules, neurons, and networks interact to shape these rules will have a sweeping impact on our understanding of brain function in health and disease. 1. [↵][12]“Mas, por desgracia, faltábanos el arma poderosa con que descuajar la selva impenetrable de la substancia gris…” ([ 10 ][13]). 2. [↵][14]1. C. A. Zimmerman, 2. D. E. Leib, 3. Z. A. Knight , Nat. Rev. Neurosci. 18, 459 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 3. [↵][18]1. S. M. Sternson , Neuron 77, 810 (2013). [OpenUrl][19][CrossRef][20][PubMed][21][Web of Science][22] 4. [↵][23]1. W. E. Allen et al ., Science 357, 1149 (2017). [OpenUrl][24][Abstract/FREE Full Text][25] 5. [↵][26]1. X. Wang et al ., Science 361, eaat5691 (2018). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]1. L. Ye et al ., Cell 165, 1776 (2016). [OpenUrl][30][CrossRef][31][PubMed][32] 7. [↵][33]1. W. E. Allen et al ., Neuron 94, 891 (2017). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. J. J. Jun et al ., Nature 551, 232 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 9. [↵][41]1. W. E. Allen et al ., Science 364, eeav3932 (2019). [OpenUrl][42] 10. [↵][43]1. S. Ramón y Cajal , Recuerdos de mi vida: Historia de mi labor científica (Moya, Madrid, 1917). [1]: http://www.sciencemag.org/content/370/6519/925.3 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: pending:yes [8]: #ref-9 [9]: #ref-6 [10]: #ref-7 [11]: #ref-8 [12]: #xref-ref-1-1 "View reference 1 in text" [13]: #ref-10 [14]: #xref-ref-2-1 "View reference 2 in text" [15]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BNeurosci%26rft.volume%253D18%26rft.spage%253D459%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrn.2017.71%26rft_id%253Dinfo%253Apmid%252F28638120%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/external-ref?access_num=10.1038/nrn.2017.71&link_type=DOI [17]: /lookup/external-ref?access_num=28638120&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [18]: #xref-ref-3-1 "View reference 3 in text" [19]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D77%26rft.spage%253D810%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuron.2013.02.018%26rft_id%253Dinfo%253Apmid%252F23473313%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/external-ref?access_num=10.1016/j.neuron.2013.02.018&link_type=DOI [21]: /lookup/external-ref?access_num=23473313&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [22]: /lookup/external-ref?access_num=000316162600004&link_type=ISI [23]: #xref-ref-4-1 "View reference 4 in text" [24]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DAllen%26rft.auinit1%253DW.%2BE.%26rft.volume%253D357%26rft.issue%253D6356%26rft.spage%253D1149%26rft.epage%253D1155%26rft.atitle%253DThirst-associated%2Bpreoptic%2Bneurons%2Bencode%2Ban%2Baversive%2Bmotivational%2Bdrive%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aan6747%26rft_id%253Dinfo%253Apmid%252F28912243%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNTcvNjM1Ni8xMTQ5IjtzOjQ6ImF0b20iO3M6MjQ6Ii9zY2kvMzcwLzY1MTkvOTI1LjMuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [26]: #xref-ref-5-1 "View reference 5 in text" [27]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aat5691%26rft_id%253Dinfo%253Apmid%252F29930089%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNjEvNjQwMC9lYWF0NTY5MSI7czo0OiJhdG9tIjtzOjI0OiIvc2NpLzM3MC82NTE5LzkyNS4zLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [29]: #xref-ref-6-1 "View reference 6 in text" [30]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D165%26rft.spage%253D1776%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2016.05.010%26rft_id%253Dinfo%253Apmid%252F27238022%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/external-ref?access_num=10.1016/j.cell.2016.05.010&link_type=DOI [32]: /lookup/external-ref?access_num=27238022&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [33]: #xref-ref-7-1 "View reference 7 in text" [34]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D94%26rft.spage%253D891%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuron.2017.04.017%26rft_id%253Dinfo%253Apmid%252F28521139%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/external-ref?access_num=10.1016/j.neuron.2017.04.017&link_type=DOI [36]: /lookup/external-ref?access_num=28521139&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [37]: #xref-ref-8-1 "View reference 8 in text" [38]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D551%26rft.spage%253D232%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature24636%26rft_id%253Dinfo%253Apmid%252F29120427%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: /lookup/external-ref?access_num=10.1038/nature24636&link_type=DOI [40]: /lookup/external-ref?access_num=29120427&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [41]: #xref-ref-9-1 "View reference 9 in text" [42]: {openurl}?query=rft.jtitle%253DScience%26rft.volume%253D364%26rft.spage%253Deeav3932%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-10-1 "View reference 10 in text"

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Supplementary video: https://youtu.be/6WX2yEUE9_k

Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling Techniques (CHTs) on MOEA/D, a commonly used MOP solver when solving complex real-world MOPs. Moreover, we introduce a simple and effective CHT focusing on the exploration of the decision space, the Three Stage Penalty. We explore each of these CHTs in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The results of this work indicate that while the best CHT is problem-dependent, our new proposed Three Stage Penalty achieves competitive results and remarkable performance in terms of hypervolume values in the hard simulated car design MOP.

Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. Hence, it is increasingly important for a wide variety of applications, including shipping operations, coastal management, and risk assessment. However, direct high resolution surveys of nearshore bathymetry are rarely performed due to budget constraints and logistical restrictions. Another option when only sparse observations are available is to use Gaussian Process regression (GPR), also called Kriging. But GPR has difficulties recognizing patterns with sharp gradients, like those found around sand bars and submerged objects, especially when observations are sparse. In this work, we present several deep learning-based techniques to estimate nearshore bathymetry with sparse, multi-scale measurements. We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution. We train our neural networks based on synthetic data generated from nearshore surveys provided by the U.S.\ Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We compare our methods with Kriging on real surveys as well as surveys with artificially added sharp gradients. Results show that direct estimation by DNN gives better predictions than Kriging in this application. We use bootstrapping with DNN for uncertainty quantification. We also propose a method, named DNN-Kriging, that combines deep learning with Kriging and shows further improvement of the posterior estimates.

A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic. "The future is now," says Frédéric Vacher, head of innovation at Dassault Systèmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovation laboratory embraces the concept of the social enterprise and merges collective intelligence with a cross-collaborative approach by building what Vacher calls "communities of people--passionate and willing to work together to accomplish a common objective." This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "It's not only software, it's not only cloud, but it's also a community of people's skills and services available for the marketplace," Vacher says. "Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab." And for Dassault Systèmes, there's unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing together industry experts, health-care professionals, makers, and scientists to tackle covid-19. Vacher explains, "We created an open community, 'Open Covid-19,' to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country."

SoftBank Group Corp.'s founder Masayoshi Son said he has $80 billion in cash to buy back more shares and continue investing in both private and public companies. "If our shares drop down, I will buy back more shares more aggressively," the chief executive officer said at the New York Times DealBook conference Tuesday. "We have $80 billion in cash at hand." After a record fall in its share price in March, SoftBank unveiled plans to offload ¥4.5 trillion ($43.2 billion) in assets and buy back ¥2.5 trillion of its own stock. The idea of going private through a buyout has been discussed within SoftBank for at least five years, but Son declined to comment on whether he would take his company off the stock market.