Showing papers by "Chalmers University of Technology published in 2021"

First M87 Event Horizon Telescope Results. VIII. Magnetic Field Structure near the Event Horizon

Abstract: Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.; Cordes, James M.; Crawford, Thomas M.; Crew, Geoffrey B.; Cruz-Osorio, Alejandro; Cui, Yuzhu; Davelaar, Jordy; De Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Doeleman, Sheperd S.; Eatough, Ralph P.; Falcke, Heino; Farah, Joseph; Fish, Vincent L.; Fomalont, Ed; Ford, H. Alyson; Fraga-Encinas, Raquel; Friberg, Per; Fromm, Christian M.; Fuentes, Antonio; Galison, Peter; Gammie, Charles F.; Garcia, Roberto; Gelles, Zachary; Gentaz, Olivier; Georgiev, Boris; Goddi, Ciriaco; Gold, Roman; Gomez, Jose L.; Gomez-Ruiz, Arturo I.; Gu, Minfeng; Gurwell, Mark; Hada, Kazuhiro; Haggard, Daryl; Hecht, Michael H.; Hesper, Ronald; Himwich, Elizabeth; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Ikeda, Shiro; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jeter, Britton; Jiang, Wu; Jimenez-Rosales, Alejandra; Johnson, Michael D.; Jorstad, Svetlana; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kawashima, Tomohisa; Keating, Garrett K.; Kettenis, Mark; Kim, Dong-Jin; Kim, Jae-Young; Kim, Jongsoo; Kim, Junhan; Kino, Motoki; Koay, Jun Yi; Kofuji, Yutaro; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Krichbaum, Thomas P.; Kuo, Cheng-Yu; Lauer, Tod R.; Lee, Sang-Sung; Levis, Aviad; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Lindahl, Greg; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Loinard, Laurent; Lonsdale, Colin; Lu, Ru-Sen; MacDonald, Nicholas R.; Mao, Jirong; Marchili, Nicola; Markoff, Sera; Marrone, Daniel P.; Marscher, Alan P.; Marti-Vidal, Ivan; Matsushita, Satoki; Matthews, Lynn D.; Medeiros, Lia; Menten, Karl M.; Mizuno, Izumi; Mizuno, Yosuke; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Muller, Cornelia; Musoke, Gibwa; Mus Mejias, Alejandro; Michalik, Daniel; Nadolski, Andrew; Nagai, Hiroshi; Nagar, Neil M.; Nakamura, Masanori; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Nathanail, Antonios; Neilsen, Joey; Neri, Roberto; Ni, Chunchong; Noutsos, Aristeidis; Nowak, Michael A.; Okino, Hiroki; Olivares, Hector; Ortiz-Leon, Gisela N.; Oyama, Tomoaki; Ozel, Feryal; Palumbo, Daniel C. M.; Park, Jongho; Patel, Nimesh; Pen, Ue-Li; Pesce, Dominic W.; Pietu, Vincent; Plambeck, Richard; PopStefanija, Aleksandar; Porth, Oliver; Potzl, Felix M.; Prather, Ben; Preciado-Lopez, Jorge A.; Psaltis, Dimitrios; Pu, Hung-Yi; Ramakrishnan, Venkatessh; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Rezzolla, Luciano; Ricarte, Angelo; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Rygl, Kazi L. J.; Sanchez, Salvador; Sanchez-Arguelles, David; Sasada, Mahito; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; SooHoo, Jason; Sun, He; Tazaki, Fumie; Tetarenko, Alexandra J.; Tiede, Paul; Tilanus, Remo P. J.; Titus, Michael; Toma, Kenji; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Ward-Thompson, Derek; Wardle, John; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, Andre; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zensus, J. Anton; Zhao, Guang-Yao; Zhao, Shan-Shan; Event Horizon Telescope Collaboration.-- This is an open access article, original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

Abstract: Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Abstract: Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

First M87 Event Horizon Telescope Results. VII. Polarization of the Ring

Abstract: Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Balokovic, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.; Cordes, James M.; Crawford, Thomas M.; Crew, Geoffrey B.; Cruz-Osorio, Alejandro; Cui, Yuzhu; Davelaar, Jordy; De Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Doeleman, Sheperd S.; Eatough, Ralph P.; Falcke, Heino; Farah, Joseph; Fish, Vincent L.; Fomalont, Ed; Ford, H. Alyson; Fraga-Encinas, Raquel; Freeman, William T.; Friberg, Per; Fromm, Christian M.; Fuentes, Antonio; Galison, Peter; Gammie, Charles F.; Garcia, Roberto; Gentaz, Olivier; Georgiev, Boris; Goddi, Ciriaco; Gold, Roman; Gomez, Jose L.; Gomez-Ruiz, Arturo I.; Gu, Minfeng; Gurwell, Mark; Hada, Kazuhiro; Haggard, Daryl; Hecht, Michael H.; Hesper, Ronald; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Ikeda, Shiro; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jeter, Britton; Jiang, Wu; Jimenez-Rosales, Alejandra; Johnson, Michael D.; Jorstad, Svetlana; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kawashima, Tomohisa; Keating, Garrett K.; Kettenis, Mark; Kim, Dong-Jin; Kim, Jae-Young; Kim, Jongsoo; Kim, Junhan; Kino, Motoki; Koay, Jun Yi; Kofuji, Yutaro; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Krichbaum, Thomas P.; Kuo, Cheng-Yu; Lauer, Tod R.; Lee, Sang-Sung; Levis, Aviad; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Lindahl, Greg; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Loinard, Laurent; Lonsdale, Colin; Lu, Ru-Sen; MacDonald, Nicholas R.; Mao, Jirong; Marchili, Nicola; Markoff, Sera; Marrone, Daniel P.; Marscher, Alan P.; Marti-Vidal, Ivan; Matsushita, Satoki; Matthews, Lynn D.; Medeiros, Lia; Menten, Karl M.; Mizuno, Izumi; Mizuno, Yosuke; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Muller, Cornelia; Musoke, Gibwa; Mejias, Alejandro Mus; Michalik, Daniel; Nadolski, Andrew; Nagai, Hiroshi; Nagar, Neil M.; Nakamura, Masanori; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Nathanail, Antonios; Neilsen, Joey; Neri, Roberto; Ni, Chunchong; Noutsos, Aristeidis; Nowak, Michael A.; Okino, Hiroki; Olivares, Hector; Ortiz-Leon, Gisela N.; Oyama, Tomoaki; Ozel, Feryal; Palumbo, Daniel C. M.; Park, Jongho; Patel, Nimesh; Pen, Ue-Li; Pesce, Dominic W.; Pietu, Vincent; Plambeck, Richard; PopStefanija, Aleksandar; Porth, Oliver; Potzl, Felix M.; Prather, Ben; Preciado-Lopez, Jorge A.; Psaltis, Dimitrios; Pu, Hung-Yi; Ramakrishnan, Venkatessh; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Rezzolla, Luciano; Ricarte, Angelo; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Rygl, Kazi L. J.; Sanchez, Salvador; Sanchez-Arguelles, David; Sasada, Mahito; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; SooHoo, Jason; Sun, He; Tazaki, Fumie; Tetarenko, Alexandra J.; Tiede, Paul; Tilanus, Remo P. J.; Titus, Michael; Toma, Kenji; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Ward-Thompson, Derek; Wardle, John; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, Andre; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zensus, J. Anton; Zhao, Guang-Yao; Zhao, Shan-Shan; Event Horizon Telescope Collaboration.-- This is an open access article, original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

Abstract: Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc.

Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization

Abstract: A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell’s reflection law. However, the optimal control of the RIS requires perfect channel state information (CSI) of the individual channels that link the base station (BS) and the mobile station (MS) to each other via the RIS. Thereby super-resolution channel (parameter) estimation needs to be efficiently conducted at the BS or MS with CSI feedback to the RIS controller. In this paper, we adopt a two-stage channel estimation scheme for RIS-aided millimeter wave (mmWave) MIMO systems without a direct BS-MS channel, using atomic norm minimization to sequentially estimate the channel parameters, i.e., angular parameters, angle differences, and the products of propagation path gains. We evaluate the mean square error of the parameter estimates, the RIS gains, the average effective spectrum efficiency bound, and average squared distance between the designed beamforming and combining vectors and the optimal ones. The results demonstrate that the proposed scheme achieves super-resolution estimation compared to the existing benchmark schemes, thus offering promising performance in the subsequent data transmission phase.

A pluralistic and integrated approach to action-oriented knowledge for sustainability

Abstract: Sustainability science needs more systematic approaches for mobilizing knowledge in support of interventions that may bring about transformative change. In this Perspective, we contend that action-oriented knowledge for sustainability emerges when working in integrated ways with the many kinds of knowledge involved in the shared design, enactment and realization of change. The pluralistic and integrated approach we present rejects technocratic solutions to complex sustainability challenges and foregrounds individual and social learning. We argue that research institutions devoted to sustainability should focus more on creating the conditions for experimenting with multiple kinds of knowledge and ways of knowing to foster sustainability-oriented learning. Sustainability science needs to better mobilize a range of knowledge to support transformative change. This Perspective contends that such transformative, action-oriented knowledge emerges from integrating multiple kinds of knowledge and ways of knowing.

Giant optical anisotropy in transition metal dichalcogenides for next-generation photonics.

Abstract: Large optical anisotropy observed in a broad spectral range is of paramount importance for efficient light manipulation in countless devices. Although a giant anisotropy has been recently observed in the mid-infrared wavelength range, for visible and near-infrared spectral intervals, the problem remains acute with the highest reported birefringence values of 0.8 in BaTiS3 and h-BN crystals. This issue inspired an intensive search for giant optical anisotropy among natural and artificial materials. Here, we demonstrate that layered transition metal dichalcogenides (TMDCs) provide an answer to this quest owing to their fundamental differences between intralayer strong covalent bonding and weak interlayer van der Waals interaction. To do this, we made correlative far- and near-field characterizations validated by first-principle calculations that reveal a huge birefringence of 1.5 in the infrared and 3 in the visible light for MoS2. Our findings demonstrate that this remarkable anisotropy allows for tackling the diffraction limit enabling an avenue for on-chip next-generation photonics. Optical anisotropy in a broad spectral range is pivotal to efficient light manipulation. Here, the authors measure a birefringence of 1.5 in the infrared range and 3 in the visible light for MoS2.

Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

Abstract: Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.

Expanding functional protein sequence spaces using generative adversarial networks

Abstract: De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, we develop ProteinGAN, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space. A protein’s three-dimensional structure and properties are defined by its amino-acid sequence, but mapping protein sequence to protein function is a computationally highly intensive task. A new generative adversarial network approach learns from natural protein sequences and generates new, diverse protein sequence variations, which are experimentally tested.

Ultra-fast proteomics with Scanning SWATH.

Abstract: Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Three Decades of Climate Mitigation: Why Haven't We Bent the Global Emissions Curve?

Abstract: Despite three decades of political efforts and a wealth of research on the causes and catastrophic impacts of climate change, global carbon dioxide emissions have continued to rise and are 60% high...

Insight into the Critical Role of Exchange Current Density on Electrodeposition Behavior of Lithium Metal

Abstract: Due to an ultrahigh theoretical specific capacity of 3860 mAh g-1, lithium (Li) is regarded as the ultimate anode for high-energy-density batteries. However, the practical application of Li metal anode is hindered by safety concerns and low Coulombic efficiency both of which are resulted fromunavoidable dendrite growth during electrodeposition. This study focuses on a critical parameter for electrodeposition, the exchange current density, which has attracted only little attention in research on Li metal batteries. A phase-field model is presented to show the effect of exchange current density on electrodeposition behavior of Li. The results show that a uniform distribution of cathodic current density, hence uniform electrodeposition, on electrode is obtained with lower exchange current density. Furthermore, it is demonstrated that lower exchange current density contributes to form a larger critical radius of nucleation in the initial electrocrystallization that results in a dense deposition of Li, which is a foundation for improved Coulombic efficiency and dendrite-free morphology. The findings not only pave the way to practical rechargeable Li metal batteries but can also be translated to the design of stable metal anodes, e.g., for sodium (Na), magnesium (Mg), and zinc (Zn) batteries.

RadChat: Spectrum Sharing for Automotive Radar Interference Mitigation

Abstract: In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar and communication cooperation. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms.

Reconfigurable Intelligent Surfaces: A Signal Processing Perspective With Wireless Applications.

Abstract: A reconfigurable intelligent surface (RIS) is a two-dimensional surface of engineered material whose properties are reconfigurable rather than static [4]. For example, the scattering, absorption, reflection, and diffraction properties can be changed with time and controlled by software. In principle, the surface can be used to synthesize an arbitrarily-shaped object of the same size, when it comes to how electromagnetic waves interact with it [5]. The long-term vision of the RIS technology is to create smart radio environments [9], where the wireless propagation conditions are co-engineered with the physical-layer signaling, and investigate how to utilize this new capability. The common protocol stack consists of seven layers and wireless technology is chiefly focused on the first three layers (physical, link, and network) [10]. An RIS operates at what can be referred to as Layer 0, where the traditional design issue is the antennas of the transmitter/receivers; one can think of RIS as extending the antenna design towards the environment, commonly seen as uncontrollable and decided by "nature". This approach can profoundly change the wireless design beyond 5G. This article provides a tutorial on the fundamental properties of the RIS technology from a signal processing perspective, to complement the recent surveys of electromagnetic and hardware aspects [4], [7], communication theory [11], and localization [8]. We will provide the formulas and derivations that are required to understand and analyze RIS-aided systems, and exemplify how they can be utilized for improved communication, localization, and sensing. We will also elaborate on the fundamentally new possibilities enabled by Layer 0 engineering and electromagnetic phenomena that remain to be modeled and utilized for improved signal processing.

Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Abstract: Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This paper proposes a knowledge-based, multi-physics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multi-objective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to trade-off smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.

Fatty acid synthesis is required for breast cancer brain metastasis.

Abstract: Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in brain. We determine that this phenotype is an adaptation to decreased lipid availability in brain relative to other tissues, resulting in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.

A time-resolved proteomic and prognostic map of COVID-19.

Abstract: COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community.

Abstract: Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning

Abstract: The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples, by leveraging on the network’s predictions for respecting object boundaries. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results. Lastly, we also provide extensive ablation studies comparing different design decisions and training regimes.

Graph networks for molecular design

Abstract: Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resembling the training set molecules without any explicit programming of chemical rules. The models have been benchmarked using the MOSES distribution-based metrics, showing how GraphINVENT models compare well with state-of-the-art generative models. This work is one of the first thorough graph-based molecular design studies, and illustrates how GNN-based models are promising tools for molecular discovery.

Reconfigurable, Intelligent, and Sustainable Wireless Environments for 6G Smart Connectivity

Abstract: Various visions of the forthcoming sixth generation (6G) networks point toward flexible connect-and-compute technologies to support future innovative services and the corresponding use cases. 6G should be able to accommodate ever evolving and heterogeneous applications, future regulations, and diverse user-, service-, and location-based requirements. A key element toward building smart and energy sustainable wireless systems beyond 5G is the reconfigurable intelligent surface (RIS), which offers programmable control and shaping of the wireless propagation environment. Capitalizing on this technology potential, in this article we introduce two new concepts: i) wireless environment as a service, which leverages a novel RIS-empowered networking paradigm to trade off diverse, and usually conflicting, connectivity objectives; and ii) performance-boosted areas enabled by RIS-based connectivity, representing competing service provisioning areas that are highly spatially and temporally focused. We discuss the key technological enablers and research challenges with the proposed networking paradigm, and highlight the potential profound role of RISs in the recent Open Radio Access Network architecture.

Tunable Chiral Bound States with Giant Atoms.

Abstract: We propose tunable chiral bound states in a system composed of superconducting giant atoms and a Josephson photonic-crystal waveguide (PCW), with no analog in other quantum setups. The chiral bound states arise due to interference in the nonlocal coupling of a giant atom to multiple points of the waveguide. The chirality can be tuned by changing either the atom-waveguide coupling or the external bias of the PCW. Furthermore, the chiral bound states can induce directional dipole-dipole interactions between multiple giant atoms coupling to the same waveguide. Our proposal is ready to be implemented in experiments with superconducting circuits, where it can be used as a tunable toolbox to realize topological phase transitions and quantum simulations.