Photoacoustic (PA) computed tomography (PACT) shows great potentials in various preclinical and clinical applications. A great number of measurements are the premise that obtains a high-quality image, which implies a low imaging rate or a high system cost. The artifacts or sidelobes could pollute the image if we decrease the number of measured channels or limit the detected view. In this paper, a novel compressed sensing method for PACT using an untrained neural network is proposed, which decreases half number of the measured channels and recoveries enough details. This method uses a neural network to reconstruct without the requirement for any additional learning based on the deep image prior. The model can reconstruct the image only using a few detections with gradient descent. Our method can cooperate with other existing regularization, and further improve the quality. In addition, we introduce a shape prior to easily converge the model to the image. We verify the feasibility of untrained network based compressed sensing in PA image reconstruction, and compare this method with a conventional method using total variation minimization. The experimental results show that our proposed method outperforms 32.72% (SSIM) with the traditional compressed sensing method in the same regularization. It could dramatically reduce the requirement for the number of transducers, by sparsely sampling the raw PA data, and improve the quality of PA image significantly.

A multidisciplinary team from the Idaho and Argonne National Laboratories, Kairos Power, and Curtiss-Wright, along with support from academics, have developed digital twin nuclear reactors. By using a US$5.2 million grant from the Department of Energy's Advanced Research Projects Agency-Energy, the scientists and engineers have engaged a physics-based machine learning process to construct and later maintain the digital twin reactors. By grounding the machine learning algorithm in actual physics, the artificial intelligence model generates predictions that are more robust and reliable when compared to more abstract models. The complex nature of this approach provides two layers of problem-solving simultaneously. First, a machine learning-driven predictive maintenance system actively avoids unexpected outages while optimizing maintenance, and predicts mechanical failure before prototypical mechanical stress indicates as much.

New technology from Stanford scientists finds long-hidden quakes, and possible clues about how earthquakes evolve. Tiny movements in Earth's outermost layer may provide a Rosetta Stone for deciphering the physics and warning signs of big quakes. New algorithms that work a little like human vision are now detecting these long-hidden microquakes in the growing mountain of seismic data. As part of his PhD studies in geophysics, he sat scanning earthquake signals recorded the night before, verifying that decades-old algorithms had detected true earthquakes rather than tremors generated by ordinary things like crashing waves, passing trucks or stomping football fans. "I did all this tedious work for six months, looking at continuous data," Mousavi, now a research scientist at Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth), recalled recently.

Nvidia and the National Energy Research Scientific Computing Center (NERSC) on Thursday flipped the "on" switch for Perlmutter, billed as the world's fastest supercomputer for AI workloads. Named for astrophysicist Saul Perlmutter, the new supercomputer boasts 6,144 NVIDIA A100 Tensor Core GPUs and will be tasked with stitching together the largest ever 3D map of the visible universe, among other projects. Perlmutter is "the fastest system on the planet" at processing workloads with the 16-bit and 32-bit mixed-precision math used in artificial intelligence (AI) applications, said Nvidia global HPC/AI product marketing lead Dion Harris during a press briefing earlier this week. Later this year, a second phase will add even more AI supercomputing power to Perlmutter, which is housed at NERSC at the Lawrence Berkeley National Laboratory. "In one project, the supercomputer will help assemble the largest 3D map of the visible universe to date. It will process data from the Dark Energy Spectroscopic Instrument (DESI), a kind of cosmic camera that can capture as many as 5,000 galaxies in a single exposure," Harris wrote in a blog post announcing the news.

This review deals with Restricted Boltzmann Machine (RBM) under the light of statistical physics. The RBM is a classical family of Machine learning (ML) models which played a central role in the development of deep learning. Viewing it as a Spin Glass model and exhibiting various links with other models of statistical physics, we gather recent results dealing with mean-field theory in this context. First the functioning of the RBM can be analyzed via the phase diagrams obtained for various statistical ensembles of RBM leading in particular to identify a {\it compositional phase} where a small number of features or modes are combined to form complex patterns. Then we discuss recent works either able to devise mean-field based learning algorithms; either able to reproduce generic aspects of the learning process from some {\it ensemble dynamics equations} or/and from linear stability arguments.

We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. After training, the GAN can be used to predict the evolution of the spatial distribution of the simulation states and observed data is assimilated. In this paper, we describe the process required in order to quantify uncertainty, during which no additional simulations of the high-fidelity numerical model are required. These methods take advantage of the adjoint-like capabilities of generative models and the ability to simulate forwards and backwards in time. Set within a reduced-order model framework for efficiency, we apply these methods to a compartmental model in epidemiology to predict the spread of COVID-19 in an idealised town. The results show that the proposed method can efficiently quantify uncertainty in the presence of measurements using only unconditional simulations of the high-fidelity numerical model.

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. A recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation. They provide a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Then, a probabilistic forecast-driven strategy is designed, modeled as a min-max-min robust optimization problem with recourse, and solved using a Benders decomposition. The convergence is improved by building an initial set of cuts derived from domain knowledge. Robust optimization models the generation randomness using an uncertainty set that includes the worst-case generation scenario and protects this scenario under the minimal increment of costs. This approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. Finally, a dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution provides an additional gain. The case study uses the photovoltaic generation monitored on-site at the University of Li\`ege (ULi\`ege), Belgium.

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

When the constitution of the American Association for the Advancement of Science was revised in 1946, its statement of objectives contained new language: “…to increase public understanding and appreciation of the importance and promise of the methods of science in human progress.” The association has since fulfilled that charge in diverse sectors, including policy, education, and public engagement, to make science more relatable and relevant to the public. Making science relatable also requires a variety of engagement strategies, including facilitating in-depth discussions with local policy leaders, translating technical language into digestible summaries for the classroom, and promoting science role models. In the case of the AAAS Center for Scientific Evidence in Public Issues or EPI Center, for instance, a successful part of bringing clear and actionable scientific advice to policy-makers has been encouraging discussions among a broad group of experts and policy peers. During meetings organized by the EPI Center this year, city council members, mayors, water engineers, and local utility managers joined scientists to discuss perand polyfluoroalkyl substances or PFAS, synthetic chemicals found in drinking water systems. At least two PFAS have been associated with increased rates of some cancers and thyroid disease. The EPI Center provides nontechnical syntheses of topics for policy-makers, “but one thing we have seen is that examples from their peers that have implemented and used the scientific evidence are much more valuable and easier to understand,” said Kathryn McGrath, communications director for the center. Whether the focus is clean water or voting technology or hydraulic fracturing, the EPI Center strives to make the science of these topics relatable by talking with the public and policy-makers to find out exactly what information would be helpful for them. The discussions allow city council members, for instance, “to ask the science experts what they need to know to go back to their communities and regions and take action on some of these issues,” McGrath said. AAAS's Local Science Engagement Network, a grassroots platform that nurtures local and state science advocates for climate and energy policy, has also found success with local partnerships. In Colorado, Missouri, and Georgia, LSENs work with organizations in each state that “have a good sense of policy landscapes as well as the cultural and scientific landscapes in those areas,” said Daniel Barry, local and state advocacy director and head of LSEN at AAAS. LSENs offer an avenue for engagement and advocacy that AAAS members have been asking for, by connecting scientists with their own elected representatives on the local, state, and federal levels. As both constituents and neutral, honest brokers of scientific information, LSEN participants can be a key resource when legislatures grapple with the more local implications of climate change, such as modernizing the state power grid, said Barry. “They can step up and say, ‘Science, that's what I do, and I live here in this community. I know how to get you the science you need.’” LSEN members also condense technical research into locally relevant analyses in plain English for business leaders and citizens. So far in 2021, Missouri LSEN partner MOST Policy Initiative has produced more than 80 such “science notes” about pending state legislation. Among AAAS's numerous education efforts to make science more relevant is Science in the Classroom, an initiative that annotates and provides additional resources to accompany research papers from the Science family of journals. The goal is to make scientific papers more accessible to high school, community college, and undergraduate students, while putting a face on the papers' authors in communities with little exposure to working scientists, said program director Suzanne Thurston. The popular resource had more than 1 million page views in the past 3 years, and the hunger for accessible scientific content during a pandemic year led to a 50% increase in total site visits in 2020 compared to 2019. The program also offers professional development workshops to educators, researchers, and annotators. By showcasing a range of authors and annotators, Science in the Classroom helps “to expose students to diversity within STEM and demonstrates what ‘actual living scientists’ look like,” said Thurston, who serves as a program director in AAAS's Inclusive STEM Ecosystems for Equity and Diversity (ISEED). The IF/THEN Ambassador program, led by AAAS's Center for Public Engagement with Science and Technology, was another recent effort to show off the diverse faces of science, by highlighting 125 women in STEM as role models for middle school girls. Lyda Hill Philanthropies, which funds the IF/THEN initiative, wanted to work with AAAS on the ambassador program after the association's success with other public engagement initiatives such as the AAAS Mass Media Science & Engineering Fellowship and the Leshner Leadership Institute for Public Engagement with Science, said Emily Therese Cloyd, director of the AAAS Center for Public Engagement with Science and Technology. The ambassador program was distinguished by its emphasis on increasing visibility for women in STEM who demonstrate how science is involved in everyday careers beyond the traditional lab, said Cloyd. “We're moving beyond scientists who work at an academic institution and thinking about the ways that a video game designer or a fashion designer might be using STEM every day.” AAAS is committed to making science relatable and relevant for everyone from policy-makers to educators to students. It is at the core of the organization's mission and will continue to be a top priority for years to come.

Exoskeletons and exosuits are wearable devices designed to work alongside the musculoskeletal system and reduce the effort needed to walk or run. Exoskeletons can benefit users by reducing the mechanical power and metabolic energy that they need to move about on the factory floor, in the rehabilitation clinic, on the playing field, and out at the shopping mall ([ 1 ][1]). Portable exoskeletons can use motors to add mechanical power into movement phases [net-positive exoskeleton power ([ 2 ][2], [ 3 ][3])] or use springs to store and later return mechanical energy in a regenerative braking action [net-zero exoskeleton power ([ 4 ][4], [ 5 ][5])]. On page 957 of this issue, Shepertycky et al. ([ 6 ][6]) describe a wearable assistive device that uses a generator to extract mechanical energy from the walking cycle (net-negative power) and convert it to electricity. At the same time, the walker actually uses less metabolic energy with the exosuit, saving on the cost to operate muscles as “biological brakes.” Handgrip and pedal-powered dynamos have long been in use and can convert mechanical power to electrical power, and these devices can have efficiencies as high as 70% ([ 7 ][7]). More recently, “hands-free” energy harvesters have been developed that can be worn on the back ([ 8 ][8]) or attached with an exoskeletal structure around the lower-limb joints ([ 9 ][9]–[ 11 ][10]). A performance metric for these devices is the cost of harvesting (COH), which is the ratio of the change in a user's metabolic power (measured in watts) when moving with versus without the device to the electrical power generated by the device. A positive COH means that the user must provide additional metabolic effort to generate electricity. For the examples above, the reported COH values have ranged from 4.8 for the back-mounted device ([ 8 ][8]) to 0.7 for a knee-joint mount ([ 9 ][9]). This latter device developed by Donelan et al. ([ 9 ][9]) incorporated principles from fundamental movement biomechanics to strategically target phases of human walking where the lower-limb joints already resist motion (negative mechanical power) and behave effectively as brakes. Biomechanical analyses combining data from high-speed motion capture and instrumented force platforms with inverse-dynamics calculations reveal that the knee joint acts mostly like a brake during walking, especially at the end of the swing phase, when the foot is in the air (see the figure, top left). Muscles convert metabolic power to mechanical power with 25% efficiency when acting as motors (positive mechanical power output) and −125% efficiency when acting as brakes (negative mechanical power output) ([ 12 ][11]). ![Figure][12] Charging ahead by braking Shepertycky et al. developed an exosuit that reduces the metabolic energy needed by muscles to resist motion during gait. A generator provides the “braking” force and produces electricity. GRAPHIC: V. ALTOUNIAN/ SCIENCE Donelan et al. designed a knee exoskeleton in which a rotary generator attached in parallel with the human knee worked to help off-load biological braking. The resistance of the generator to turning provided the braking torque. With this device, they established that by targeting phases of negative mechanical power, exoskeletons can generate electricity with minimal increase in user effort. If muscles had acted as motors to provide the 1.7 W of mechanical power needed to generate each 1 W of electricity (their device had a 60% conversion efficiency), then users would have had to expend 6.8 W more metabolic power. However, for each 1 W of generated electricity, users only expended 0.7 W of metabolic energy (COH = 0.7). Although this system still required additional user effort, the results suggested that energy can be harvested from gait while at the same time saving metabolic energy—a negative COH. This result highlights a key difference between skeletal muscle and engineered systems, namely, that braking is energetically cheap for machines (like a bicycle hand brake) but expensive for muscles, which have to consume metabolic energy to tense up and maintain braking force, especially when changing length ([ 12 ][11]). Thus, properly timed exoskeleton resistance could provide a portion of the negative muscle power that is normally lost as heat. Rather than requiring additional user effort to perform positive mechanical work on the exoskeleton generator, exoskeleton negative power would save the user the metabolic energy needed for muscle braking ([ 13 ][13]). Shepertycky et al. designed a streamlined exosuit with a negative COH using a feedback-controlled “muscle-centric” loading profile. They specifically targeted the period during very late leg swing ( just before the foot makes contact with the ground) when large braking forces are produced by actively lengthening hamstring muscles (for example, biceps femoris), rather than metabolically inactive passive elastic structures (for example, tendons and ligaments) ([ 14 ][14]). Their “traditional” loading profile ([ 10 ][15]) extracted the same total mechanical energy but resulted in a 3% metabolic penalty. The relatively subtle shift in timing and magnitude of the “muscle-centric” profile resulted in a 2.5% net metabolic benefit—a 5.5% improvement. By strategically placing the device on the user's back, Shepertycky et al. were also able to reduce the carrying cost of their exosuit to just over 1%. This penalty is meager compared with the nearly 20% metabolic increase that was imposed by bulky knee-mounted exoskeletons that weighed 1.65 kg per leg ([ 9 ][9]). Their 1.1-kg device hardware rested at the waist near the user's center of mass. Exosuit support was supplied by tensioning cables that were routed along the posterior thigh and shank. The other ends of these cables were ultimately attached at the ankle to apply forces parallel to the hamstrings (see the figure, top middle). Shepertycky et al. 's energy-extracting exosuit, which achieves a net 2.5% reduction in the metabolic cost of walking along with 0.25 W of generated electricity, may only be the first of many such devices that could achieve a negative COH. Rough calculations based on engineering specifications for generators ([ 7 ][7]), locomotion biomechanics data ([ 15 ][16]), and fundamental muscle physiology relationships ([ 12 ][11]) suggest many opportunities to extend the principle of “resistive assistance” (see the figure, bottom). Targets include lower-limb joints other than the knee, gait phases other than terminal swing, and locomotion tasks other than walking on level ground. More intense gaits like running, where the legs cycle more positive and negative mechanical power, and tasks like walking downhill, descending staircases, or decelerating to a stop all provide increased opportunities for rigid exoskeletons or soft exosuits to assist the body's biological brakes while generating electricity. The next-generation exosuits will begin to integrate physiological sensing systems and machine-learning algorithms to increase the versatility and impact of wearable assistive devices. During the next decade, a new challenge may be the development of an exosuit that minimizes human metabolic energy expenditure on a round-trip course spanning many kilometers over many days with access to a single onboard rechargeable battery. Optimal performance will likely require multijoint, hybrid support strategies that combine injection, extraction, and transfer of both electrical and mechanical energy to adapt continuously to locomotion-task demands and reduce metabolic energy expenditure of the user. Such devices could have several applications, such as extending the range of on-foot search-and-rescue crews, outdoor adventurers, or soldiers on humanitarian missions. In the developing world, an exosuit could provide between 20 and 40% of the electricity needed per person on a typical day. The energy demands of portable electronics and increased recognition of the role of movement in longevity may drive exosuits toward widespread adoption. 1. [↵][17]1. G. S. Sawicki, 2. O. N. 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