The movie will perhaps force us to ask questions about topics usually reserved for the back pages of a newspaper

Personal: I ran headfirst into a bit of a classic burnout two years ago. I’m still recovering from it. I’ve been trying to maintain a presence and not make this condition show too much, but I need to scale down the rest of my presence too for a while in order to reset and recharge.

I’ve been starting and re-starting writing this post way too many times now. I’ve decided to just post it as a stream of consciousness, readable or not as it may be, rather than my usual bar of having some sort of clear red thread with step-by-step logical coherence.

Two years ago, while moving from Stockholm to Berlin, I hit the infamous brick wall. I became incapable of most work that required any form of vehicular travel — I was literally limited to walking distance. Yes, it felt as ridiculous as it sounds, but it was just a matter of accepting the lay of the land and working with it. At the time, I was able to maintain some illusion of normality while starting to wind down and recover behind the scenes, thanks to being able to work remotely. I’ve since stopped working altogether — or so I thought, at least — and focusing on recharging.

When you drive a solar-powered rover too aggressively in Kerbal Space Program and the sun goes down, the batteries deplete quickly. You can’t start driving the rover again when the sun goes up from its state of depleted batteries, not even at its rated speed; you have to wait until the batteries have recharged, even if the circumstances (i.e. shining sun) should otherwise make you able to operate nominally. This is a little bit the state I’m in: I should nominally be fine, with most of the everyday load reduced significantly, but my batteries are still not recharging at the rate I had expected them to. (Yes, I’m impatient, which is admittedly part of the problem in the first place.)

So to all people who have written to me over this past time that I haven’t responded to: Please accept my apologies. It’s not out of malice or disinterest I haven’t responded, I’m simply getting done in a month what I used to get done in a day, and even that is a marked improvement. The “need to respond” queue is silly long by now, and includes conference invites and whatnot, that I would normally have responded to within minutes. It includes pings from near friends, that I had hoped to spend a lot more time with here in Berlin, as well as distant friends.

A close friend of mine pointed at a recent study about stress, a study looking at burnout symptoms in places with very good work-to-life balance, and the study concluded that the body doesn’t make a difference between obligations for work or obligations that are felt outside of work for any other reason than money. And she’s right: I’ve been feeling a pressure to shoot video, to code open-source projects, to participate in the community. I need to, bluntly speaking, drop all of these expectations for the foreseeable future. “Go off-grid” is a little too harsh, but I’ll need to turn off the expectation heartbeat on literally everything. I’ll do random things from time to time when I have the energy and desire for it, which unfortunately won’t be most of the time.

These recoveries basically take whatever damn time they please. I could have recharged batteries in six months, in a year, in ten years. I have honestly no idea and therefore I’m not setting any expectations, in either direction.

I’d like to say “I’ll be back”, but I don’t think the person on the other side of this recovery is going to be the same person I am today. I am sure I will still want to change the world for the better, somehow. I just can’t tell today how I’ll be wanting to change the world tomorrow. So even though I’ll very likely be back doing something, it’ll very likely not be the exact same things I’ve done up until this point.

Toy giant Mattel says it "deeply" regrets an error on the packaging of its "Wicked" movie-themed dolls, which mistakenly links toy buyers to a pornographic website.

Senate Intelligence Committee Chairman Mark R. Warner says that if the Nov. 5 vote is as close as anticipated, U.S. adversaries can be expected to ramp up digital disinformation operations with the goal of sowing chaos, discord and confusion among Americans during the days immediately following the election.

Everyone who grinds it out day after day in the construction trenches, has at least one story of “stupidity unbecoming a human.” Some stories have tragic endings, while others generate belly laughs for years to come.

Los Angeles – The Los Angeles garment manufacturing industry – the nation’s largest cut-and-sew apparel base – is “plagued by workplace violations and marked by a lack of worker protections,” according to a new report released by the Garment Worker Center, the UCLA Labor Center and UCLA Labor Occupational Safety and Health.

3xLOGIC, a provider of integrated and intelligent security and business solutions, will debut its recently launched edge based deep learning analytics cameras at ISC West 2024, Booth #23059.

Washington – Contending that it lacks proper regulatory authority, the Chemical Safety Board on Nov. 14 voted to withdraw its recommendations issued to the Bureau of Safety and Environmental Enforcement after its investigation into the April 2010 explosion and fire that killed 11 workers on the Deepwater Horizon oil rig in the Gulf of Mexico.

Washington — A new report concludes that offshore oil and gas operations have become safer since the 2010 Deepwater Horizon disaster, but it finds “little evidence” that the industry is working together on improving safety culture.

Washington — Decreases in lung function observed among cleanup workers shortly after the 2010 Deepwater Horizon oil rig disaster were no longer apparent within the next few years, results of a new study from the National Institute of Environmental Health Sciences indicate – suggesting that some adverse health effects linked to the spill may resolve over time.

Washington — Workers involved in cleanup after the 2010 Deepwater Horizon oil rig disaster were significantly more likely to have been diagnosed with asthma or experienced asthma symptoms within three years of the incident, according to a new study from the National Institute of Environmental Health Sciences.

The funding round included participation from Shasta Ventures, Slow Ventures, UP2398 and TheSyndicate.com, an angel investing club led by Jason Calacanis.

Practicing mindful breathing can help you relieve stress and focus on the present moment, safety pro and motivational speaker Richard Hawk says.

Deep Indian Kitchen's new Kati Street Wraps deliver a unique toasted texture and a vibrant, rich flavor experience - just like the Katis made famous by street carts in India and Indian restaurants in America.

Children's Geographies; 08/01/2022
(AN 158427718); ISSN: 14733285
Academic Search Premier

Children's Geographies; 08/01/2024
(AN 178911405); ISSN: 14733285
Academic Search Premier

The post Co-knowledge production linking science, communities and public policy under deep uncertainty in a modern risk society in Japan was curated by information for practice.

Virtual Zoom event
Thursday, November 14, 2024, 7 – 8:30pm

Sycamore Land Trust and Citizens’ Climate Lobby Indiana present a free lecture and Q&A with Dr. Richard Phillips “From Tree Tops to Deep Roots: The Role of Eastern Forests as Carbon Sinks.” We’ll discuss how eastern forest ecosystems serve as important carbon sinks that can help mitigate rapid climate change, and explore above- and below-ground processes in forests and how they contribute to the land sink for carbon. Dr. Richard Phillips is a Professor of Biology at Indiana University, Bloomington, Director of the Ecology and Evolutionary Biology Graduate Program, and Science Director at IU Research and Teaching Preserve.

Presenter: Sycamore Land Trust and Citizens' Climate Lobby Indiana
Contact: Kate Hammel, Communications Director
Cost: Free
Ticket Phone: 812-336-5382
Ticket Web Link: sycamorelandtrust.org…
Communities: Bedford, Bloomington, Brown County, Columbus, Franklin, French Lick/West Baden, Greencastle, Greene County, Greensburg, Greenwood, Indianapolis, Kokomo, Martinsville, Seymour, Spencer, Statewide, Terre Haute
More info: sycamorelandtrust.org…

While discounts and sales events have long been associated with holiday shopping, new data from e-commerce provider Visualsoft reveals that consumers are motivated by more than just deep discounts when it comes to their seasonal and gifting purchases.

Added February 17, 2006:

Explore Aquarius Season's unique energy, from innovation to social connection. Learn how this zodiac period influences creativity, relationships, and self-expression.

Explore Cancer and Taurus compatibility, where stability meets sensitivity. Learn how these signs create lasting love through trust, loyalty, and mutual understanding.

Deep-sea mining evidence review published  British Geological Survey

New report assesses deep geothermal energy in the UK  British Geological Survey

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

A deep-learning algorithm is proposed for the inpainting of Bragg coherent diffraction imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artefacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of the method lies in the choice of training the neural network with cropped sections of diffraction data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This approach enables access to a greater amount of experimental data for training and offers the ability to average overlapping sections during patching. As a result, it produces robust and dependable predictions for experimental data arrays of any size. It is shown that the method is able to remove gap-induced artefacts on the reconstructed objects for both simulated and experimental data, which becomes essential in the case of high-resolution BCDI experiments.

Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.

A new large-scale, multidisciplinary ocean exploration program would increase the pace of discovery of new species - ecosystems, energy sources, seafloor features, pharmaceutical products, and artifacts, as well as improve understanding of the role oceans play in climate change.

The numerous technical and operational breakdowns that contributed to the Deepwater Horizon oil rig explosion and spill from the Macondo well in the Gulf of Mexico suggest the lack of a suitable approach for managing the inherent risks.

Educational and business leaders want todays students both to master school subjects and to excel in areas such as problem solving, critical thinking, and communication

The ocean may have something to teach us about the pandemic we’re grappling with, according to oceanographer and National Academy of Sciences member Jody Deming. Deming is a member of the Ocean Memory Project — a collaboration of scientists, artists, and others who are exploring how changes over time are encoded into ocean “memories.”

Most of the offshore oil and gas industry operating in the Gulf of Mexico has improved its management of systemic risk in recent years, according to a new report that also points out where uneven progress and critical gaps remain for industry and regulators to address.

As financial institutions embark on this data-led journey, they stand at the threshold of a new era—a future where innovation and data-driven decision-making will carve the path for the financial world's evolution.

The National Deep Tech Startup Policy (NDTSP) Consortium had released the draft policy on Jul 31 for public consultation, and sought feedback till September 15.

Even as it’s suffering from a heavy debt load, billionaire Anil Agarwal’s metals and mining conglomerate is expanding in electronics components to take advantage of India’s push to become a technology manufacturing hub. The display business is separate from Vedanta’s struggling chip venture and may find an easier path to success as it’s a less techically demanding undertaking.

The pizza reportedly includes the blend of Lou's buttery pizza topped with Portillo's slow-roasted Italian beef.

Bagels continue to tempt consumers, with better-for-you versions attracting attention and dollars.

VITO Fryfilter, Inc. recently released an intensive cleaner individually packed as tabs, for an effective cleaning of commercial deep fryers: the VITO tab.

Did you know that it only takes 30 seconds to create a deepfake? Deepfakes are AI-generated media that can be made using any device. They can be used to spread misinformation and harm a plumbing and mechanical company's reputation.

Most readers are familiar with the common phrase, “The errors of our ways.“ So why am I talking about the intention of our ways -- not errors – in this article?

Pradeep Singh recognized as a visionary in network management solutions and cutting-edge product development