With the growth of video content across various industries, Lumeo said the ability to efficiently analyze and derive insights from this data is crucial.

Parameters in analytical models for X-ray form factors of ions f0(s), based on the inverse Mott–Bethe formula involving a variable number of Gaussians, are determined for a wide range of published data sets {s, f0(s)}. The models reproduce the calculated form-factor values close to what is expected from a uniform statistical distribution with limits determined by their precision. For different ions associated with the same atom, the number of Gaussians in the models decreases with increasing net positive charge.

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

Animations, videos and 3D models have been designed to visualize the effects of symmetry operators on selected cases of crystal structures, pointing out the relationship with the diagrams published in International Tables for Crystallography, Vol. A.

A series of animations, videos and 3D models that were developed, filmed or built to teach the symmetry properties of crystals are described. At first, these resources were designed for graduate students taking a basic crystallography course, coming from different careers, at the National Autonomous University of Mexico. However, the COVID-19 pandemic had the effect of accelerating the generation of didactic material. Besides our experience with postgraduate students, we have noted that 3D models attract the attention of children, and therefore we believe that these models are particularly useful for teaching children about the assembled arrangements of crystal structures.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

A new report from the National Academies of Sciences, Engineering, and Medicine provides a blueprint and eight overarching strategies for improving health care for the approximately 100,000 people in the United States living with sickle cell disease (SCD).

New models for studying the human brain — human neural organoids, transplants, and chimeras — show promise for advancing understanding of the brain and laying the groundwork for new therapeutic approaches to brain diseases that have so far proved hard to treat, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Artificial intelligence (AI) based foundations models such as Meta's Llama 2 and OpenAI's GPT-4 are low on transparency, according to a global report.

Georgia-based HRS Heat Exchangers has been awarded the American 3-A Sanitary Standards approval for two of its key heat exchanger models for food and beverage use.

KPM Analytics has announced three new instrument configurations for its Unity Scientific SpectraStar product line.

 In the age of customer-centric business models, it's easy to overlook the fundamental role of employees in driving business success. Here's one company's story utilizing a focused approach to enhance employee engagement.

As consumers’ purchasing behaviors continue to impact the U.S. on-premise, experts note that the channel remains resilient amid fluctuating trends.

New Hunting, Cargo, eMTB models on display June 8-11 in Denver

Canadian Classroom and Inventive Classroom Partner with Creality to Introduce Four New 3D Printing Products to the North American Market

F-Series E-bikes now available. Special Promotional price.

Magnetic cushions and armrests, bold colors and a range of licensed designs. Gaming and lifestyle chair brand WARP has unveiled its new flagship line of ergonomic chairs named «Xd»

Standout features like Memosens digital connectivity set them apart

GL Homes is excited to announce the highly anticipated sale of it's cutting edge, contemporary model homes at Lotus Palm.

Homebuyers dreaming about owning a designer-decorated, fully-furnished home now have that opportunity with several stunning models for sale at Valencia Trails, Naples's premier 55+ living community, by Misha and Itchko Ezratti's team at GL Homes.

Misha Ezratti and Itchko Ezratti are thrilled to present an incredible opportunity for homebuyers in one of Palm Beach County's most exciting new communities near Wellington.

FFTV+ Disrupts Traditional Short Series Creation with Innovative Ecosystem at Consensus 2024

Tennr, the company automating the messy, manual work holding healthcare organizations back, is announcing the completion of a Series B funding round?led by Lightspeed Ventures with participation from existing investors a16z and Foundation Capital?having raised $37 million. Bringing Tennr's total capital raised to over $61 million, the latest funding will continue to support the company's technological breakthroughs in document automation for the healthcare industry.

Laboratory assays are central to much of biomedical research. My colleagues and I recently received a research grant to do better assays using Bayesian inference. Beyond the usual challenges of fitting nonlinear hierarchical models to real data that can sometimes … Continue reading →

Which ecommerce business model is best for your idea? Long-term and short-term strategies for operations, profits, and a successful launch.

Joas Pambou built an app that integrates vision language models (VLMs) and text-to-speech (TTS) AI technologies to describe images audibly with speech. This audio description tool can be a big help for people with sight challenges to understand what’s in an image. But how this does it even work? Joas explains how these AI systems work and their potential uses, including how he built the app and ways to further improve it.

In the second part of this series, Joas Pambou aims to build a more advanced version of the previous application that performs conversational analyses on images or videos, much like a chatbot assistant. This means you can ask and learn more about your input content.

In this third part of the series, you are looking at two models that handle all three modalities — text, images or videos, and audio — without needing a second model for text-to-speech or speech recognition.

Do certain exoplanets mirror Earth regarding their distribution of iron and water? This is what a recent study published in Nature Astronomy hopes to addre

Nov 14, 2024, 4pm EST

All are welcome to come to the Fung House (48 Professors Row, Medford) on Thursday, November 14, at 4 p.m. to hear Professor Barbara Graziosi from Princeton University discuss how AI can be used in the reconstruction of ancient texts. This event will be moderated by Gregory Crane, chair of the Tufts Department of Classical Studies.

Graziosi is the Ewing Professor of Greek Language and Literature, and the department chair of the Department of Classics at Princeton University. Her research focuses on ancient Greek literature and the ways in which different readers, through time and across the globe, make it their own.

This event is available in person and on Zoom. All are welcome. Refreshments will be served.

Building: Fung House 48 Professors Row
Campus Location: Medford/Somerville campus
City: Somerville, MA 02144
Campus: Medford/Somerville campus
Open to Public: Yes
Primary Audience(s): Faculty, Postdoctoral Fellows, Staff, Students (Graduate), Students (Postdoctoral)
Event Type: Lecture/Presentation/Seminar/Talk
Subject: Humanities, Innovation
Event Sponsor: School of Arts and Sciences
Event Sponsor Details: Tufts University
Event Contact Name: Amanda Pepper
Event Contact Email: amanada.pepper@tufts.edu
Event Contact Phone: 2037639353
RSVP Information: No RSVP needed
Event Admission: Free
More info: humanities.tufts.edu…

ETSI Encrypted Traffic Integration group extends term to work on cryptographic and key management models

Sophia Antipolis, 2 August 2022

ETSI has recently extended the term of its Industry Specification Group Encrypted Traffic Integration (ISG ETI) for a two-year period through to mid-2024 to work on specific cryptographic and key management models.

Read More...

Tenable®, the exposure management company, today announced the release of AI Aware, advanced detection capabilities designed to rapidly surface artificial intelligence solutions, vulnerabilities and weaknesses available in Tenable Vulnerability Management, the world’s #1 vulnerability management solution. Tenable AI Aware provides exposure insight into AI applications, libraries and plugins so organizations can confidently expose and close AI risk, without inhibiting business operations.

The rapid development and adoption of AI technologies in the past two years has introduced major cybersecurity and compliance risks that organizations must proactively address without established best practices. As a result, cybersecurity teams face significant AI-related challenges, such as vulnerability detection and remediation, containing data leakage and reining in unauthorized AI use. 

According to recent Tenable Research, more than one-third of security teams are finding usage of AI applications in their environment that might not have been provisioned via formal processes. In fact, during a 75-day period between late June and early September, Tenable found over 9 million instances of AI applications on more than 1 million hosts. The cybersecurity risk of unfettered AI usage is compounded by the increasing volume of AI vulnerabilities. Tenable Research has found and disclosed several vulnerabilities in AI solutions, including in Microsoft Copilot, Flowise, Langflow, among others.

With AI Aware, Tenable transforms proactive security for AI solutions. Tenable AI Aware uniquely leverages agents, passive network monitoring, dynamic application security testing and distributed scan engines to detect approved and unapproved AI software, libraries and browser plugins, along with associated vulnerabilities, thereby mitigating risks of exploitation, data leakage and unauthorized resource consumption. The combined depth of these multiple assessment methods delivers the most complete detection of AI in the modern ecosystem. 

[Watch the Tenable AI Aware product demo video here.]

“In an effort to keep pace with the sea change introduced by AI, organizations around the world ran full speed ahead, potentially bypassing countless cybersecurity, privacy and compliance red flags,” said Shai Morag, chief product officer, Tenable. “Perhaps more so than with any other new technology we’ve seen, there are many risk factors to consider, especially with rushed development and deployment. Tenable AI Aware empowers organizations to deploy AI confidently, ensuring their security measures keep pace with the rapid evolution of AI technologies.”

In addition to AI software and vulnerability detection, key AI Aware features available in Tenable Vulnerability Management, Tenable Security Center and Tenable One include:

• Dashboard Views provide a snapshot of the most common AI software discovered in the ecosystem, top assets with vulnerabilities related to AI and the most common communication ports leveraged by AI technologies. 
• Shadow Software Development Detection illuminates the unexpected existence of the building blocks of AI development in the environment, enabling businesses to align initiatives with organizational best practices.
• Filter Findings for AI Detections enable teams to focus on AI-related findings when reviewing vulnerability assessment results. Combined with the power of Tenable Vulnerability Prioritization Rating (VPR), teams can effectively assess and prioritize vulnerabilities introduced by AI packages and libraries. 
• Asset-Centric AI-Inventory provides a complete inventory of AI-related packages, libraries and browser plugins while reviewing the detailed profile of an asset. 

Join the upcoming Tenable webinar titled, "Mitigating AI-Related Security Risks: Insights and Strategies with Tenable AI Aware" on October 9, 2024 at 11:00 am ET, by registering here.

More information on Tenable AI Aware is available at: https://www.tenable.com/products/vulnerability-management/ai-aware 

About Tenable

Tenable® is the exposure management company, exposing and closing the cybersecurity gaps that erode business value, reputation and trust. The company’s AI-powered exposure management platform radically unifies security visibility, insight and action across the attack surface, equipping modern organizations to protect against attacks from IT infrastructure to cloud environments to critical infrastructure and everywhere in between. By protecting enterprises from security exposure, Tenable reduces business risk for more than 44,000 customers around the globe. Learn more at tenable.com. 

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Media Contact:

Tenable

tenablepr@tenable.com

Reporting and reproducibility: Proteomics of fish models in environmental toxicology and ecotoxicology  ScienceDirect.com

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models  Nature.com

Unico Inc. announced it is making highly efficient EC motors standard for all blower models within its modular air handling units.

In this episode we're talking to Dirk Riehle about open source business models. We started looking at the way OS projects work and defined different kinds of open source projects. In the main part of the discussion we looked at various ways of how to make money with open source: consulting, support contracts, commercial variant of an open source project, etc. We then looked at the chances and risks of each of these approaches. The next part focused on different open source licenses and how they are suitable for open source business. We concluded the episode by discussing a couple of specific questions and loose ends. After the show, Dirk informed me about the following three corrections: Black Duck Software's main product is called protexIP not IP Central, there are presently 70 licenses approved by the Open Source Initiative, and EnterpriseDB has so far acquired $37M in venture capital

William Falcon of Lighting AI discusses how to optimize deep learning models using the Lightning platform, optimization is a necessary step towards creating a production application. Philip Winston spoke with Falcon about PyTorch, PyTorch Lightning...

Phillip Carter, Principal Product Manager at Honeycomb and open source software developer, talks with host Giovanni Asproni about observability for large language models (LLMs). The episode explores similarities and differences for observability with LLMs versus more conventional systems. Key topics include: how observability helps in testing parts of LLMs that aren't amenable to automated unit or integration testing; using observability to develop and refine the functionality provided by the LLM (observability-driven development); using observability to debug LLMs; and the importance of incremental development and delivery for LLMs and how observability facilitates both. Phillip also offers suggestions on how to get started with implementing observability for LLMs, as well as an overview of some of the technology's current limitations. This episode is sponsored by WorkOS.

The Models —

Jim Grisanzio from Oracle Developer Relations talks with Gabriele Provinciali, an engineer and solutions architect at Oracle in Rome, about smart and safe city models built with Lego Bricks, Arduinos, Raspberry Pis, and a variety of IoT edge sensors all connected to the Oracle Cloud. Twitter Thread. Video on YouTube.

Oracle News Connect Article

• Little towns, big ideas: How scale models can imagine smarter and safer cities

Gabriele Provinciali, Solution Architect, Oracle Cloud Infrastructure

• https://www.linkedin.com/in/gabriele/
  
• https://twitter.com/GabbaPro

ProximaSafe: Joining the Dots in OCI to build a Stream Analysis Lab

• Part 1: Setting up the event-driven OCI components
• Part 2: From Edge to Serverless
• Part 3: Sensors, Pipelines and back to Edge
• YouTube Video: Oracle Proxima City

Jim Grisanzio, Oracle Developer Relations

• https://twitter.com/jimgris
• https://www.linkedin.com/in/jimgris/
• https://developer.oracle.com/team/
• https://oraclegroundbreakers.libsyn.com/