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  • OpenAI launches GPT‑5.2 AI model with enhanced capabilities

    OpenAI launches GPT‑5.2 AI model with enhanced capabilities

    OpenAI has grown rapidly to more than 800 million people using its services weekly since its 2022 launch. PHOTO: REUTERS

    OpenAI on Thursday launched its GPT-5.2 artificial intelligence model, after CEO Sam Altman reportedly issued an internal “code red” in early December pausing non‑core projects and redirecting teams to accelerate development in response to Google’s Gemini 3.

    GPT-5.2 comes with improvements in general intelligence, coding and long-context understanding, the company said in a statement.

    The new model is expected to bring even more economic value for users, as it is better at creating spreadsheets, building presentations and handling complex multi-step projects, OpenAI said.

    Alphabet’s Google launched the latest version of its Gemini in November, highlighting Gemini 3’s lead position on several popular industry leaderboards that measure AI model performance.

    “Gemini 3 has had less of an impact on our metrics than we feared,” Altman said in an interview with CNBC on Thursday, alongside Disney’s CEO Bob Iger.

    Google did not immediately respond to a Reuters request for comment.

    Disney said on Thursday it is investing $1 billion in OpenAI and will let the startup use characters from Star Wars, Pixar and Marvel franchises in its Sora AI video generator.

    Microsoft-backed OpenAI said that it currently has no plans to drop GPT‑5.1, GPT‑5, or GPT‑4.1 from its application programming interface.

    GPT-5.2 Instant, Thinking, and Pro will begin rolling out in ChatGPT on Thursday, beginning with paid plans.

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  • Infant gut anellovirus composition associates with early life factors and childhood atopic disease

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  • How AI Prompting Is Shaping the Future of Work

    How AI Prompting Is Shaping the Future of Work

    Sandvik hosted its first ever Promptathon in December, with the goal of helping employees master the art of asking better questions.

    Unlike a traditional hackathon focused on writing code, the event centered on crafting prompts, questions and instructions given to AI systems to tackle real business challenges. “Initiatives like the Sandvik Promptathon show how we’re advancing our strategic objectives and shaping the industry’s future. I’m impressed by the achievements it delivered,” explained Stefan Widing, CEO and President of Sandvik.

    For two hours, teams across the world dove into Microsoft Copilot to tackle real business challenges—from boosting profitability and efficiency to reimagining how people work and collaborate. Ideas were judged on how well they support long-term strategy, the tangible value they create, their originality, and how easily they can be scaled across the organization.

    The four-ingredient formula for better prompting

    The event opened with a masterclass led by Usman Afzal, Solution Architect and Productivity Coach at Microsoft, who shared practical tips for getting more out of Copilot while building scenarios for the Promptathon.

    (Not every prompt needs all four ingredients, Afzal explained, but including the relevant ones dramatically improves the outcome and keeps AI grounded in real business needs.)

    Celebrating the winners

    The winners of the first Promptathon was a team from Brazil (including Flávio Lopes, Gabriel Pereira, Gabriela Sousa, Gustavo Cunha, Leonardo Ferreira, and Matheus Oliveira) (, with their concept “Sandvik Game Master,” in the category Empower High Performing Teams.

    Their idea reimagines onboarding and training as an engaging, gamified journey that fosters learning, psychological safety, and collaboration. Complex processes are transformed into accessible “worlds,” helping people build competence faster while reinforcing a safety-first culture.

    I would say that this is a bold, human-centered innovation that empowers teams and drives Sandvik forward

    “I would say that this is a bold, human-centered innovation that empowers teams and drives Sandvik forward,” said Stefan Widing as he congratulated the winners.

    As AI tools become standard workplace technology, the ability to communicate effectively with them becomes as fundamental as any other professional skill. By investing in prompt literacy, employees are equipped to maximize the potential of these tools and enhance productivity while maintaining the human judgment and expertise.

    The question is no longer whether AI will transform how we work, but how quickly we can learn to work alongside it effectively.

    Discover more stories like this:

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  • Pakistan grants preliminary approval to Binance and HTX crypto exchanges – Investing.com

    1. Pakistan grants preliminary approval to Binance and HTX crypto exchanges  Investing.com
    2. Binance and Pakistan Partner to Advance Digital-Asset Innovation and Regulatory Development  Binance
    3. Pakistan enters digital-finance, crypto era  The Express Tribune
    4. Pakistan to allow Binance to explore ‘tokenisation’ of up to $2bn of assets  Dawn
    5. Homecoming  The Nation (Pakistan )

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  • UBS shares hit 17-year high on hopes of watered down capital reforms

    UBS shares hit 17-year high on hopes of watered down capital reforms

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    UBS shares have climbed to their highest level in more than 17 years, buoyed by growing investor optimism that Swiss lawmakers will reach a compromise on proposals to impose tougher capital rules on the bank.

    The stock has been sensitive for months to debate over the Swiss government’s June 2025 banking reform package, which could require UBS to hold up to $26bn in extra capital.

    The bank has been particularly opposed to the proposal to force it to back its foreign subsidiaries with an extra $23bn in capital.

    Investor sentiment has been boosted by local press reports of a compromise being proposed by multiple political parties. The multi-party proposal suggests broader political momentum behind a more moderate overhaul of the capital regime.

    A group of senior legislators proposed softening the extra capital burden for UBS by allowing it to use additional tier one (AT1) debt — a cheaper form of capital — rather than equity to cover up to half of the capitalisation of its foreign subsidiaries.

    Politicians in the FDP party told Swiss newspaper Neue Zürcher Zeitung that they had “constructive conversations” with finance minister Karin Keller-Sutter, who is a member of the party.

    UBS shares climbed more than 4 per cent in morning trading in Zurich, pushing the stock to SFr35.17, its highest level since February 2008 before receding slightly.

    The Federal Council — the country’s government — is not expected to make a decision on the new capital rules until a formal consultation period on the reforms ends on January 9.

    Politicians and lobbyists, including representatives from the FDP and People’s party, have been discussing a compromise solution for months, the Financial Times reported in October.

    The latest proposal is that Switzerland should keep very strong capital rules for UBS but that these should be no harsher than necessary and should not be so strict that the bank becomes uncompetitive internationally, according to a copy of the plan seen by the FT.

    It recommends aligning several technical rules with those in the EU, UK and the US.

    The proposal also envisages UBS capping the size of its investment bank at 30 per cent of its risk-weighted assets. The investment bank already has a self-imposed limit of 25 per cent of UBS’s risk-weighted assets, and the Swiss lender has indicated that it would be willing to make a cap permanent.

    UBS said the new proposal “goes in a more constructive direction than the extreme approach proposed by the Federal Council”. It added: “However, Switzerland already has the most stringent capital requirement regime in the world.”

    The bank said it would “advocate for a strengthening of the regulatory framework with targeted, proportionate and internationally aligned measures”.

    However, some people close to UBS still believe the fresh proposals do not do enough to address the bank’s concerns.

    “The signals are more positive than they have been but this is still not going to solve the problem. The proposal still does not address the fact we will be less competitive,” said one person familiar with UBS’s thinking.

    The Swiss bank has held discussions about moving its headquarters to the US if the rules are not watered down, the FT reported last month.

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  • The Port of Rotterdam joins Global Maritime Forum as an Associate Partner

    The Port of Rotterdam joins Global Maritime Forum as an Associate Partner

    COPENHAGEN, 12 DECEMBER 2025 – The Global Maritime Forum is pleased to welcome The Port of Rotterdam as an Associate Partner. 

    The Port of Rotterdam Authority manages, operates, and develops the port and industrial area of Rotterdam, ensuring safe and smooth shipping operations while driving sustainable development and innovation. With approximately 1,400 employees and a turnover of €882 million, the Port Authority plays a central role in strengthening Rotterdam’s position as a leading European logistics hub and world-class industrial complex. 

    “Decarbonisation and sustainability are core priorities for the Global Maritime Forum, and collaboration with partners who combine operational expertise with a strong commitment to innovation and resilience is essential,” said Christian Jacob Hansen, Director of Partnerships and Community Engagement at the Global Maritime Forum. “The Port of Rotterdam brings immeasurable insight and experience to our initiatives, and we look forward to working together to accelerate industry-wide progress.” 

    Berte Simons, Chief Operating Officer of the Port of Rotterdam Authority, added: “We are proud to formalise our partnership with the Global Maritime Forum after years of productive collaboration. The Global Maritime Forum brings together an incredible network of expertise and strategic insight and combines this with a practical, action-oriented approach that accelerates progress. This makes them an invaluable partner on our shared journey toward maritime decarbonisation and industry action.” 

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  • Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models

    Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models

    Understanding the complex dynamics of the brain and their neurobiological underpinnings, with the potential to advance precision medicine (Falcon et al., 2016; Tan et al., 2016; Vogel et al., 2023; Williams and Whitfield Gabrieli, 2025), is a central goal in neuroscience. Modeling these dynamics provides crucial insights into causality and mechanisms underlying both normal brain function and various neurological disorders (Breakspear, 2017; Wang et al., 2023b; Ross and Bassett, 2024). By integrating the average activity of large populations of neurons (e.g. neural mass models; Wilson and Cowan, 1972; Jirsa and Haken, 1996; Deco et al., 2008; Jirsa et al., 2014; Montbrió et al., 2015; Cook et al., 2022) with information provided by structural imaging modalities (i.e. connectome; Honey et al., 2009; Sporns et al., 2005; Schirner et al., 2015; Bazinet et al., 2023), the whole-brain network modeling has proven to be a powerful tractable approach for simulating brain activities and emergent dynamics as recorded by functional imaging modalities (such as (s)EEG, MEG, and fMRI; Sanz-Leon et al., 2015; Schirner et al., 2022; Amunts et al., 2022; D’Angelo and Jirsa, 2022; Patow et al., 2024; Hashemi et al., 2025).

    The whole-brain models have been well-established in network neuroscience (Sporns, 2016; Bassett and Sporns, 2017) for understanding the brain structure and function (Ghosh et al., 2008; Honey et al., 2010; Park and Friston, 2013; Melozzi et al., 2019; Suárez et al., 2020; Feng et al., 2024; Tanner et al., 2024) and investigating the mechanisms underlying brain dynamics at rest (Deco et al., 2011; Wang et al., 2019; Ziaeemehr et al., 2020; Kong et al., 2021), normal aging (Lavanga et al., 2023; Zhang et al., 2024), and also altered states such as anesthesia and loss of consciousness (Barttfeld et al., 2015; Hashemi et al., 2017; Luppi et al., 2023; Perl et al., 2023b). This class of computational models, also known as virtual brain models (Jirsa et al., 2010; Sanz Leon et al., 2013; Sanz-Leon et al., 2015; Schirner et al., 2022; Jirsa et al., 2023; Wang et al., 2024), has shown remarkable capability in delineating the pathophysiological causes of a wide range of brain diseases, such as epilepsy (Jirsa et al., 2017; Proix et al., 2017; Wang et al., 2023b), multiple sclerosis (Wang et al., 2024; Mazzara et al., 2025), Alzheimer’s disease (Yalçınkaya et al., 2023; Perl et al., 2023a), Parkinson’s disease (Jung et al., 2022; Angiolelli et al., 2025), neuropsychiatric disorders (Deco and Kringelbach, 2014; Iravani et al., 2021), stroke (Allegra Mascaro et al., 2020; Idesis et al., 2022), and focal lesions (Rabuffo et al., 2025). In particular, they enable the personalized simulation of both normal and abnormal brain activities, along with their associated imaging recordings, thereby stratifying between healthy and diseased states (Liu et al., 2016; Patow et al., 2023; Perl et al., 2023a) and potentially informing targeted interventions and treatment strategies (Jirsa et al., 2017; Proix et al., 2018; Wang et al., 2023b; Jirsa et al., 2023; Hashemi et al., 2025). Although there are only a few tools available for forward simulations at the whole-brain level, for example the brain network simulator The Virtual Brain (TVB; Sanz Leon et al., 2013), there is a lack of tools for addressing the inverse problem, that is finding the set of control (generative) parameters that best explains the observed data. This study aims to bridge this gap by addressing the inverse problem in large-scale brain networks, a crucial step toward making these models operable for clinical applications.

    Accurately and reliably estimating the parameters of whole-brain models remains a formidable challenge, mainly due to the high dimensionality and nonlinearity inherent in brain activity data, as well as the non-trivial effects of noise and network inputs. A large number of previous studies in whole-brain modeling have relied on optimization techniques to identify a single optimal value from an objective function, scoring the model’s performance against observed data (Wang et al., 2019; Kong et al., 2021; Cabral et al., 2022; Liu et al., 2023). This approach often involves minimizing metrics such as the Kolmogorov-Smirnov distance or maximizing the Pearson correlation between observed and generated data features such as functional connectivity (FC), functional connectivity dynamics (FCD), and/or power spectral density (PSD). Although fast, such a parametric approach results in only point estimates and fails to capture the relationship between parameters and their associated uncertainty. This limits the generalizability of findings and hinders identifiability analysis, which explores the uniqueness of solutions. Furthermore, optimization algorithms can easily get stuck in local extrema, requiring multi-start strategies to address potential parameter degeneracies. These additional steps, while necessary, ultimately increase the computational cost. Critically, the estimation heavily depends on the form of the objective function defined for optimization (Svensson et al., 2012; Hashemi et al., 2018). These limitations can be overcome by employing Bayesian inference, which naturally quantifies the uncertainty in the estimation and statistical dependencies between parameters, leading to more robust and generalizable models. Bayesian inference is a principal method for updating prior beliefs with information provided by data through the likelihood function, resulting in a posterior probability distribution that encodes all the information necessary for inferences and predictions. This approach has proven essential for understanding the intricate relationships between brain structure and function (Hashemi et al., 2021; Lavanga et al., 2023; Rabuffo et al., 2025), as well as for revealing the pathophysiological causes underlying brain disorders (Hashemi et al., 2023; Yalçınkaya et al., 2023; Wang et al., 2024; Wang et al., 2024; Hashemi et al., 2025; Hashemi et al., 2024).

    In this context, simulation-based inference (SBI; Cranmer et al., 2020; Gonçalves et al., 2020; Hashemi et al., 2023; Hashemi et al., 2024) has gained prominence as an efficient methodology for conducting Bayesian inference in complex models where traditional inference techniques become inapplicable. SBI leverages computational simulations to generate synthetic data and employs advanced probabilistic machine learning methods to infer the joint distribution over parameters that best explain the observed data, along with associated uncertainty. This approach is particularly well-suited for Bayesian inference on whole-brain models, which often exhibit complex dynamics that are difficult to retrieve from neuroimaging data with conventional estimation techniques. Crucially, SBI circumvents the need for explicit likelihood evaluation and the Markovian (sequential) property required in sampling. Markov chain Monte Carlo (MCMC; Gelman et al., 1995) is the gold-standard nonparametric technique and asymptotically exact for sampling from a probability distribution. However, for Bayesian inference on whole-brain models given high-dimensional data, the likelihood function becomes intractable, rendering MCMC sampling computationally prohibitive. SBI offers significant advantages, such as parallel simulation while leveraging amortized learning, making it effective for personalized inference from large datasets (Hashemi et al., 2024). Amortization in artificial neural networks refers to the idea of reusing learned computations across multiple tasks or inputs (Gershman and Goodman, 2014). Amortization in Bayesian inference refers to the process of training a shared inference network (e.g. a neural network) with an intensive upfront computational cost, to perform fast inference across many different observations. Instead of re-running inference for each new observation, the trained model can rapidly return posterior estimates, significantly reducing computational cost at test time. Following an initial computational cost during simulation and training to learn all posterior distributions, subsequent evaluation of new hypotheses can be conducted efficiently, without additional computational overhead for further simulations (Hashemi et al., 2023). Importantly, SBI sidesteps the convergence issues caused by complex geometries that are often encountered when using gradient-based MCMC methods (Betancourt and Girolami, 2013; Betancourt et al., 2014; Hashemi et al., 2020). It also substantially outperforms approximate Bayesian computation (ABC) methods, which rely on a threshold to accept or reject samples (Sisson et al., 2007; Beaumont et al., 2009; Gonçalves et al., 2020). Such a likelihood-free approach provides us with generic inference on complex systems as long as we can provide three modules:

    1. A prior distribution, describing the possible range of parameters from which random samples can be easily drawn, that is θ→∼p(θ→).

    2. A simulator in computer code that takes parameters as input and generates data as output, that is x→∼p(x→∣θ→).

    3. A set of low-dimensional data features, which are informative of the parameters that we aim to infer.

    These elements prepare us with a training data set {(θ→i,x→i)}i=1Nsim with a budget of Nsim simulations. Then, using a class of deep neural density estimators, such as masked autoregressive flows (MAFs; Papamakarios and Pavlakou, 2017) or neural spline flows (NSFs; Durkan et al., 2019), we can approximate the posterior distribution of parameters given a set of observed data, that is p(θ→∣x→obs). Therefore, a versatile toolkit should be flexible and integrative, adeptly incorporating these modules to enable efficient Bayesian inference over complex models.

    To address the need for widely applicable, reliable, and efficient parameter estimation from different (source-localized) neuroimaging modalities, we introduce Virtual Brain Inference (VBI), a flexible and integrative toolkit for probabilistic inference at whole-brain level. This open-source toolkit offers fast simulation through just-in-time (JIT) compilation of various brain models in different programming languages (Python/C++) and devices (CPUs/GPUs). It supports space-efficient storage of simulated data (HDF5/NPZ/PT), provides a memory-efficient loader for batched data, and facilitates the extraction of low-dimensional data features (FC/FCD/PSD). Additionally, it enables the training of deep neural density estimators (MAFs/NSFs), making it a versatile tool for inference on neural sources corresponding to (s)EEG, MEG, and fMRI recordings. VBI leverages high-performance computing, significantly enhancing computational efficiency through parallel processing of large-scale datasets, which would be impractical with current alternative methods. Although SBI has been used for low-dimensional parameter spaces (Gonçalves et al., 2020; Wang et al., 2024; Baldy et al., 2024), we demonstrate that it can scale to whole-brain models with high-dimensional unknown parameters, as long as informative data features are provided. VBI is now accessible on the cloud platform EBRAINS (https://ebrains.eu), enabling users to explore more realistic brain dynamics underlying brain (dys)functioning using Bayesian inference.

    In the following sections, we will describe the architecture and workflow of the VBI toolkit and demonstrate the validation through a series of case studies using in silico data. We explore various whole-brain models corresponding to different types of brain recordings: a whole-brain network model of Wilson-Cowan (Wilson and Cowan, 1972), Jansen-Rit (Jansen and Rit, 1995; David and Friston, 2003), and Stuart-Landau (Selivanov et al., 2012) for simulating neural activity associated with EEG/MEG signals, the Epileptor (Jirsa et al., 2014) related to stereoelectro-EEG (sEEG) recordings, and Montbrió (Montbrió et al., 2015), and Wong-Wang (Wong and Wang, 2006; Deco et al., 2013) mapped to fMRI BOLD signals. Although these models represent source signals and could be applied to other modalities (e.g. Stuart-Landau representing generic oscillatory dynamics), we focused on their capabilities to perform optimally in specific contexts. For instance, some are better suited for encephalographic signals (e.g. EEG/MEG) due to their ability to preserve spectral properties, while others have been used for fMRI data, emphasizing their ability to capture dynamic features such as bistability and time-varying functional connectivity.

    VBI workflow

    Figure 1 illustrates an overview of our approach in VBI, which combines virtual brain models and SBI to make probabilistic predictions on brain dynamics from (sourc-localized) neuroimaging recordings. The inputs to the pipeline include the structural imaging data (for building the connectome), functional imaging data such as (s)EEG/MEG, and fMRI as the target for fitting, and prior information as a plausible range over control parameters for generating random simulations. The main computational costs involve model simulations and data feature extraction. The output of the pipeline is the joint posterior distribution of control parameters (such as excitability, synaptic weights, or effective external input) that best explains the observed data. Since the approach is amortized (i.e. it learns across all combinations in the parameter space), it can be readily applied to any new data from a specific subject.

    The workflow of Virtual Brain Inference (VBI).

    This probabilistic approach is designed to estimate the posterior distribution of control parameters in virtual brain models from whole-brain recordings. (A) The process begins with constructing a personalized connectome using diffusion tensor imaging and a brain parcellation atlas, such as Desikan-Killiany (Desikan et al., 2006), Automated Anatomical Labeling (Tzourio-Mazoyer et al., 2002), or VEP (Wang et al., 2021). (B) The personalized virtual brain model is then assembled. Neural mass models describing the averaged activity of neural populations, in the generic form of x˙→=f(x→,θ→,Iinput), are placed to each brain region and interconnected via the structural connectivity matrix. Initially, the control parameters are randomly drawn from a simple prior distribution. (C) Next, the VBI operates as a simulator that uses these samples to generate time series data associated with neuroimaging recordings. (D) We extract a set of summary statistics from the low-dimensional features of the simulations (FC, FCD, PSD) for training. (E) Subsequently, a class of deep neural density estimators is trained on pairs of random parameters and their corresponding data features to learn the joint posterior distribution of the model parameters. (F) Finally, the amortized network allows us to quickly approximate the posterior distribution for new (empirical) data features, enabling us to make probabilistic predictions that are consistent with the observed data.

    In the first step, non-invasive brain imaging data, such as T1-weighted MRI and Diffusion-weighted MRI (DW-MRI), are collected for a specific subject (Figure 1A). T1-weighted MRI images are processed to obtain brain parcellation, while DW-MRI images are used for tractography. Using the estimated fiber tracts and the defined brain regions from the parcellation, the connectome (i.e. the complete set of links between brain regions) is constructed by counting the fibers connecting all regions. The SC matrix, with entries representing the connection strength between brain regions, forms the structural component of the virtual brain which constrains the generation of brain dynamics and functional data at arbitrary brain locations (e.g. cortical and subcortical structures).

    Subsequently, each brain network node is equipped with a computational model of average neuronal activity, known as neural mass models (see Figure 1B and Materials and methods). They can be represented in the generic form of a dynamical model as x˙→=f(x→,θ→,Iinput), with the system variables x→ (such as membrane potential and firing rate), the control parameters θ→ (such as excitability), and the input current Iinput (such as stimulation). This integration of mathematical mean-field modeling (neural mass models) with anatomical information (connectome) allows us to efficiently analyze functional neuroimaging modalities at the whole-brain level.

    To quantify the posterior distribution of control parameters given a set of observations, p(θ→∣x→), we first need to define a plausible range for the control parameters based on background knowledge p(θ→), that is a simple base distribution known as a prior. We draw random samples from the prior and provide them as input to the VBI simulator (implemented by Simulation module) to generate simulated time series associated with neuroimaging recordings, as shown in Figure 1C. Subsequently, we extract low-dimensional data features (implemented by Features module), as shown in Figure 1D for FC/FCD/PSD, to prepare the training dataset {(θ→i,x→i)}i=1Nsim , with a budget of Nsim simulations. Then, we use a class of deep neural density estimators, such as MAF or NSF models, as schematically shown in Figure 1E, to learn all the posterior p(θ→∣x→). Finally, we can readily sample from p(θ→∣xobs→), which determines the probability distribution in parameter space that best explains the observed data.

    Figure 2 depicts the structure of the VBI toolkit, which consists of three main modules. The first module, referred to as the Simulation module, is designed for fast simulation of whole-brain models, such as Wilson-Cowan (Wilson-Cowan model), Jansen-Rit (Jansen-Rit model), Stuart-Landau (Stuart-Landau oscillator), Epileptor (Epileptor model), Montbrió (Montbrió model), and Wong-Wang (Wong-Wang model). These whole-brain models are implemented across various numerical computing libraries such as Cupy (GPU-accelerated computing with Python), C++ (a high-performance systems programming language), Numba (a JIT compiler for accelerating Python code), and PyTorch (an open-source machine learning library for creating deep neural networks).


    Flowchart of the VBI Structure.

    This toolkit consists of three main modules: (1) The Simulation module, implementing various whole-brain models, such as Wilson-Cowan (WCo), Jansen-Rit (JR), Stuart-Landau (SL), Epileptor (EPi), Montbrió (MPR), and Wong-Wang (WW), across different numerical computing libraries (C++, Cupy, PyTorch, Numba). (2) The Features module, offering an extensive toolbox for extracting low-dimensional data features, such as spectral, temporal, connectivity, statistical, and information theory features. (3) The Inference module, providing neural density estimators (such as MAF and NSF) to approximate the posterior of parameters.

    The second module, Features, provides a versatile tool for extracting low-dimensional features from simulated time series (see Comprehensive feature extraction). The features include, but are not limited to, spectral, temporal, connectivity, statistical, and information theory related features, and the associated summary statistics. The third module focuses on Inference, that is training the deep neural density estimators, such as MAF and NSF (see Simulation-based inference), to learn the joint posterior distribution of control parameters. See Figure 2—figure supplement 1 and Figure 2—figure supplement 2 for benchmarks comparing CPU/GPU and MAF/NSF performances, and Figure 2—figure supplement 3 for the estimation of the global coupling parameter across different whole-brain network models, evaluated under multiple configurations.

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  • HENSOLDT supplies radars for Rheinmetall air defence systems

    HENSOLDT supplies radars for Rheinmetall air defence systems

    With the framework agreement now in place, both companies have laid the foundation for predictable, efficient and reliable cooperation. The agreement offers both sides a high degree of planning security and binding purchase conditions and ensures that core components of the SPEXER 2000 product range are available as needed and in resilient supply chains. Various companies within the Rheinmetall Group can place orders under the framework agreement.
    The SPEXER radar family offers high-performance surveillance radars for various ranges for the automatic detection and classification of ground, sea and low-flying air targets. The SPEXER 2000 is used by the German Armed Forces in field camp protection (ASUL), the high-energy laser for drone defence (HoWiSM) and the air defence system for close and next-range protection (NNbS), among other things.

    Together, Rheinmetall and HENSOLDT are making an important contribution to strengthening the air defence capabilities of the German Armed Forces and allied nations. The cooperation underscores the shared goal of strengthening the European defence industry, keeping critical technologies available and providing long-term support for the operational readiness of modern air defence systems.

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  • Lululemon boss to step down early next year

    Lululemon boss to step down early next year

    The boss of Lululemon Athletica, the brand known for its expensive yoga leggings and other sports clothing, is to leave the company early next year.

    Calvin McDonald, the firm’s chief executive, will depart at the end of January after more than seven years at the helm.

    The decision comes amid a run of poor sales for Lululemon in the US, its main market, in recent times and its share price falling almost 50% in the past year.

    However, this week the company upgraded its annual revenue forecast after better-than-expected sales in the past few months.

    Mr McDonald said the decision to leave the company was taken after discussions with the board.

    “As we near the end of our five-year strategy, and with our strong senior leadership team in place, we all agree that now is the time for a change,” he said in a LinkedIn post.

    While the Canadian company’s latest results revealed a boost to its sales internationally driven by its business in China, its performance in the Americas has been going in the opposite direction.

    The brand’s share price on the US Nasdaq index peaked in late 2023 and has been on a downward trend since. In September, its shares fell sharply after it warned of the impact tariffs imposed by US President Donald Trump would have on its business.

    Lululemon’s particular concern was over the ending of the so-called de-minimis exemption, a former duty-free loophole for low-cost goods entering America from countries such as China.

    Many of the Canadian company’s suppliers are based in China, Vietnam and other Asian countries. In September, it estimated the newly-imposed import taxes would cost it about $240m (£178.4m) this year.

    However, sales in China and around the rest of the world have been positive, driving its net revenues to the start of November to $2.6bn.

    “As we enter the holiday season, we are encouraged by our early performance,” said Mr McDonald. However, he said that despite good Thanksgiving period, demand had slowed since as consumers continued to look for cheaper products.

    Lululemon has faced increasing competition from lower-priced rivals such as Vuori and Alo Yoga for its products.

    Dan Coatsworth, head of markets at AJ Bell, told the BBC that competition had been “fierce”, and that the brand needed to “go back to the drawing board and work out ways to make its products ‘must-have’ items again”.

    Mr Coastworth also said that under Mr McDonald the brand had gone through the “embarrassment of having to pull its Breezethrough product line after negative reviews and customer complaints about the leggings being uncomfortable to wear” last year.

    The company halted sales of its then newly-launched $98 leggings last summer after shoppers criticized the V-shaped back seam of the tights as “unflattering” and others said the seam at the top of the waistband dug into their waists.

    Lululemon was also mocked on social media in 2020 for promoting an event about how to “resist capitalism”.

    Lululemon named its finance boss Meghan Frank and chief commercial officer André Maestrini as co-interim chief executives while it searches for a new leader.

    Marti Morfitt, chair of the brand’s board, thanked Mr McDonald for “his visionary leadership building Lululemon into one of the strongest brands in retail”.

    “During his tenure, Calvin led Lululemon through a period of impressive revenue growth, with differentiated products and experiences that resonated with guests around the world.”

    Mr Coatsworth likened Mr McDonalds tenure as “akin to the highs and lows of an athlete hitting their peak and then rapidly going off the boil”.

    “He steered Lululemon to greatness as the athleisure trend boomed, with people happy to pay top dollar for posh leggings.

    “Then came a series of mistakes which were compounded by factors outside of the company’s control. It’s no wonder Lululemon is looking to bring in fresh leadership,” he added.

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  • Advocate of the Global South, global provider of green tech: China has come to dominate the climate discourse

    Advocate of the Global South, global provider of green tech: China has come to dominate the climate discourse

    The COP30 climate conference ended on November 21st without much success. The hoped-for roadmap for phasing out climate-damaging energy sources such as coal, oil, and gas failed due to fierce resistance from some countries. But the conference made clear: China is dominating the climate discourse now, says Johanna Krebs. 

    “China keeps its promises and delivers on its commitments.” This is how China’s leading negotiator on international climate issues, Vice Premier Ding Xuexiang, described his country’s approach at the COP30 conference in November. And China’s contribution to the conference was indeed more visible than that of European countries, let alone of the United States. 

    China sent the second largest delegation to COP after host country Brazil, signaling that it considers these meetings important. In the runup to the conference, Ding had stated China’s priorities: further drive the green and low-carbon transformation, uphold the principle of “common but differentiated responsibilities” and remove trade barriers that are an obstacle to the development of green products. China also openly pushed to put “unilateral trade measures” – like the EU’s Carbon Border Adjustment Mechanism (CBAM) and EV tariffs – on the agenda of COP. It didn’t succeed, but the move showed that climate and trade interests are increasingly intertwined, pitting China against the EU. 

    At the conference, it became clear that China dominates the climate discourse now. Its envoys engaged in the discussions around green development in the Global South, with an event on the topic in the China pavilion reportedly being “packed”. China offers real solutions for climate mitigation like, for instance, the export of cheap green tech, wind turbines and solar panels. By doing so, it shifts the international climate discourse to an international green tech discourse – an area where China and the EU are competing. 

    China’s image as a climate actor benefits from the US’s withdrawal and Europe’s dividedness 

    Germany and the EU used to be pioneers in climate action, but this image has suffered greatly, also because the EU published weakened NDC (nationally determined contributions) due to disagreement among its member states. Germany has a legacy as a climate leader, however, at this year’s COP it did not stand out as such, also because the Merz government has ranked the topic lower on its priority list. 

    China, on the other hand, stresses that it will stay the course: In autumn, Xi Jinping stated that “green and low-carbon transition is the trend of the time. While some country is acting against it, the international community should stay focused on the right direction.” The United States’ (second) withdrawal from the Paris agreement and other formats enables China to present itself as an alternative model for tackling the climate crisis. 

    Beijing also assertively criticizes the EU, with climate envoy Liu Zhenmin calling the Union’s pollution-cutting targets insufficient and a Chinese diplomat allegedly calling the EU’s backtracking on its climate targets right before COP30 “shameful”. Vice Premier Ding stressed that China still expects developed countries to “implement their obligations to take lead in reducing emissions” and “fulfill their investment commitments”. By positioning itself as a defender of Global South interests and criticizing developed countries for abandoning the developing world, China’s government currently also occupies much of the international climate negotiation space and avoids having to justify its own, widely seen as less ambitious climate commitments, untransparent climate finance, or the installation of new coal fired plants. 

    Bridging the gap between industrial upgrading and climate policy in the next Five-Year Plan

    How is China making progress on its domestic climate goals? The proposal for the 15th Five-Year Plan (FYP), that will be passed next spring, shows that Beijing views decarbonization and industrial development as two sides of the same coin. It offers a glance into how China imagines its industrial future: being a climate role model which fuses industry and climate policy to the benefit of all. In the coming five years, China plans to double down on domestic industrial modernization and technological progress. As the document states, “green development is a defining feature of Chinese modernization” and part of “high quality development”. 

    For the first time, an FYP proposal mentions the “safe, reliable, and orderly replacement of fossil fuels”, describing top level momentum to accelerate the structural transition to non-fossil fuel energy. Moreover, it stresses the need to control both the total amount and intensity of emissions, a levelling up compared to the 14th FYP which strongly focused on emission intensity. The proposal also identifies the installation of energy storage technologies, and construction of smart-grids and microgrids as crucial. 

    However, at the same time, it advocates for the “clean and efficient use of fossil fuels” and the upgrading of coal-fired power plants. Fossil fuelled energy production will continue to play a considerable role in China in the upcoming years.  

    China’s climate risks: China is highly vulnerable to climate change and due to its large territory will experience various climate related risk profiles. Historically, China is prone to experiencing heat waves, storms, floods and droughts, all of which will be more extreme as climate change progresses. Moreover, China is already witnessing alarming glacier retreats. Lastly, given that a large part of the population lives in China’s coastal provinces, China will also be greatly impacted by sea level rise, with sea levels projected to rise 40-60 centimeters until the end of the current century, potentially receding China’s coastline more than 10 meters in some parts of China. 

    China’s own climate targets remain “highly insufficient”

    China’s officially announced policies and climate targets, including the 2035 NDC remain “highly insufficient” and, according to estimates, will lead to a global warming above 4°C. Its five-year carbon intensity target, as defined in the 14th FYP, would have required a total emissions reduction of four percent in 2025 – China is set to underdeliver here. More stringent policies will be required to reach the energy intensity targets for 2030. 

    Observers fear that carbon-reduction policies might be counter-played by the renewed uptake in permissions for coal-plans, which reached a high in 2023. Although the number of plants permitted has noticeably decreased since then, the fact that some are still in construction and have yet to come online means that China is still adding coal capacity to its power grid. 

    However, as data from the last five years shows, adding coal capacity to the grid does not predict a proportional increase in coal-power output;  despite building new coal power plants, China does not seem to increase its reliance on coal, leading to a falling utilization rate of coal plants. More relevant to the future trajectory of Chinese carbon emissions are China’s industries, especially the chemical industry which have seen a rising demand for plastic production. The trajectory strongly depends on how fast China can decouple its industries from CO2 emissions. 

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