This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models. This data comprises the past five years and captures changes in n-gram frequency, similarity, sentiment and topic distribution. The interface built on top of this data enables temporal analysis for detecting and characterizing shifts in meaning, including complementary information to trending metrics, such as sentiment and topic association over time. We release an online demo for easy experimentation, and we share code and the underlying aggregated data for future work. In this paper, we also discuss three case studies unlocked thanks to our platform, showcasing its potential for temporal linguistic analysis.

We present a critical analysis of the simulation framework RICE-N, an integrated assessment model (IAM) for evaluating the impacts of climate change on the economy. We identify key issues with RICE-N, including action masking and irrelevant actions, and suggest improvements such as utilizing tariff revenue and penalizing overproduction. We also critically engage with features of IAMs in general, namely overly optimistic damage functions and unrealistic abatement cost functions. Our findings contribute to the ongoing efforts to further develop the RICE-N framework in an effort to improve the simulation, making it more useful as an inspiration for policymakers.

As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation. Our proposal seeks to address the challenges of carbon leakage through methods inspired by the Carbon Border Adjustment Mechanism (CBAM) and Climate Clubs (CC). We demonstrate the effectiveness of our approach by comparing simulated outcomes to representative concentration pathways (RCP) and shared socioeconomic pathways (SSP). Our protocol results in a temperature rise comparable to RCP 3.4/4.5 and SSP 2. Furthermore, we provide an analysis of our protocol's World Trade Organization compliance, administrative and political feasibility, and ethical concerns. We recognize that our proposal risks hurting the least developing countries, and we suggest specific corrective measures to avoid exacerbating existing inequalities, such as technology sharing and wealth redistribution. Future research should improve the RICE-N tariff mechanism and implement actions allowing for the aforementioned corrective measures.

We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space. Because representation learning is used, concepts can be identified even if they do not appear in the training data set. However, reference EEG data must exist for each such concept. We also show an application of the method to the problem of information retrieval. In neural information retrieval, EEG data is captured while a user recalls the contents of a document, and a list of links to predicted documents is produced.

Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, even fine-tuning the PLMs and doing inference are expensive, especially on edge devices with low computing power. Some general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fine-tuning, while very few one-shot compression techniques are explored. In this paper, we investigate the neural tangent kernel (NTK)--which reveals the gradient descent dynamics of neural networks--of the multilayer perceptrons (MLP) modules in a PLM and propose to coin a lightweight PLM through NTK-approximating MLP fusion. To achieve this, we reconsider the MLP as a bundle of sub-MLPs, and cluster them into a given number of centroids, which can then be restored as a compressed MLP and surprisingly shown to well approximate the NTK of the original PLM. Extensive experiments of PLM fine-tuning on both natural language understanding (NLU) and generation (NLG) tasks are provided to verify the effectiveness of the proposed method MLP fusion. Our code is available at https://github.com/weitianxin/MLP_Fusion.

In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter $\theta$, encoded into an $n$-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the system response $\mathcal{R}(\theta)$ (i.e., changes in the measurement outcomes). For simple cases the form of $\mathcal{R}(\theta)$ is known, but the same cannot be said for realistic scenarios, as no general closed-form expression exists. In this work we present an inference-based scheme for QS. We show that, for a general class of unitary families of encoding, $\mathcal{R}(\theta)$ can be fully characterized by only measuring the system response at $2n+1$ parameters. This allows us to infer the value of an unknown parameter given the measured response, as well as to determine the sensitivity of the scheme, which characterizes its overall performance. We show that inference error is, with high probability, smaller than $\delta$, if one measures the system response with a number of shots that scales only as $\Omega(\log^3(n)/\delta^2)$. Furthermore, the framework presented can be broadly applied as it remains valid for arbitrary probe states and measurement schemes, and, even holds in the presence of quantum noise. We also discuss how to extend our results beyond unitary families. Finally, to showcase our method we implement it for a QS task on real quantum hardware, and in numerical simulations.

The European Space Agency said Thursday that it is using artificial intelligence for satellite navigation. The engineering teams of the agency's NAVISP program are working with European industry and academics to "invent the future of navigation," it said, resulting in a growing portfolio of services to improve space and Earth weather forecasting, enhance autonomous car and boat performance and help to identify rogue drones in sensitive airspace. The program aims to improve "satnav" performance by combining Global Navigation Satellite Systems (GNSS) with AI. "AI comprises all techniques enabling computers to mimic intelligence, whether they be data analysis systems or the embedded intelligence overseeing an autonomous vehicle," Rafael Lucas Rodriguez, the head of the NAVISP Technical Programme Office, said in a statement. A picture taken on February 7, 2020, shows the logo of the European Space Agency (ESA) at its European Space Operations Centre (ESOC) in Darmstadt, western Germany. "What AI is very good at, through so-called Machine Learning, ML, is extracting meaningful information to identify useful patterns that would otherwise have gone unseen. Satellite navigation is among the fields yielding large amounts of data, so within our sector AI could also serve as the basis of novel approaches and services," he noted.

The row over a manipulated photo of Rishi Sunak pulling an imperfect pint is the latest example of doctored or deepfake images attempting to disrupt politics. Using false information or imagery to alter public opinion is not new but breakthroughs in artificial intelligence threaten to take deception to a new level. Here are some recent examples of image-based disinformation. Last year a video appeared of the Ukrainian president calling on his soldiers to lay down their weapons and return to their families. It was an amateurish example of a deepfake, the term for a hoax that uses AI to create a phoney image, most commonly fake videos of people.

Ukrainian President Volodymyr Zelenskyy warned this week that war is coming to Russia after kamikaze drone attacks targeted skyscrapers in Moscow's financial district, as his country's forces continued to score small-scale territorial successes against Russian troops in Ukraine's east and south. Here is a round-up of the main battlefield events during the 75th week of the war. On July 30, a suspected Ukrainian long-range drone hit a Moscow high-rise building that houses the Ministry of Digital Development, the Economy Ministry and the Ministry of Industrial Development, responsible for military industry. Two days later, another pair of drones was shot down outside Moscow, but a third made it through to the city where it was intercepted by electronic jammers and crashed into a skyscraper, damaging the facade. The attacks came just days after a previous drone raid on the centre of the Russian capital.

Experts have warned that action needs to be taken on the use of artificial intelligence-generated or enhanced images in politics after a Labour MP apologised for sharing a manipulated image of Rishi Sunak pouring a pint. Karl Turner, the MP for Hull East, shared an image on the rebranded Twitter platform, X, showing the prime minister pulling a sub-standard pint at the Great British beer festival while a woman looks on with a derisive expression. The image had been manipulated from an original photo in which Sunak appears to have pulled a pub-level pint while the person behind him has a neutral expression. The image brought criticism from the Conservatives, with the deputy prime minister, Oliver Dowden, calling it "unacceptable". "I think that the Labour leader should disown this and Labour MPs who have retweeted this or shared this should delete the image, it is clearly misleading," Dowden told LBC on Thursday.