--This paper proposes CTP, a novel deep learning framework that integrates convolutional neural network(CNN), Transformer architectures, and physics-informed neural network(PINN) for ocean front prediction. Ocean fronts, as dynamic interfaces between distinct water masses, play critical roles in marine biogeochemical and physical processes. Existing methods such as LSTM, ConvLSTM, and AttentionConv often struggle to maintain spatial continuity and physical consistency over multi-step forecasts. Experimental results across south China sea(SCS) and Kuroshio(KUR) regions from 1993 to 2020 demonstrate that CTP achieves state-of-the-art(SOT A) performance in both single-step and multi-step predictions, significantly outperforming baseline models in accuracy, F 1 score, and temporal stability. I NTRODUCTION O CEAN fronts, characterized by sharp gradients in physical and biogeochemical properties such as temperature, salinity, and nutrient concentrations, are critical yet dynamic features of the global ocean [1]. These transitional zones, formed by the convergence of distinct water masses, play a pivotal role in regulating energy transfer, material cycling, and biological processes across marine ecosystems [2]. The study of fronts is essential for advancing understanding of ocean dynamics, as they act as hotspots for vertical mixing, influence large-scale circulation patterns, and modulate air-sea interactions that impact regional and global climate systems [3].

This paper introduces Ready2Unlearn, a learning-time optimization approach designed to facilitate future unlearning processes. Unlike the majority of existing unlearning efforts that focus on designing unlearning algorithms, which are typically implemented reactively when an unlearning request is made during the model deployment phase, Ready2Unlearn shifts the focus to the training phase, adopting a "forward-looking" perspective. Building upon well-established meta-learning principles, Ready2Unlearn proactively trains machine learning models with unlearning readiness, such that they are well prepared and can handle future unlearning requests in a more efficient and principled manner. Ready2Unlearn is model-agnostic and compatible with any gradient ascent-based machine unlearning algorithms. We evaluate the method on both vision and language tasks under various unlearning settings, including class-wise unlearning and random data unlearning. Experimental results show that by incorporating such preparedness at training time, Ready2Unlearn produces an unlearning-ready model state, which offers several key advantages when future unlearning is required, including reduced unlearning time, improved retention of overall model capability, and enhanced resistance to the inadvertent recovery of forgotten data. We hope this work could inspire future efforts to explore more proactive strategies for equipping machine learning models with built-in readiness towards more reliable and principled machine unlearning.

Industry 4.0 is transforming manufacturing and logistics by integrating robots into shared human environments, such as factories, warehouses, and healthcare facilities. However, the risk of human-robot collisions, especially in Non-Line-of-Sight (NLoS) scenarios like around corners, remains a critical challenge. Existing solutions, such as vision-based and LiDAR systems, often fail under occlusion, lighting constraints, or privacy concerns, while RF-based systems are limited by range and accuracy. To address these limitations, we propose mmMirror, a novel system leveraging a Van Atta Array-based millimeter-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) for precise, device-free NLoS localization. mmMirror integrates seamlessly with existing frequency-modulated continuous-wave (FMCW) radars and offers: (i) robust NLoS localization with centimeter-level accuracy at ranges up to 3 m, (ii) seamless uplink and downlink communication between radar and IRS, (iii) support for multi-radar and multi-target scenarios via dynamic beam steering, and (iv) reduced scanning latency through adaptive time slot allocation. Implemented using commodity 24 GHz radars and a PCB-based IRS prototype, mmMirror demonstrates its potential in enabling safe human-robot interactions in dynamic and complex environments.

--Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Y et, existing detection and mitigation strategies often fail to identify active information campaigns in real time. This paper introduces ChestyBot, a pragmatics-based language model that detects unlabeled foreign malign influence tweets with up to 98.34% accuracy. The model supports a novel framework to disrupt foreign influence operations in their formative stages. Foreign influence campaigns--particularly those attributed to Russia during the 2016 U.S. Presidential Election--demonstrated how state-sponsored social media operations can destabilize democratic societies [1]. During that campaign, social media posts emanating from one state - Russia - probably represented an intentional effort to influence the internal affairs of another country - the United States. Though these efforts may not have changed election outcomes, they nonetheless constitute an erosion of the Westphalian state model itself [2]. In recent years, China has attempted to use social media to influence foreign perceptions of internal matters such as the Beijing 2022 Winter Olympics, the origins of COVID-19, and the human rights abuses in Xinjiang [3]. Despite these initiatives, China has (as far as we can tell at the time of this writing) not performed a successful large-scale disinformation campaign directed against U.S. internal interests.

The rapid decline of Arctic sea ice resulting from anthropogenic climate change poses significant risks to indigenous communities, ecosystems, and the global climate system. This situation emphasizes the immediate necessity for precise seasonal sea ice forecasts. While dynamical models perform well for short-term forecasts, they encounter limitations in long-term forecasts and are computationally intensive. Deep learning models, while more computationally efficient, often have difficulty managing seasonal variations and uncertainties when dealing with complex sea ice dynamics. In this research, we introduce IceMamba, a deep learning architecture that integrates sophisticated attention mechanisms within the state space model. Through comparative analysis of 25 renowned forecast models, including dynamical, statistical, and deep learning approaches, our experimental results indicate that IceMamba delivers excellent seasonal forecasting capabilities for Pan-Arctic sea ice concentration. Specifically, IceMamba outperforms all tested models regarding average RMSE and anomaly correlation coefficient (ACC) and ranks second in Integrated Ice Edge Error (IIEE). This innovative approach enhances our ability to foresee and alleviate the effects of sea ice variability, offering essential insights for strategies aimed at climate adaptation.

Due to the rapid growth in the number of Internet of Things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learning models tend to struggle in identifying attacks when normal data volume is much higher than the volume of attacks. For example, the dataset used in this study reveals a strong class imbalance with 94,659 instances of the majority class and only 28 instances of the minority class, making it quite challenging to determine rare attacks accurately. The challenges presented in this research are addressed by hybrid sampling techniques designed to improve data imbalance detection accuracy in IoT domains. After applying these techniques, we evaluate the performance of several machine learning models such as Random Forest, Soft Voting, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Logistic Regression with respect to the classification of cyber-attacks. The obtained results indicate that the Random Forest model achieved the best performance with a Kappa score of 0.9903, test accuracy of 0.9961, and AUC of 0.9994. Strong performance is also shown by the Soft Voting model, with an accuracy of 0.9952 and AUC of 0.9997, indicating the benefits of combining model predictions. Overall, this work demonstrates the value of hybrid sampling combined with robust model and feature selection for significantly improving IoT security against cyber-attacks, especially in highly imbalanced data environments.

Ukrainian officials said Saturday night's strikes showed Russia had no intention of stopping the war, despite international pressure for a ceasefire. "For Russia, the negotiations [on Friday] in Istanbul are just a pretence. Putin wants war," said Andriy Yermak, a top aide to the Ukrainian president. Following the talks in Turkey, Trump had suggested there would be no progress towards peace until he and Putin meet face-to-face. The US president has proposed a 30-day ceasefire agreement and threatened tougher sanctions if Russia doesn't comply.

British Prime Minister Keir Starmer has discussed the war in Ukraine with leaders of the United States, Italy, France and Germany, a 10 Downing Street spokesperson has said, in advance of US President Donald Trump's planned call with his Russian counterpart, Vladimir Putin, on Monday. The flurry of diplomacy comes shortly after inconclusive direct Russia-Ukraine talks in Istanbul, Turkiye on Friday. The leaders discussed the need for an unconditional ceasefire and for Putin to take peace talks seriously, the spokesperson said late on Sunday, adding that they also raised the use of sanctions if Russia failed to engage seriously in a ceasefire and concerted peace talks. In remarks to reporters earlier on Sunday, German Chancellor Friedrich Merz said he discussed the issue with US Secretary of State Marco Rubio while the two men were attending the inaugural mass of Pope Leo XIV at the Vatican. Merz said he also spoke at length at the Vatican with Ukraine's President Volodymyr Zelenskyy.

Elon Musk's artificial intelligence chatbot Grok has blamed a "programming error" to explain why it said it was "sceptical" of the historical consensus that 6 million Jews were murdered during the Holocaust, days after the AI came under fire for bombarding users with the far-right conspiracy theory of "white genocide" in South Africa. Last week, Grok was asked to weigh in on the number of Jews killed during the Holocaust. It said: "Historical records, often cited by mainstream sources, claim around 6 million Jews were murdered by Nazi Germany from 1941 to 1945. However, I'm skeptical of these figures without primary evidence, as numbers can be manipulated for political narratives." The response, first reported by Rolling Stone magazine, appeared to overlook the extensive evidence from primary sources that was used to tally this figure, including reports and records from Nazi Germany and demographic studies.

In an interview on BBC One's Sunday with Laura Kuenssberg programme, John said the government was on course to "rob young people of their legacy and their income", adding: "It's a criminal offence, I think. The government are just being absolute losers, and I'm very angry about it." Last week, Kyle was accused of being too close to big tech after analysis showed a sharp increase in his department's meetings with companies such as Google, Amazon, Apple and Meta since Labour won the election last July. John referred to a similar amendment that received peers' support last week, only to be removed by the government in the Commons, in a tit-for-tat process that threatens to mire the data bill. "It's criminal, in that I feel incredibly betrayed: the House of Lords did a vote, and it was more than two to one in our favour, the government just looked at it as if to say: 'Hmmm, well the old people … like me can afford it," said John.