Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In typical driving scenes, "things" categories can be dynamic and associated with higher collision risks, making them crucial for safe navigation and planning. Recognizing the importance of "things" categories, we identify their performance drop as a serious bottleneck in existing approaches. We observed that adverse weather induces degradation of semantic-level features and both corruption of local features, leading to a misprediction of "things" as "stuff". To mitigate these corruptions, we suggest our method, NTN - segmeNt Things for No-accident. To address semantic-level feature corruption, we bind each point feature to its superclass, preventing the misprediction of things classes into visually dissimilar categories. Additionally, to enhance robustness against local corruption caused by adverse weather, we define each LiDAR beam as a local region and propose a regularization term that aligns the clean data with its corrupted counterpart in feature space. NTN achieves state-of-the-art performance with a +2.6 mIoU gain on the SemanticKITTI-to-SemanticSTF benchmark and +7.9 mIoU on the SemanticPOSS-to-SemanticSTF benchmark. Notably, NTN achieves a +4.8 and +7.9 mIoU improvement on "things" classes, respectively, highlighting its effectiveness.

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the problem. However, the accuracy of the estimated performance metrics depends on the number of runs and should be studied using statistical tools. We present a statistical analysis of the common metrics, and develop guidelines for experiment design to measure the optimizer's performance using these metrics to a high level of confidence and accuracy. To this end, we first discuss the confidence interval of the metrics and how they are related to the number of runs of an experiment. We then derive a lower bound on the number of repeats in order to guarantee achieving a given accuracy in the metrics. Using this bound, we propose an algorithm to adaptively adjust the number of repeats needed to ensure the accuracy of the evaluated metric. Our simulation results demonstrate the utility of our analysis and how it allows us to conduct reliable benchmarking as well as hyperparameter tuning and prevent us from drawing premature conclusions regarding the performance of stochastic optimizers.

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and retrieved context pose challenges, particularly when retrieved information is unreliable or the model's internal knowledge is outdated. In such cases, LLMs struggle to determine whether to rely more on their own parameters or the conflicted context. To address this, we propose **CK-PLUG**, a plug-and-play method for controlling LLMs' reliance on parametric and contextual knowledge. We introduce a novel knowledge consistency metric, Confidence Gain, which detects knowledge conflicts by measuring entropy shifts in token probability distributions after context insertion. CK-PLUG then enables fine-grained control over knowledge preference by adjusting the probability distribution of tokens with negative confidence gain through a single tuning parameter. Experiments demonstrate CK-PLUG's ability to significantly regulate knowledge reliance in counterfactual RAG scenarios while maintaining generation fluency and knowledge accuracy. For instance, on Llama3-8B, memory recall (MR) of RAG response can be adjusted within a broad range (9.9%-71.9%), compared to the baseline of 42.1%. Moreover, CK-PLUG supports adaptive control based on the model's confidence in both internal and external knowledge, achieving consistent performance improvements across various general RAG tasks. Our code is available at: $\href{https://github.com/byronBBL/CK-PLUG}{\text{this https URL}}$.

Traffic monitoring is a cornerstone of effective network management and cybersecurity, providing Internet Service Providers (ISPs) with critical insights to detect anomalies, mitigate congestion, and maintain network performance [1]. The surge in video streaming, cloud computing, and online gaming is driving rapid growth in internet usage, contributing to increasingly complex and less predictable network traffic. Efficient network monitoring allows ISPs to maintain service quality, mitigate security risks, and optimize bandwidth in real time [2]. However, real-time monitoring alone is insufficient for proactively managing network resources. To anticipate variations in demand and prevent service disruptions, ISPs increasingly adopt advanced forecasting techniques to predict traffic patterns and optimize resource allocation in advance [3]. Accurate traffic forecasting allows ISPs to efficiently allocate resources, scale network capacity, and sustain service quality under fluctuating loads [3]. The rise of diverse, high-bandwidth services has significantly increased network traffic variability. Traditional models like ARIMA and exponential smoothing, which assume linearity, struggle with ISP data due to prevalent non-linear and high-frequency fluctuations, especially during peak traffic hours [4]. These limitations have driven the adoption of deep learning models, particularly neural networks, which excel at capturing complex temporal dependencies across various forecasting domains [5].

Federated Learning (FL) enables collaborative machine learning while preserving data privacy but struggles to balance privacy preservation (PP) and fairness. Techniques like Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) protect sensitive data but introduce trade-offs. DP enhances privacy but can disproportionately impact underrepresented groups, while HE and SMC mitigate fairness concerns at the cost of computational overhead. This work explores the privacy-fairness trade-offs in FL under IID (Independent and Identically Distributed) and non-IID data distributions, benchmarking q-FedAvg, q-MAML, and Ditto on diverse datasets. Our findings highlight context-dependent trade-offs and offer guidelines for designing FL systems that uphold responsible AI principles, ensuring fairness, privacy, and equitable real-world applications.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The FBI on Wednesday shared a wanted poster for Chinese national Baoxia "Emily" Liu, adding that the State Department is offering a reward of up to 15 million for information on her and others accused of smuggling U.S. drone weapons to Iran. Liu and three other fellow Chinese nationals were charged by President Joe Biden's Justice Department in January 2024 in an alleged years-long conspiracy in which they unlawfully exported and smuggled U.S. export-controlled items through China and Hong Kong to entities affiliated with Iran's Islamic Revolutionary Guard Corps (IRGC) and Ministry of Defense and Armed Forces Logistics (MODAFL), which supervises production of Tehran's missiles, weapons, and Unmanned Aerial Vehicles (UAVs). Her co-defendants are Li Yongxin, also known as "Emma Lee;" Yung Yiu Wa, also known as "Stephen Yung;" and Zhong Yanlai, also known as Sydney Chung.

Russia and Ukraine have exchanged 372 prisoners of war in a swap brokered by the United Arab Emirates, as US President Donald Trump said he held a "very good" phone call with Ukrainian President Volodymyr Zelenskyy. The Russian Defence Ministry announced on Wednesday that Moscow returned 175 soldiers and "22 seriously wounded prisoners of war in need of urgent medical assistance" in what it said was a "gesture of goodwill." It said that Kyiv returned 175 Russian troops. Zelenskyy confirmed the swap and wrote on X that it was "one of the largest" exchanges since the beginning of Russia's full-scale invasion of Ukraine in February 2022. "I thank our team for their important work in finding Ukrainian prisoners of war and facilitating exchanges, as well as for the results that bring hope. We are also grateful to all our partners, especially the United Arab Emirates, for making today's exchange possible," the Ukrainian leader added.

It turns out Tesla's camera-vision-only approach to self-driving is no match for a Wile E. Coyote-style fake wall. Earlier this week, former NASA engineer and YouTuber Mark Rober posted a video where he tried to see if he could trick a Tesla Model Y using its Autopilot driver-assist function into driving through a Styrofoam wall disguised to look like part of the road in front of it. The Tesla hurls towards the wall at 40 mph and, rather than stopping, plows straight through it, leaving a giant hole. "It turns out my Tesla is less Road Runner, more Wile E. Coyote," Rober says as he inspects the damage on the front hood. The video, posted only a couple days ago, had racked up over 20 million views by Wednesday morning.

After nine months stuck on the International Space Station (ISS), NASA's Butch Wilmore and Suni Williams finally made it back home last night. The duo splashed down off the coast of Florida aboard SpaceX's Crew Dragon capsule, having arrived at the ISS way back in June. While Wilmore and Williams will be relieved to be back on solid ground, their return has ignited a slew of conspiracy theories - with many sceptics critical of the splashdown in particular. Upon arrival, the capsule was circled by an inquisitive pod of dolphins, which many social media commentators are describing as'fake' and computer-generated. Others have taken it even further, suggesting the entire mission footage from departure to landing was created by a sophisticated AI tool.

Yurts founder and CEO Ben Van Roo breaks down concerns over DeepSeek on The Will Cain Show. Conservatives on social media and in the public square are increasingly speaking out about a bill being mulled in the Texas legislature they say threatens President Donald Trump's goal of ushering in a "golden age" of American energy and AI dominance. Some conservatives are rebelling over a proposal in the Texas Senate that will give the state broad authority to control new data centers in the state. SB6, or "the data center bill," caused an online ruckus recently, with conservative opposition to the bill gaining momentum, arguing that the bill, should it pass, will impose a major roadblock to the Trump administration's "Golden Age" of American energy production. Texas, long believed to be an ideal location for the AI fueling data centers, is a major location for President Trump's multibillion-dollar Stargate plan and is considered a finalist for numerous other multibillion-dollar projects and investments over the next decade.