Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce PACE, which is a dataset that includes high-distress episodes constructed from first-person narratives, and structured around the principles of Psychological First Aid (PFA). Using this data, we train PACER, a counseling model designed to provide both empathetic and directive support, which is optimized through supervised learning and simulated preference alignment. To assess its effectiveness, we propose PanicEval, a multi-dimensional framework covering general counseling quality and crisis-specific strategies. Experimental results show that PACER outperforms strong baselines in both counselor-side metrics and client affect improvement. Human evaluations further confirm its practical value, with PACER consistently preferred over general, CBT-based, and GPT-4-powered models in panic scenarios (Code is available at https://github.com/JihyunLee1/PanicToCalm ).

Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error planning. Purpose: Develop a stochastic policy-based DRL agent for automatic treatment planning with efficient training, broad applicability, and robustness against adversarial attacks using Fast Gradient Sign Method (FGSM). Methods: Using the Actor-Critic with Experience Replay (ACER) architecture, the agent tunes treatment planning parameters (TPPs) in inverse planning. Training is based on prostate cancer IMRT cases, using dose-volume histograms (DVHs) as input. The model is trained on a single patient case, validated on two independent cases, and tested on 300+ plans across three datasets. Plan quality is assessed using ProKnow scores, and robustness is tested against adversarial attacks. Results: Despite training on a single case, the model generalizes well. Before ACER-based planning, the mean plan score was 6.20$\pm$1.84; after, 93.09% of cases achieved a perfect score of 9, with a mean of 8.93$\pm$0.27. The agent effectively prioritizes optimal TPP tuning and remains robust against adversarial attacks. Conclusions: The ACER-based DRL agent enables efficient, high-quality treatment planning in prostate cancer IMRT, demonstrating strong generalizability and robustness.

Today at CES 2025, Acer announced their newest Nitro V AI lineup of gaming laptops infused with artificial intelligence. Not only are they designed to consume less power (and extend battery life), but they also feature AI-powered graphics thanks to Nvidia's DLSS 3.5 technology. AI really is an all-consuming thing, huh? Let's jump into the details. The Acer Nitro V AI comes in three different sizes: 15 inches, 16 inches, and 17 inches.

Believe it or not, AI is already subtly reshaping the PC. No, we're not talking about the microprocessor or integrated NPUs. There, progress has been slow and stuttering, as chip vendors and Microsoft work toward establishing an ecosystem of Copilot PCs. Instead, PC vendors are looking for ways to reinvent the familiar with AI capabilities. But we wanted to know what PC vendors thought about the future of the AI PCs they're building.

Believe it or not, AI is already subtly reshaping the PC. No, we're not talking about the microprocessor or integrated NPUs. There, progress has been slow and stuttering, as chip vendors and Microsoft work toward establishing an ecosystem of Copilot PCs. Instead, PC vendors are looking for ways to reinvent the familiar with AI capabilities. But we wanted to know what PC vendors thought about the future of the AI PCs they're building.

Deep Reinforcement Learning (DRL) is a powerful tool used for addressing complex challenges in mobile networks. This paper investigates the application of two DRL models, on-policy and off-policy, in the field of resource allocation for Open Radio Access Networks (O-RAN). The on-policy model is the Proximal Policy Optimization (PPO), and the off-policy model is the Sample Efficient Actor-Critic with Experience Replay (ACER), which focuses on resolving the challenges of resource allocation associated with a Quality of Service (QoS) application that has strict requirements. Motivated by the original work of Nessrine Hammami and Kim Khoa Nguyen, this study is a replication to validate and prove the findings. Both PPO and ACER are used within the same experimental setup to assess their performance in a scenario of latency-sensitive and latency-tolerant users and compare them. The aim is to verify the efficacy of on-policy and off-policy DRL models in the context of O-RAN resource allocation. Results from this replication contribute to the ongoing scientific research and offer insights into the reproducibility and generalizability of the original research. This analysis reaffirms that both on-policy and off-policy DRL models have better performance than greedy algorithms in O-RAN settings. In addition, it confirms the original observations that the on-policy model (PPO) gives a favorable balance between energy consumption and user latency, while the off-policy model (ACER) shows a faster convergence. These findings give good insights to optimize resource allocation strategies in O-RANs. Index Terms: 5G, O-RAN, resource allocation, ML, DRL, PPO, ACER.

Berlin's annual IFA electronics show isn't typically a hotspot of PC news, since it follows hot on the heels of the computer-centric Computex in June. Intel used IFA to launch its hotly anticipated "Lunar Lake" Core Series Ultra 2 processors, packing a radical new architecture and seriously improved graphics chops. Not to be outgunned, Qualcomm revealed new 8-core variants of its Snapdragon X PC chips, aiming to bring multi-day battery life to sub- 900 laptops. With so many notebook reveals crammed into such a short time span, however, the deluge felt a bit same-y at times – but not with the delightfully unorthodox systems below, which lean on surprisingly nifty gimmicks to stand out from the crowd. Without further ado, let's get weird.

Google just announced a slew of new features coming to ChromeOS, many of them coming to the more premium Chromebook Plus models that were announced last fall. But today's news isn't just about the software -- Google's hardware partners have a bunch of new laptops ready to take advantage of these features. Acer has two updates to existing models, the Chromebook Plus Spin 714 and Chromebook Plus 516 GE. These were already two of my favorite Chromebooks, and they've now been updated with new Intel chips. The Spin 714 starts with an Intel Core Ultra 5 115U processor, while the 516 GE has the Core 5 120U processor.

The Acer TravelMate P6 offers excellent value for a business laptop, with long battery life, a surprisingly light weight, and more ports than a typical consumer laptop. The Acer TravelMate P6 is a business laptop through and through. It's packed with ports, delivers long battery life, is surprisingly lightweight, and has a nice matte screen designed to avoid glare in normally uncomfortable lighting conditions. It's a nice and supremely practical piece of hardware, and I'd be happy to get a machine like this from my job. Starting at a retail price of 1,329, it's a bargain as far as business laptops go, especially if a workplace is getting a discount for buying a bunch at once! But if you're just looking to buy a single laptop for your own personal use, a consumer laptop may be better bet.

Acer unveiled a pair of AMD Ryzen 8040 series laptops on Tuesday. Unsurprisingly, given their chips' dedicated neural processing units (NPU), the company is marketing the 2024 Acer Swift Edge 16 and Swift Go 14 as AI workhorses. The Windows 11 machines support OLED displays, Radeon 780M graphics and 32GB of RAM. The Ryzen 8040 chip series, revealed in December, has a dedicated AI Engine that AMD claims makes it up to 1.4 times faster than its predecessors in Llama 2 and AI vision model performance. Acer says the Swift Edge 16 and Swift Go 14 will deploy the NPU for AI-related tasks like PurifiedVoice (remove background noise in calls and recordings) and PurifiedView (blurring backgrounds in images and correcting your eyes' positioning on video calls).