PCWorld highlights the Acer TravelMate P6 14 AI, a remarkably lightweight laptop at 2.2 pounds that promises up to 30 hours of battery life through advanced engineering. The device features Intel Core Ultra Series 3 Panther Lake processors, carbon fiber and magnesium-aluminum construction, plus multiple display options including 3K OLED touchscreen. Set for August launch, this laptop targets business travelers seeking exceptional portability without sacrificing performance or battery endurance. Historically, a travel laptop with long battery life meant more weight and thickness. That's not the case with Acer's TravelMate P6 14 AI, which somehow squeezes up to 30 hours of battery life inside a laptop weighing less than 2.2 pounds.

Acer announced the Predator Atlas 8 gaming handheld featuring Intel's new Arc G3 Extreme chip, an 8-inch 1200p 120Hz screen, and up to 24GB RAM. PCWorld notes the October launch faces intense competition in the increasingly crowded premium gaming handheld market. The device includes high-end specs like 1TB Gen 4 storage, 80Wh battery, and Thunderbolt 4 ports, representing Intel's first custom handheld processor. A year or two ago, gaming handhelds were the most exciting, fastest-growing segment in the PC market. So exciting that Acer announced a couple of models that I'm not sure ever actually landed at retail .

PCWorld reports Acer unveiled two new Snapdragon-powered laptops at Computex 2026: the Swift Spin 14 AI and budget-focused Aspire Go 15. The Swift Spin 14 AI features Snapdragon X2 Elite/Plus chips with 80 TOPS AI performance, while the Aspire Go 15 uses a Snapdragon C chip prioritizing portability. Acer emphasizes exceptional battery life with the Aspire Go 15 offering up to 23 hours, marking the company's first Snapdragon entry into budget laptops. Acer showed off two new Snapdragon-powered laptops at Computex 2026: the Swift Spin 14 AI and the Aspire Go 15. The Swift Spin 14 AI is definitely the flashier one, but the cheaper Aspire Go 15 feels like the bigger story and I want to tell you why. Acer is finally bringing a lower-end Snapdragon chip into its budget lineup. Most people shopping for a cheap laptop care more about battery life anyway, and that's what Snapdragon chips do well.

Acer's Predator Helios Neo 16S AI laptop can be outfitted with Intel's new Core Ultra 9 386H CPU The company announced the gaming computer at CES. Acer just announced the Predator Helios 16S AI gaming laptop at . This computer is filled with both bells and whistles, making it a decent choice for modern gamers. To that end, the laptop can be equipped with up to an Intel Core Ultra 9 386H processor. This is Intel's upcoming flagship mobile processor that has . The Helios 16S AI can also be outfitted with up to the NVIDIA GeForce RTX 5070 GPU.

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.