Predicting energy consumption in smart buildings is challenging due to dependencies in sensor data and the variability of environmental conditions. We introduce S4ConvD, a novel convolutional variant of Deep State Space Models (Deep-SSMs), that minimizes reliance on extensive preprocessing steps. S4ConvD is designed to optimize runtime in resource-constrained environments. By implementing adaptive scaling and frequency adjustments, this model shows to capture complex temporal patterns in building energy dynamics. Experiments on the ASHRAE Great Energy Predictor III dataset reveal that S4ConvD outperforms current benchmarks. Additionally, S4ConvD benefits from significant improvements in GPU runtime through the use of Block Tiling optimization techniques. Thus, S4ConvD has the potential for practical deployment in real-time energy modeling. Furthermore, the complete codebase and dataset are accessible on GitHub, fostering open-source contributions and facilitating further research. Our method also promotes resource-efficient model execution, enhancing both energy forecasting and the potential integration of renewable energy sources into smart grid systems.

Detecting hallucinations in Large Language Models (LLMs) remains a critical challenge for their reliable deployment in real-world applications. To address this, we introduce SelfCheckAgent, a novel framework integrating three different agents: the Symbolic Agent, the Specialized Detection Agent, and the Contextual Consistency Agent. These agents provide a robust multi-dimensional approach to hallucination detection. Notable results include the Contextual Consistency Agent leveraging Llama 3.1 with Chain-of-Thought (CoT) to achieve outstanding performance on the WikiBio dataset, with NonFactual hallucination detection scoring 93.64%, Factual 70.26%, and Ranking 78.48% respectively. On the AIME dataset, GPT-4o with CoT excels in NonFactual detection with 94.89% but reveals trade-offs in Factual with 30.58% and Ranking with 30.68%, underscoring the complexity of hallucination detection in the complex mathematical domains. The framework also incorporates a triangulation strategy, which increases the strengths of the SelfCheckAgent, yielding significant improvements in real-world hallucination identification. The comparative analysis demonstrates SelfCheckAgent's applicability across diverse domains, positioning it as a crucial advancement for trustworthy LLMs. These findings highlight the potentiality of consistency-driven methodologies in detecting hallucinations in LLMs.

This paper proposes a voxel-based approach for creating a digital twin of an urban environment that is capable of efficiently managing smart spaces. The paper explains the registration and localization procedure of the point cloud dataset, which uses the KISS ICP for scan point cloud combination and the RANSAC method for the initial alignment of the combined point cloud. The mobile mapping point cloud using Riegl VMX-250 serves as the reference map, and Velodyne scans are used for localization purposes. The point-to-plane iterative closest-point method is then employed to refine the alignment. The paper evaluates the efficacy of the proposed method by calculating the errors between the estimated and ground truth positions. The results indicate that the voxel-based approach is capable of accurately estimating the position of the sensor platform, which are applicable for various use cases. A specific use case in the context is smart parking space management, which is described and initial visualization results are shown.

We design a concept for an autonomous underground freight transport system for Hanover, Germany. To evaluate the resulting system changes in overall traffic flows from an environmental perspective, we carried out an agent-based traffic simulation with MATSim. Our simulations indicate comparatively low impacts on network-wide traffic volumes. Local CO2 emissions, on the other hand, could be reduced by up to 32 %. In total, the shuttle system can replace more than 18 % of the vehicles in use with conventional combustion engines. Thus, an autonomous underground freight transportation system can contribute to environmentally friendly and economical transportation of urban goods on the condition of cooperative use of the system.

Hannover Messe (or Hannover Fair), the #1 global industrial tradeshow, was back in action earlier this month. The event that took place from 30 May–02 June 2022, in Hannover, Germany, showcased once again the latest developments and industrial technology trends. Despite a much smaller crowd (75,000 visitors--roughly 40% of pre-pandemic levels), the fairgrounds were buzzing and filled with senior executives from many of the leading industrial hardware, software, and service providers. The conference remains one of those rare fairs where you randomly walk into senior executives, like a Head of Engineering for a major industrial conglomerate, and not only into the pre-sales representatives giving you the usual pitch. "In the face of disrupted supply chains, rising energy prices, inflation, and climate change, it was all the more important to meet face-to-face again in the exhibition halls after two years marked by a pandemic, to take in the latest technology trends and get a window to the future."

Hanover, Germany – Continental has acquired a minority stake in Recogni, a German-US start-up working on a new chip architecture for AI-based object-recognition in real time. The San Jose, California-based tech firm's chips are intended for use in Continental's vehicle computers, for example to perform rapid processing of sensor data for automated and autonomous driving. As an investor – percentage stake not disclosed – the Hanover-based group is contributing financial support and expertise in the field of AI, vehicle sensors and advanced driver assistance systems to Recogn's chip design work. Continental said volume production featuring the new chip application could begin as early as 2026: the new processors serving as "ultra-economical data boosters: with minimal energy consumption." The development, it added, will enable vehicle computers to gain a rapid sense of the vehicle's immediate surroundings, thus creating the basis for automated and autonomous driving.

They can unload the dishwasher, deliver packages to your home and open doors. Their thin, metallic legs are able to traverse a steep flight of stairs -- or crawl straight into your worst nightmares. Now Boston Dynamics' awkward, four-legged, doglike robot, SpotMini, is evolving from a YouTube sensation to a purchasable pet of sorts, according to the company's founder, Marc Raibert. Raibert told an audience last month at the CeBIT computer expo in Hanover, Germany, that his company is already testing SpotMini with potential customers from four separate industries: security, delivery, construction and home assistance. His presentation at the expo was reported by Inverse.

They can unload the dishwasher, deliver packages to your home and open doors. Their thin metallic legs are able to traverse a steep flight of stairs -- or crawl straight into your worst nightmares. Now Boston Dynamics's awkward, four-legged, dog-like robot, SpotMini, is evolving from a viral YouTube sensation to a purchasable pet of sorts, according to the company's founder, Marc Raibert. Raibert told an audience last month at the CeBIT computer expo in Hanover, Germany, that his company is already testing SpotMini with potential customers from four separate industries: security, delivery, construction and home assistance. His presentation at the expo was reported by Inverse.

At a technology conference in Hannover, Germany, Marc Raibert, the founder of Boston Dynamics, outlined how his company may soon begin to turn its decades-long robotics research into an actual business. Boston Dynamics was sold to SoftBank by Alphabet last year following concerns around its ability to generate revenue. Since the acquisition, it seems that the company has ramped up testing on its increasingly dexterous and nimble robots. Earlier this year, Raibert said the company planned to start selling its SpotMini robot dogs in 2019, and onstage this week, he said the company plans to produce about 100 of the robots by the end of this year. The goal is to begin mass production at the rate of about 1,000 robots per year in the middle of 2019.

HANOVER, GERMANY - APRIL 25: Close up of the digital display while a camera and radar system assists as artificial intelligence takes over driving the car during tests of autonomous car abilities conducted by Continental AG on the A2 highway on April 25, 2018, near Hanover, Germany. Israeli artificial intelligence (AI) startup, Hailo Technologies, has closed a $12.5 million series A from Maniv Mobility, OurCrowd, and NextGear to develop a chip for deep learning on edge devices and processing of high-resolution sensory data in real time. According to a report from Markets and Markets, edge computing will be worth $6.72 billion by 2020, and IC Insights reported that integrated circuits in cars are expected to generate global sales of $42.9 billion in 2021. In 2017, McKinsey reported in the study, Self Driving Car Technology: when will robots hit the road?, that ADAS systems grew to 140 million in 2016 from 90 million units in 2014. "Because of the low latency required for autonomous driving and advanced driving assistance, deep learning with convolutional neural networks, running on in-vehicle hardware, is necessary," offers Tom Coughlin, IEEE Fellow and President at Coughlin Associates.