Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this paper, we propose an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm is based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. We next propose a line-fitting algorithm that yields robust results for curb locations. Finally, we demonstrate the practical feasibility of our solution by testing in different road environments and evaluating our implementation in a real vehicle\footnote{Demo video clips demonstrating our algorithm have been uploaded to Youtube: https://www.youtube.com/watch?v=w5MwsdWhcy4, https://www.youtube.com/watch?v=Gd506RklfG8.}. Our algorithm maintains over 90\% accuracy within 4.5-22 meters and 0-14 meters for the KITTI dataset and our dataset respectively, and its average processing time per frame is approximately 10 ms on Intel i7 x86 and 100ms on NVIDIA Xavier board.

"Data centre is an asset that needs to be protected"- Michael Kagan, CTO of NVIDIA On the first day of the NVIDIA GPU Technology Conference, Jensen Huang, founder of NVIDIA revealed the company's three-year DPU roadmap that featured the new NVIDIA BlueField-2 family of DPUs and NVIDIA DOCA software development kit for building applications on DPU-accelerated data centre infrastructure services. Michael Kagan, CTO of NVIDIA recently in a talk, explained the next generation of fully integrated data centres and how supercomputers and edge AI helps in augmenting such initiatives. Kagan stated that the state-of-the-art technologies from both NVIDIA and Mellanox created a great opportunity to build a new class of computers, i.e. the fully-integrated cloud data centres that are designed to handle the workload of the 21st century. Historically, servers were the unit of computing, But eventually, Moore's law has slowed down as the performance of CPUs could not keep up the workload demands. According to Kagan, with the revolution of Cloud AI and edge computing, instead of a single server, the entire data centre has become the new unit of computing designed to handle parallel workloads.

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.

Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build agents with human-like abilities, we must expand beyond handling just text. A particularly important topic is the ability to see images and communicate about what is perceived. With the goal of engaging humans in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. We study incorporating different image fusion schemes and domain-adaptive pre-training and fine-tuning strategies, and show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based conversation. We additionally investigate and incorporate safety components in our final model, and show that such efforts do not diminish model performance with respect to engagingness metrics.

While getting to grips with open banking regulation, skyrocketing transaction volumes and expanding customer expectations, banks have been rolling out major transformations of data infrastructure and partnering with Silicon Valley's most innovative tech companies to rebuild the banking business around a central nervous system. This can also be labelled as event stream processing (ESP), which connects everything happening within the business - including applications and data systems - in real-time. ESP allows banks to respond to a series of data points – events - that are derived from a system that consistently creates data – the stream – to then leverage this data through aggregation, analytics, transformations, enrichment and ingestion. Further, ESP is instrumental where batch processing falls short and when action needs to be taken in real-time, rather than on static data or data at rest. However, handling a flow of continuously created data requires a special set of technologies.

The vision of smart autonomous robots in the indoor environment is becoming a reality in the current decade. This vision is now becoming a reality because of emerging technologies of Sensor Fusion and Artificial Intelligence. Sensor fusion is aggregating informative features from disparate hardware resources. Just like autonomous vehicles, the robotic industry is quickly moving towards automatic smart robots for handling indoor tasks. Now the major question arises.

Corp. (PINKSHEETS: GCKO) -- -- announced today that they have further cost reduced their robot controller board, the GeckoSPIO, while improving ease of manufacturability and maintaining robust functionality. GeckoSystems is a dynamic leader in the emerging mobile robotics industry revolutionizing their development and usage with "Mobile Robot Solutions for Safety, Security, and Service ". "The GeckoSPIO is the critical interface between the robot's physical platform and higher AI functions. This interface provides a level of abstraction for the commands sent to, and the data sent from, the robot platform. The abstraction and hierarchal architecture the GeckoSPIO provides simplifies interacting with the platform and the real world for the high-level software, along with enabling a wide array of sensor fusion techniques. We are pleased that one of our recently hired electrical engineers has made these improvements," stated Mark Peele, Vice President, R&D, GeckoSystems.

Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease. Although limited, existing BNA estimation methods have several limitations. First, they primarily rely on a similarity network diffusion and fusion technique, which only considers node degree as a topological measure in the cross-network diffusion process, thereby overlooking rich topological measures of the brain network (e.g., centrality). Second, both diffusion and fusion techniques are implemented in fully unsupervised manner, which might decrease the discriminative power of the estimated BNAs. To fill these gaps, we propose a supervised multi-topology network cross-diffusion (SM-netFusion) framework for estimating a BNA satisfying : (i) well-representativeness (captures shared traits across subjects), (ii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we learn a weighted combination of the topological diffusion kernels derived from degree, closeness and eigenvector centrality measures in a supervised manner. Specifically, we learn the cross-diffusion process by normalizing the training brain networks using the learned diffusion kernels. Our SM-netFusion produces the most centered and representative template in comparison with its variants and state-of-the-art methods and further boosted the classification of autistic subjects by 5-15%. SM-netFusion presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further leveraged to design an efficient graph feature selection method for training predictive learners in network neuroscience.

Advancements in technology have revolutionized almost every aspect of medical imaging. With the rapid developments in high technology and modern instrumentation, medical image fusion has become a vital aid for medical diagnosis, treatment and research. Medical imaging is the process, which produces images of internal aspects of the body by either invasive or noninvasive techniques. To support more accurate clinical information, medical images are required by the physicians for diagnosis and treatment (Goshtas and Nikolov 2007; Dammavalam et al. 2012). In the field of medical image processing and analysis, radiologists require high-resolution medical images with information such as region, tissue and visualization to help with improved disease diagnosis and computer assisted surgery (National Brain Tumor Society 2015; American Brain Tumor Association 2015). These requirements cannot be resolved with single modality medical images, because each of the imaging technique is designed to capture only specific aspects of the human anatomy. Computed tomography (CT) is more popularly used for recognizing the bone structure and tumor region, the soft tissue information is more visible in magnetic resonance image (MRI), positron emission tomography (PET) is useful in the diagnosis of brain disease, brain tumors, strokes, and neuron-damaging diseases (dementia) while single photon emission computed tomography (SPECT) conveys clear information in blood flow analysis during active/inactive state of the brain (American Brain Tumor Association 2015). For efficient disease diagnosis, complementary information from multiple modalities becomes necessary (Nishele 2015). Thus, fusion of multimodality medical images has become a promising and very challenging research area in recent years (Wang et al. 2005; Brainimages&information2015).This research work focuses on designing a fusion system for complementary information retrieval and analysis for the images acquired from multiple sensors of the patient during nearly same timeframes.

Do ETL development using PDI 9.0 without coding background Bestseller What you'll learn The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. ETL is an essential component of data warehousing and analytics. Why Pentaho for ETL? Pentaho has phenomenal ETL, data analysis, metadata management and reporting capabilities. Pentaho is faster than other ETL tools (including Talend). Its GUI is easier and takes less time to learn.