Our Sarper Ozharar, Yue Tian and Yangmin Ding and Jessica L. Ware from the American Museum of Natural History have discovered that fiber optic cables equipped with distributed acoustic sensing (DAS) can pick up the sounds of Brood X cicadas. DAS technology, typically used to monitor seismic activity, can detect the vibrations caused by the loud sounds of cicadas, which live underground for years until they come up to mate.
About NEC Labs America
This author has not written his bio yet.
But we are proud to say that NEC Labs America contributed 494 entries already.
Entries by NEC Labs America
NEC Labs America is proud to be a Silver Sponsor for NeurIPS 2023 in New Orleans from December 10-16. Visit our booth to meet our team and learn about our intern opportunities in machine learning, data science, media analytics and integrated systems. Also, our Vijay Kumar.B.G, Samuel Schulter & Manmohan Chandraker, along with Zaid Khan, Northeastern University and Yun Fu, UC San Diego will present a paper, Exploring Question Decomposition for Zero-Shot VQA,
Sarper Ozharar was awarded an Achievement in Science and Technology Award from Koç University on their notable 30th anniversary. As an alumnus of this esteemed institution, Sarper shared that this recognition is especially meaningful to him, marking a significant milestone in his professional journey.
We present the field trial of an innovative neural network and DAS-based technique, employing a pre-trained CNN fine-tuning strategy for effective rain detection and classification within two practical scenarios.
A novel acoustic transmission technique using distributed acoustic sensors is introduced. By choosing better incident angles for smaller fading and employing an 8- channel beamformer, over 10KB data is transmitted at a 6.4kbps data rate.
Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below 10?2.
Acoustic data transmission with the Orthogonal Frequency Division Multiplexing (OFDM) signal has been demonstrated using a Distributed Acoustic Sensor (DAS) based onPhase-sensitive Optical Time-Domain Reflectometry (?-OTDR).
Our Chris White outlines how Invisible AI transforms our lives and its potential to bring about transformative societal changes, including safer space travel, reliable cell networks, smarter cities, productive factories, and efficient homes. Invisible AI will intelligently anticipate needs, automate tasks, and enhance the human experience.
Graph neural networks (GNNs) have achieved great success in dealing with graph-structured data that are prevalent in the real world. The core of graph neural networks is the message passing mechanism that aims to generate the embeddings of nodes by aggregating the neighboring node information. However, recent work suggests that GNNs also suffer the trustworthiness issues. Our empirical study shows that the calibration error of the in-distribution (ID) nodes would be exacerbated if a graph is mixed with out-of-distribution (OOD) nodes, and we assume that the noisy information from OOD nodes is the root for the worsened calibration error. Both previous study and our empirical study suggest that adjusting the weights of edges could be a promising way to reduce the adverse impact from the OOD nodes. However, how to precisely select the desired edges and modify the corresponding weights is not trivial, since the distribution of OOD nodes is unknown to us. To tackle this problem, we propose a Graph Edge Re-weighting via Deep Q-learning (GERDQ) framework to calibrate the graph neural networks. Our framework aims to explore the potential influence of the change of the edge weights on target ID nodes by sampling and traversing the edges in the graph, and we formulate this process as a Markov Decision Process (MDP). Many existing GNNs could be seamlessly incorporated into our framework. Experimental results show that when wrapped with our method, the existing GNN models can yield lower calibration error under OOD nodes as well as comparable accuracy compared to the original ones and other strong baselines. The source code is available at:https://github.com/DamoSWL/Calibration-GNN-OOD.
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models respectively exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.
4 Independence Way, Suite 200
Princeton, NJ 08540
San Jose Office
2033 Gateway Place, Suite 200
San Jose, CA 95110
NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.
Read Our Blog Posts
- Unearthing Nature’s Orchestra – How Fiber Optic Cables Can Hear Cicada Secrets
- NEC Labs America Team Heading to NeurIPS23 in New Orleans
- Sarper Ozharar Receives Award from Koç University
- Meet the NEC Labs America Intern Helping to Make Autonomous Vehicles Safer and More Secure
- AI/Fiber-Optic Combo Poised To Improve Telecommunications
- Industrial Labs to Drive Disruptive Innovation for the Fourth Industrial Revolution
- A New Hope: AI Research is Conquering Today’s Computer Vision Plateau
- NEC Labs America’s Time Series Data Research Drives Space Systems Innovation
- Next-Generation Computing Finally Sees Light
- AI/Fiber-Optic Combo Poised To Improve Telecommunications
- Using AI To Safely Put The First Woman On The Moon
- Our AI Research Contributing to NASA’s Artemis Space Program
- NEC provides AI-based traffic monitoring system with fiber-optic sensing technology for NEXCO CENTRAL