Traffic flow forecasting is a crucial first step in intelligent and proactive traffic management. Traffic flow parameters are volatile and uncertain, making traffic flow forecasting a difficult task if the appropriate forecasting model is not used. Additionally, the non-Euclidean data structure of traffic flow parameters is challenging to analyze from both spatial and temporal perspectives. State-of-the-art deep learning approaches use pure convolution, recurrent neural networks, and hybrid methods to achieve this objective efficiently. However, many of the approaches in the literature rely on complex architectures that can be difficult to train. This complexity also adds to the black-box nature of deep learning. This study introduces a novel deep learning architecture, referred to as the multigraph convolution neural network (MGCNN), for turning movement prediction at intersections. The proposed architecture combines a multigraph structure, built to model temporal variations in traffic data, with a spectral convolution operation to support modeling the spatial variations in traffic data over the graphs. The proposed model was tested using twenty days of flow and traffic control data collected from an arterial in downtown Chattanooga, TN, with ten signalized intersections. The model's ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into the future was evaluated against four baseline state-of-the-art models. The results showed that our proposed model is superior to the other baseline models in predicting turning movements with a mean squared error (MSE) of 0.9

In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Salmonella infection. Data is collected from several strains of collaborative cross mice. Samples from the spleen and liver serve as the two domains. Several machine learning experiments are conducted and the small subset of discriminative across domains features have been extracted in each case. The algorithm proves viable and underlines the benefits of across domain feature selection by extracting new subset of discriminative features which couldn't be extracted only by one-domain approach.

Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spatial domains. The Fairguard framework consists of two phases: first, we develop a static generator that is able to reduce data bias based on temporal logic conditions by minimizing correlations between selected attributes. Then, to ensure fairness in predictive algorithms, we design a dynamic component to regulate prediction results and generate future fair predictions by harnessing logic rules. Evaluations show that logic-enabled static Fairguard can effectively reduce the biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.

CHATTANOOGA, Tenn.--Johnathan Bragg has always looked at his job selling home-repair insurance the same way an artist looks at a canvas. "I got this road map in my head of what it looks like when you're delivering world-class customer service--what triggers people, what makes people trust you," Mr. Bragg said. "It's like when da Vinci was painting."

CHATTANOOGA, TENNESSEE - Using unstructured healthcare text first requires applying some level of natural language processing (NLP), but NLP by itself is not enough to provide answers to all the complex questions in healthcare today. While NLP can be used to extract values, such as a tumor size after radiation treatment or whether or not a patient has been prescribed a specific treatment, classification is used to provide answers to more complex questions such as whether a patient will respond to treatment. Classification is the process of identifying a category or label for a new event based on a labeled training dataset. A familiar classification use case is assigning a label such as spam to an email. In a clinical setting, however, the challenges are much more complex.

Artificial-intelligence capabilities, like conversational AI software, can speed up the early back-and-forth emails, texts and other communications with applicants and quickly get strong candidates in front of recruiters. Other AI-enabled tools are being used to accelerate the employee onboarding process, getting new hires oriented, trained and set up with computers, business apps and corporate email accounts. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Trucking company U.S. Xpress Enterprises Inc. uses conversational AI software to handle most of the early stages of the hiring process, including text exchanges with job applicants, said Amanda Thompson, the Chattanooga, Tenn.-based business's chief people officer. When job seekers submit an application via a mobile device, the AI tool automatically replies with a series of preliminary questions, she said.

The push toward automation comes as businesses say they can't hire warehouse workers fast enough to meet surging online demand for everything from furniture to frozen food in pandemic-disrupted supply chains. The crunch is accelerating the adoption of robots and other technology in a sector that still largely relies on workers pulling carts. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. "This is not about taking over your job, it's about taking care of those jobs we can't fill," said Kristi Montgomery, vice president of innovation, research and development for Kenco Logistics Services LLC, a third-party logistics provider based in Chattanooga, Tenn. Kenco is rolling out a fleet of self-driving robots from Locus Robotics Corp. to bridge a labor gap by helping workers fill online orders at the company's largest e-commerce site, in Jeffersonville, Ind.

Ford Motor and Volkswagen announced a worldwide partnership on Tuesday that's aimed at saving the two companies millions on development of pickup trucks, vans and transit vehicles, with an eye toward working together in the future on self-driving and electric cars. Under the alliance, each company will design and produce cars for the other. In Europe, Volkswagen will begin to sell Ford-produced medium pickups and commercial vans by 2022, and Volkswagen will develop a city-oriented van for Ford that would arrive by 2023. Each company would enjoy the flexibility to brand and market the new vehicles according to its own strategies, executives said. The partnership is not a merger and will not involve either company taking an ownership stake in the other.

Ford Motor and Volkswagen announced a worldwide partnership on Tuesday that's aimed at saving the two companies millions on development of pickup trucks, vans and transit vehicles, with an eye toward working together in the future on self-driving and electric cars. Under the alliance, each company will design and produce cars for the other. In Europe, Volkswagen will begin to sell Ford-produced medium pickups and commercial vans by 2022, and Volkswagen will develop a city-oriented van for Ford that would arrive by 2023. Each company would enjoy the flexibility to brand and market the new vehicles according to its own strategies, executives said. The partnership is not a merger and will not involve either company taking an ownership stake in the other.

Uber has launched a'panic button' allowing passengers to call 911 directly from its app during a ride. The emergency button is part of a new'safety center' menu designed to help passengers if something goes wrong during their trip. It is hoped the feature will help allay concerns over the safety of Uber. The emergency button is located in a new'safety center' menu that is easily accessible from the app's home screen, giving riders a quick way to contact first responders in the event that something goes wrong during their trip. To dial 911, riders will need to swipe up on the safety center icon, and then tap '911 assistance.'