We propose two sparsity-aware normalized subband adaptive filter (NSAF) algorithms by using the gradient descent method to minimize a combination of the original NSAF cost function and the l1-norm penalty function on the filter coefficients. This l1-norm penalty exploits the sparsity of a system in the coefficients update formulation, thus improving the performance when identifying sparse systems. Compared with prior work, the proposed algorithms have lower computational complexity with comparable performance. We study and devise statistical models for these sparsity-aware NSAF algorithms in the mean square sense involving their transient and steady -state behaviors. This study relies on the vectorization argument and the paraunitary assumption imposed on the analysis filter banks, and thus does not restrict the input signal to being Gaussian or having another distribution. In addition, we propose to adjust adaptively the intensity parameter of the sparsity attraction term. Finally, simulation results in sparse system identification demonstrate the effectiveness of our theoretical results.

Abstract--In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajectory estimation based on convolutional neural network (CNN) and long short-term memory (LSTM) network with this database. For localization, we map RSS data for a reference point to an image-like, two-dimensional array and then apply CNN which is popular in image and video analysis and recognition. For trajectory estimation, we use a modified random way point model to efficiently generate continuous step traces imitating human walking and train a stacked twolayer LSTM network with the generated data to remember the changing pattern of geomagnetic field intensity against (x, y) coordinates. Experimental results demonstrate the usefulness of our new database and the feasibility of the CNN and LSTMbased localization and trajectory estimation with the database. Index Terms--Indoor localization, trajectory estimation, received signal strength, Wi-Fi fingerprinting, deep learning, CNN, LSTM, geomagnetic field. With the increasing demands for location-aware services and proliferation of smart phones with embedded highprecision sensors, indoor localization has attracted lots of attention from the research community. Global navigation satellite system (GNSS) like global positioning system (GPS), which provides accurate geo-spatial positioning, cannot be used indoors as the radio signals from satellites is easily blocked in an indoor environment.

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.

In this paper, we study the problem of monotone (weakly) DR-submodular continuous maximization. While previous methods require the gradient information of the objective function, we propose a derivative-free algorithm LDGM for the first time. We define $\beta$ and $\alpha$ to characterize how close a function is to continuous DR-submodulr and submodular, respectively. Under a convex polytope constraint, we prove that LDGM can achieve a $(1-e^{-\beta}-\epsilon)$-approximation guarantee after $O(1/\epsilon)$ iterations, which is the same as the best previous gradient-based algorithm. Moreover, in some special cases, a variant of LDGM can achieve a $((\alpha/2)(1-e^{-\alpha})-\epsilon)$-approximation guarantee for (weakly) submodular functions. We also compare LDGM with the gradient-based algorithm Frank-Wolfe under noise, and show that LDGM can be more robust. Empirical results on budget allocation verify the effectiveness of LDGM.

We suggest a new optimization technique for minimizing the sum $\sum_{i=1}^n f_i(x)$ of $n$ non-convex real functions that satisfy a property that we call piecewise log-Lipschitz. This is by forging links between techniques in computational geometry, combinatorics and convex optimization. Example applications include the first constant-factor approximation algorithms whose running-time is polynomial in $n$ for the following fundamental problems: (i) Constrained $\ell_z$ Linear Regression: Given $z>0$, $n$ vectors $p_1,\cdots,p_n$ on the plane, and a vector $b\in\mathbb{R}^n$, compute a unit vector $x$ and a permutation $\pi:[n]\to[n]$ that minimizes $\sum_{i=1}^n |p_ix-b_{\pi(i)}|^z$. (ii) Points-to-Lines alignment: Given $n$ lines $\ell_1,\cdots,\ell_n$ on the plane, compute the matching $\pi:[n]\to[n]$ and alignment (rotation matrix $R$ and a translation vector $t$) that minimize the sum of Euclidean distances \[ \sum_{i=1}^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z \] between each point to its corresponding line. These problems are open even if $z=1$ and the matching $\pi$ is given. In this case, the running time of our algorithms reduces to $O(n)$ using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time. Generalizations for handling e.g. outliers or pseudo-distances such as $M$-estimators for these problems are also provided. Experimental results show that our provable algorithms improve existing heuristics also in practice. A demonstration in the context of Augmented Reality show how such algorithms may be used in real-time systems.

Researchers have revealed a radical new use of AI - to predict earthquakes. A team from Tokyo Metropolitan University have used machine-learning techniques to analyze tiny changes in geomagnetic fields. These allow the system, to predict natural disaster far earlier than current methods. A team from Tokyo Metropolitan University have used machine-learning techniques to analyze tiny changes in geomagnetic fields. These allow the system, to predict natural disaster far earlier than current methods.

The Dubai Civil Aviation Authority has launched a new artificial intelligence-led programme that will help manage airspace-related incidents. The system, called Integrated Investigation and Notification (IIAN), will be a virtual manager and push through notifications to concerned people at the Dubai Civil Aviation Authority (DCAA) once any kind of incident occurs, including airspace and ground-related incidents. The IIAN has been launched to help the DCAA respond faster to problems and improve efficiency among staff in the authority. Part of the system functions using artificial intelligence (AI) and will also help predict what the root of the problem is. Abdulla Mohammed Al Blooshi, head of accidents investigations section at the DCAA, told Khaleej Times: "The system is born from our weakness. We had weaknesses in the system itself which delayed the process and made it very difficult to follow up certain investigations to a level we wanted to. However, we turned our weaknesses to strengths by using this system. We created a specific feature to target every weakness that we have and each feature is quite unique and innovative. Basically, the air control tower usually calls us if they need to notify us, but through the system, they will be able to type in the notification and they can send it to our duty investigator, who in turn will verify the information before pressing the submit button."

Chinese telecommunications giant Huawei is driving the use of artificial intelligence (AI) to build smart cities. At Huawei Connect 2018 in Shanghai, China, the company held a Smart City Summit with the theme "Activate Intelligence to Build Better Smart Cities". It featured discussions with industry experts and smart city practitioners on how to build new smart cities using AI. "Huawei is committed to becoming a smart city enabler and promoter by providing a city nervous system," said Zheng Zhibin, president of the global smart city business department at Huawei Enterprise. "Currently, Huawei is developing a ' AI Smart City Digital Platform' which is built upon the strategy of'Platform Ecosystem'.

Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2019. Analysts presented their findings during Gartner Symposium/ITxpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years. "The Intelligent Digital Mesh has been a consistent theme for the past two years and continues as a major driver through 2019. Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of a ContinuousNEXT strategy," said David Cearley, vice president and Gartner Fellow.

Two Artificial Intelligence-driven Internet paradigms may emerge in the near future. One will be based on logic, smart enterprises and human merit while the other may morph into an Orwellian control tool. Even former Google CEO Eric Schmidt has foreseen a bifurcation of the Internet by 2028 and China's eventual triumph in the AI race by 2030. In the meantime, the US seems more interested in deflecting the smart questions of today than in building the smart factories of tomorrow. Nothing embodies this better than the recent attempt by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) to create an AI-based filter to "stamp out fake-news outlets before the stories spread too widely."