We present a generalised Hanson-Wright inequality and use it to establish new statistical insights into the geometry of data point-clouds. In the setting of a general random function model of data, we clarify the roles played by three notions of dimensionality: ambient intrinsic dimension $p_{\mathrm{int}}$, which measures total variability across orthogonal feature directions; correlation rank, which measures functional complexity across samples; and latent intrinsic dimension, which is the dimension of manifold structure hidden in data. Our analysis shows that in order for persistence diagrams to reveal latent homology and for manifold structure to emerge it is sufficient that $p_{\mathrm{int}}\gg \log n$, where $n$ is the sample size. Informed by these theoretical perspectives, we revisit the ground-breaking neuroscience discovery of toroidal structure in grid-cell activity made by Gardner et al. (Nature, 2022): our findings reveal, for the first time, evidence that this structure is in fact isometric to physical space, meaning that grid cell activity conveys a geometrically faithful representation of the real world.

In the fields of brain-computer interaction and cognitive neuroscience, effective decoding of auditory signals from task-based functional magnetic resonance imaging (fMRI) is key to understanding how the brain processes complex auditory information. Although existing methods have enhanced decoding capabilities, limitations remain in information utilization and model representation. To overcome these challenges, we propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net), which enhances decoding performance through physics-informed embedding and deep learning techniques. PEMT-Net consists of two principal components: feature augmentation and classification. For feature augmentation, we propose a novel approach by creating neural embedding graphs via node embedding, utilizing random walks to simulate the physical diffusion of neural information. This method captures both local and non-local information overflow and proposes a position encoding based on relative physical coordinates. In the classification segment, we propose adaptive embedding fusion to maximally capture linear and non-linear characteristics. Furthermore, we propose an innovative parameter-sharing mechanism to optimize the retention and learning of extracted features. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding, surpassing existing methods and offering new insights into the brain's mechanisms for processing complex auditory information.

Flash-based solid-state drives (SSDs) are a key component in most computer systems, thanks to their ability to support parallel I/O at sub-millisecond latency and consistently high throughput. At the same time, due to the limitations of the flash media, they perform writes out-of-place, often incurring a high internal overhead which is referred to as write amplification. Minimizing this overhead has been the focus of numerous studies by the systems research community for more than two decades. The abundance of system-level optimizations for reducing SSD write amplification, which is typically based on experimental evaluation, stands in stark contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings.

With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology employs some Graph Neural Network (GNN) to capture relations between spatial locations, while another network, such as a recurrent neural network (RNN), learns temporal correlations. By representing every recorded sample as its own node in a graph, rather than all measurements for a particular location as a single node, temporal and spatial information is encoded in a similar manner. In this setting, GNNs can now directly learn both temporal and spatial dependencies, jointly, while also alleviating the need for additional temporal networks. Furthermore, the framework does not require aligned measurements along the temporal dimension, meaning that it also naturally facilitates irregular time series, different sampling frequencies or missing data, without the need for data imputation. To evaluate the proposed methodology, we consider wind speed forecasting as a case study, where our proposed framework outperformed other spatio-temporal models using GNNs with either Transformer or LSTM networks as temporal update functions.

Through the advent of the smartphone and other enabling wireless technologies there are now increased interaction between mobile devices, the physical environment and data sources within it. This scenario is known as the converging Cyber-Physical World (CPW) [Conti et al. 2012] and within this, opportunistic networking [Boldrini and Passarella 2013] is an important enabling paradigm. Opportunistic networking is an entirely self organised form of communication which functions by mobile devices, such as smartphones, transiently connecting when they come into range. This is known as the store-carry and forward paradigm, which opens up new ways to create and share knowledge through data dissemination. This makes them ideal for crowdsourcing applications where distributed resources are harnessed to provide new services for a wide range of emerging applications including smart cities, e-health, intelligent transportation systems [Conti et al. 2012]. Unlike many forms of online crowdsourcing, opportunistic networking differs in that the providers of resources are also the consumers of sub-services, described by the subset of data that is relevant for their needs and interests. This participatory prosumer model is a distinctive feature of opportunistic networking. The concept of opportunistic networking brings networking closer to the disposition of the human user because the mobile devices such as smartphones which perform the networking function are carried around throughout the users day-to-day activity. Due to this these devices can act as cyber-physical proxies for their human users [Whitaker et al. 2015], potentially autonomously discovering, collecting and evaluating data from

Companies need to focus on architecting resilience and accept that disruptive change is the norm, says research firm Gartner, which unveiled its annual look at the top strategic technology trends that organizations need to prepare for in the coming year. Gartner unveiled this year's list at its flagship IT Symposium/Xpo Americas conference, which is being held virtually this year. At the outset of the symposium, it's clear Gartner is expecting human and technology interactions will continue to challenge IT executives as companies weather the COVID-19 upheaval and current economic challenges. "The need for operational resiliency across enterprise functions has never been greater," said Brian Burke, research vice president at Gartner. "As organizations journey from responding to the COVID-19 crisis to driving growth, they must focus on the three main areas that form the themes of this year's trends: people centricity, location independence and resilient delivery," Burke said.

The digital transformation of the retail industry has been ongoing for some years. Thanks to high-level data and visitor analytics systems, companies are making well-informed and data-driven business decisions that help increase sales, marketing efficiency, and revenue across each branch. None of those insights would have been possible without artificial intelligence. AI in retail has empowered businesses to leverage advanced data to improve their retail operations and find new business opportunities. As brands compete to remain relevant, understanding why AI has become a go-to solution boils down to some key factors.

For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.

Summary: In this review of Mary Meeker's annual Internet Trends report for 2016 we'll look for the advanced analytics that makes these trends possible. It's that time of year again when Mary Meeker, the great seer of the internet once again releases her annual Internet Trends 2016 report. If by chance you don't know who Ms. Meeker is she is a partner in the VC firm Kliener Perkins and is acclaimed by Forbes to be the 77th most powerful woman in the world. She started issuing these reports in 1995 at Morgan Stanley and was and is still widely regarded as a guru of the internet. Say what you will, and she is a bit of controversial character, she gathers and analyzes voluminous amounts of data and has a well deserved track record for spotting big picture trends before they are widely recognized.

WiFi-SLAM aims to map WiFi signals within an unknown environment while simultaneously determining the location of a mobile device. This localization method has been extensively used in indoor, space, undersea, and underground environments. For the sake of accuracy, most methods label the signal readings against ground truth locations. However, this is impractical in large environments, where it is hard to collect and maintain the data. Some methods use latent variable models to generate latent-space locations of signal strength data, an advantage being that no prior labeling of signal strength readings and their physical locations is required. However, the generated latent variables cannot cover all wireless signal locations and WiFi-SLAM performance is significantly degraded. Here we propose the diversified generative latent variable model (DGLVM) to overcome these limitations. By building a positive-definite kernel function, a diversity-encouraging prior is introduced to render the generated latent variables non-overlapping, thus capturing more wireless signal measurements characteristics. The defined objective function is then solved by variational inference. Our experiments illustrate that the method performs WiFi localization more accurately than other label-free methods.