The conventional wisdom of manifold learning is based on nonlinear dimensionality reduction techniques such as IsoMAP and locally linear embedding (LLE). We challenge this paradigm by exploiting the blessing of dimensionality. Our intuition is simple: it is easier to untangle a low-dimensional manifold in a higher-dimensional space due to its vastness, as guaranteed by Whitney embedding theorem. A new insight brought by this work is to introduce class labels as the context variables in the lifted higher-dimensional space (so supervised learning becomes unsupervised learning). We rigorously show that manifold untangling leads to linearly separable classifiers in the lifted space. To correct the inevitable overfitting, we consider the dual process of manifold untangling -- tangling or aliasing -- which is important for generalization. Using context as the bonding element, we construct a pair of manifold untangling and tangling operators, known as tangling-untangling cycle (TUC). Untangling operator maps context-independent representations (CIR) in low-dimensional space to context-dependent representations (CDR) in high-dimensional space by inducing context as hidden variables. The tangling operator maps CDR back to CIR by a simple integral transformation for invariance and generalization. We also present the hierarchical extensions of TUC based on the Cartesian product and the fractal geometry. Despite the conceptual simplicity, TUC admits a biologically plausible and energy-efficient implementation based on the time-locking behavior of polychronization neural groups (PNG) and sleep-wake cycle (SWC). The TUC-based theory applies to the computational modeling of various cognitive functions by hippocampal-neocortical systems.

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively.

Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery. Feedback is one of the most effective ways to have a grasp of users' experience with Windows and its ecosystem. However, the sheer volume of feedback received by Feedback Hub makes it immensely challenging to diagnose the actual cause of reported issues. To better understand and triage issues, we leverage Double Machine Learning (DML) to associate users' feedback with telemetry signals. One of the main challenges we face in the DML pipeline is the necessity of domain knowledge for model design (e.g., causal graph), which sometimes is either not available or hard to obtain. In this work, we take advantage of reasoning capabilities in Large Language Models (LLMs) to generate a prior model that which to some extent compensates for the lack of domain knowledge and could be used as a heuristic for measuring feedback informativeness. Our LLM-based approach is able to extract previously known issues, uncover new bugs, and identify sequences of events that lead to a bug, while minimizing out-of-domain outputs.

The OBSBOT Tiny PTZ 4K webcam offers an incredible array of premium features for a reasonable price, capped off by an AI-powered ability to physically track your face as you move. The OBSBOT Tiny 4K certainly ranks among the best webcams you can buy, 4K or not, period. It offers so much: 4K video, a 60fps option (albeit at 1080p), and the real magic: an automated gimbal that physically rotates and dips the webcam to center your face. The OBSBOT Tiny 4K (sometimes sold as the OBSBOT Tiny PTZ 4K) will soon be supplemented by the Tiny 2, with a larger sensor for improved video. That should help solve one of the Tiny 4K's only shortcomings: video quality is good, just not outstanding.

There are few certainties in life: Death, taxes, and turning your computer off and on when there's a problem. This advice is usually the first tip you get from friends, family, and tech support. Rebooting your computer helps keep it running smoothly. It clears the memory, stopping any tasks that are eating up RAM. Even if you've closed an app, it could still tap your memory.

Wireless operators spend millions of dollars every year paying for the electricity to power their cell sites and small cells. But there are new energy-saving features that are being developed that could make a dramatic difference in energy consumption. And these new features incorporate tools like artificial intelligence (AI) and machine learning. In a new Ericsson white paper called "Breaking the Energy Curve," the company said that machine learning can be used to make certain network features more autonomous. Two of those features, MIMO Sleep Mode and Cell Sleep Mode, are using machine learning to study data traffic patterns and save operators money.

We have no chance of escaping the coming robot revolution, nor should we want to. Our modern lives are already full of robots -- they're in our phones, our cars, hospitals and boardrooms, assisting everyone from factory workers to astrophysicists. They make our lives overwhelmingly better -- that is, until one gets between a hungover human and their morning jolt of java. In Talking to Robots, journalist and author David Ewing Duncan -- with help from some of today's leading scientific researchers -- presents 24 visions of the future and what our personal and professional interactions might look like once robots finish taking over. Need my hit of caffeine.