Compare MSI Roamii BE Pro vs BE Lite mesh systems and Wi-Fi 7 adapters to choose the right upgrade solution for your home computing setup. Why is my Wi-Fi still slow after upgrading my internet plan? Your router is likely the bottleneck, not your internet plan. If your wired speeds are faster than your Wi-Fi, or performance drops in certain rooms, the issue is almost certainly your router hardware - and upgrading it will make a bigger difference than paying for a faster plan. A faster internet plan doesn't always translate to faster real-world Wi-Fi - your router may be limiting the speeds your devices receive.

Quantization is typically viewed as a pragmatic trade-off between model fidelity and computational costs [2, 3, 13]. Aggressive 2-bit ternary-state schemes are now commonly used to allow large-language models (LLMs) to run on commodity accelerators and edge devices. However, this leads to training-time issues resulting from intervals in which the quantizer output is numerically zero and the surrogate gradient vanishes. These near-zero intevals are referred to as "dead zones" [11]. We introduce a Signed-Zero Ternary (SZT) quantization in which we use the remaining fourth state in the 2-bit ternary encoding to distinguish two zero states (code words). This approach retains the benefits of ternary-state quantization while adding 1-bit gradient information at essentially no cost. This preserves the forward-path behavior of balanced ternary while the back-propagation rule remains fully deterministic for the straight-through form. We argue that availability of gradient information in this maximally quantized representation may tend to maximize overall information density rather than approximate it. All analytical results are obtained via changes to the encode/decode logic only, leaving the matrix-multiply datapath untouched, i.e., 1

It is important to reveal the inverse dynamics of manipulators to improve control performance of model-based control. Neural networks (NNs) are promising techniques to represent complicated inverse dynamics while they require a large amount of motion data. However, motion data in dead zones of actuators is not suitable for training models decreasing the number of useful training data. In this study, based on the fact that the manipulator joint does not work irrespective of input torque in dead zones, we propose a new loss function that considers only errors of joints not in dead zones. The proposed method enables to increase in the amount of motion data available for training and the accuracy of the inverse dynamics computation. Experiments on actual equipment using a three-degree-of-freedom (DOF) manipulator showed higher accuracy than conventional methods. We also confirmed and discussed the behavior of the model of the proposed method in dead zones.

Kyiv, Ukraine – The enemy "turns border districts into a dead zone", a war correspondent covering the Russia-Ukraine war wrote on his Telegram channel on Saturday. But retired colonel Yuri Kotyonok, who reported from almost every war zone in the former Soviet Union and whose Telegram channel has 420,000 subscribers, was not talking about Ukraine. The districts belong to the western Russian region of Belgorod that borders Ukraine. In recent months, it has been shelled and attacked by drones hundreds of times – 130 in May alone, Russian officials say. As a result, 32 people were killed and 157 wounded, regional governor Vyacheslav Gladkov said in late April.

A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as "Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.

Machine learning enables us to correctly classify a file as either benign or malicious over 99% of the time. But the question then becomes, how can this classifier be attacked? Is it possible to alter the file in such a way as to trick the classifier? We often make the mistake of assuming the model is judging as we judge, i.e., we assume the machine learning model has baked into it a conceptual understanding of the objects being classified. For example, let's look at lie detectors.

A new study exploring depths of the Gulf of Oman revealed a massive increase in the size of its "dead zone," an area with too little oxygen for the survival of marine life. First spotted nearly half a century ago, dead-zones, aka oxygen minimum zones or OMZs, were flagged as a major threat to marine biology. They occur naturally at depths ranging from 700 to 2500 feet due to changes in the level of atmospheric oxygen and have been located in three to four parts of the world including the Gulf of Oman which shares its waters with the Arabian Sea. Now, that dead-zone appears to have grown bigger than what was previously thought. During a recent study, scientists from the University of East Anglia (UEA) sent two underwater robots, dubbed Seagliders, into the gulf to create a detailed picture of oxygen levels and the mechanics that mix oxygen and other nutrients into the water.

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is randomly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is randomly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value,; then, another item is randomly selected.The convergence time can be dramatically increased or decreased by this heuristic, depending on the task, and is very sensitive to the value of .