Keeping track of your pets has never been easier with these gadgets. If you're a pet owner and lover like me, you know there's nothing more important than keeping your furry friend safe. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER As luck would find it, there have never been as many affordable options that make all the difference between a missing pet poster and being together safe and sound at home. That's why I recommend you invest in a pet tracker of some kind so you can always have peace of mind knowing where your pet is. There are lots of pet trackers on the market, so before you make your choice, make sure the one you're purchasing has all these features.

Scan provided by Dr Quentin Meyer. Researchers from UNSW Sydney have developed an algorithm which produces high-resolution modelled images from lower-resolution micro X-ray computerised tomography (CT). The new process, detailed in a paper published in Nature Communications, has been tested on individual hydrogen fuel cells to accurately model the interior in precise detail and potentially improve the efficiency of them. But the researchers say it could also be used in future on human X-rays to give medical professionals a better understanding of tiny cellular structures inside the body, which could allow for better and faster diagnosis of a wide range of diseases. The team, featuring Professor Ryan Armstrong, Professor Peyman Mostaghimi, Dr Ying Da Wang, and Kunning Tang from the School of Mineral and Energy Resources Engineering and Prof Chuan Zhao and Dr Quentin Meyer from the School of Chemistry, developed the algorithm to improve the understanding of what is happening inside a Proton Exchange Membrane Fuel Cell (PEMFC).

Cloud computing holds the promise of reduced costs through economies of scale. To realize this promise, cloud computing vendors typically solve sequential resource allocation problems, where customer workloads are packed on shared hardware. Virtual machines (VM) form the foundation of modern cloud computing as they help logically abstract user compute from shared physical infrastructure. Traditionally, VM packing problems are solved by predicting demand, followed by a Model Predictive Control (MPC) optimization over a future horizon. We introduce an approximate formulation of an industrial VM packing problem as an MILP with soft-constraints parameterized by the predictions. Recently, predict-and-optimize (PnO) was proposed for end-to-end training of prediction models by back-propagating the cost of decisions through the optimization problem. But, PnO is unable to scale to the large prediction horizons prevalent in cloud computing. To tackle this issue, we propose the Predict-and-Critic (PnC) framework that outperforms PnO with just a two-step horizon by leveraging reinforcement learning. PnC jointly trains a prediction model and a terminal Q function that approximates cost-to-go over a long horizon, by back-propagating the cost of decisions through the optimization problem \emph{and from the future}. The terminal Q function allows us to solve a much smaller two-step horizon optimization problem than the multi-step horizon necessary in PnO. We evaluate PnO and the PnC framework on two datasets, three workloads, and with disturbances not modeled in the optimization problem. We find that PnC significantly improves decision quality over PnO, even when the optimization problem is not a perfect representation of reality. We also find that hardening the soft constraints of the MILP and back-propagating through the constraints improves decision quality for both PnO and PnC.

Quantization methods reduce the number of bits required to represent each parameter in a model, trading accuracy for smaller memory footprints and inference latencies. However, the final model size depends on both the number of parameters of the original model and the rate of compression. For example, a 30B 8-bit model and a 60B 4-bit model have the same number of bits but may have very different zero-shot accuracies. In this work, we study this trade-off by developing inference scaling laws of zero-shot performance in Large Language Models (LLMs) to determine the bit-precision and model size that maximizes zero-shot performance. We run more than 35,000 experiments with 16-bit inputs and k-bit parameters to examine which zero-shot quantization methods improve scaling for 3 to 8-bit precision at scales of 19M to 176B parameters across the LLM families BLOOM, OPT, NeoX/Pythia, and GPT-2. We find that it is challenging to improve the bit-level scaling trade-off, with the only improvements being the use of a small block size -- splitting the parameters into small independently quantized blocks -- and the quantization data type being used (e.g., Int vs Float). Overall, our findings show that {4-bit} precision is almost universally optimal for total model bits and zero-shot accuracy.

Stochastic models with global parameters $\bm{\theta}$ and latent variables $\bm{z}$ are common, and variational inference (VI) is popular for their estimation. This paper uses a variational approximation (VA) that comprises a Gaussian with factor covariance matrix for the marginal of $\bm{\theta}$, and the exact conditional posterior of $\bm{z}|\bm{\theta}$. Stochastic optimization for learning the VA only requires generation of $\bm{z}$ from its conditional posterior, while $\bm{\theta}$ is updated using the natural gradient, producing a hybrid VI method. We show that this is a well-defined natural gradient optimization algorithm for the joint posterior of $(\bm{z},\bm{\theta})$. Fast to compute expressions for the Tikhonov damped Fisher information matrix required to compute a stable natural gradient update are derived. We use the approach to estimate probabilistic Bayesian neural networks with random output layer coefficients to allow for heterogeneity. Simulations show that using the natural gradient is more efficient than using the ordinary gradient, and that the approach is faster and more accurate than two leading benchmark natural gradient VI methods. In a financial application we show that accounting for industry level heterogeneity using the deep model improves the accuracy of probabilistic prediction of asset pricing models.

This paper presents a deep learning based model predictive control algorithm for control affine nonlinear discrete time systems with matched and bounded state-dependent uncertainties of unknown structure. Since the structure of uncertainties is not known, a deep neural network (DNN) is employed to approximate the disturbances. In order to avoid any unwanted behavior during the learning phase, a tube based model predictive controller is employed, which ensures satisfaction of constraints and input-to-state stability of the closed-loop states.

When can the input of a ReLU neural network be inferred from its output? In other words, when is the network injective? We consider a single layer, $x \mapsto \mathrm{ReLU}(Wx)$, with a random Gaussian $m \times n$ matrix $W$, in a high-dimensional setting where $n, m \to \infty$. Recent work connects this problem to spherical integral geometry giving rise to a conjectured sharp injectivity threshold for $\alpha = \frac{m}{n}$ by studying the expected Euler characteristic of a certain random set. We adopt a different perspective and show that injectivity is equivalent to a property of the ground state of the spherical perceptron, an important spin glass model in statistical physics. By leveraging the (non-rigorous) replica symmetry-breaking theory, we derive analytical equations for the threshold whose solution is at odds with that from the Euler characteristic. Furthermore, we use Gordon's min--max theorem to prove that a replica-symmetric upper bound refutes the Euler characteristic prediction. Along the way we aim to give a tutorial-style introduction to key ideas from statistical physics in an effort to make the exposition accessible to a broad audience. Our analysis establishes a connection between spin glasses and integral geometry but leaves open the problem of explaining the discrepancies.

This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously incorporates intra-image information to promote sparsity in each individual edge map with inter-image information to promote similarities in any unchanged regions. By treating both the edges as well as the similarity between adjacent images as random variables, there is no need to separately form regions of change. Thus we avoid both additional computational cost as well as any information loss resulting from pre-processing the image. Our numerical examples demonstrate that our new method compares favorably with more standard SBL approaches.

Point cloud data plays an essential role in robotics and self-driving applications. Yet, annotating point cloud data is time-consuming and nontrivial while they enable learning discriminative 3D representations that empower downstream tasks, such as classification and segmentation. Recently, contrastive learning-based frameworks have shown promising results for learning 3D representations in a self-supervised manner. However, existing contrastive learning methods cannot precisely encode and associate structural features and search the higher dimensional augmentation space efficiently. In this paper, we present CLR-GAM, a novel contrastive learning-based framework with Guided Augmentation (GA) for efficient dynamic exploration strategy and Guided Feature Mapping (GFM) for similar structural feature association between augmented point clouds. We empirically demonstrate that the proposed approach achieves state-of-the-art performance on both simulated and real-world 3D point cloud datasets for three different downstream tasks, i.e., 3D point cloud classification, few-shot learning, and object part segmentation.

While each new learning task requires the handcrafted design of a new DNN, automated deep learning facilitates the creation of powerful DNNs. Interests are to give access to deep learning to less experienced people, to reduce the tedious tasks of managing many parameters to reach the optimal DNN, and finally, to go beyond what humans can design by creating non-intuitive DNNs that can ultimately prove to be more efficient. Optimizing a DNN means automatically finding an optimal architecture for a given learning task: choosing the operations and the connections between those operations and the associated hyperparameters. The first task is also known as Neural Architecture Search [Elsken et al., 2019], also named NAS, and the second, as HyperParameters Optimization (HPO). Most works from the literature try to tackle only one of these two optimization problems. Many papers related to NAS [White et al., 2021, Loni et al., 2020b, Wang et al., 2019b, Sun et al., 2018b, Zhong, 2020] focus on designing optimal architectures for computer vision tasks with a lot of stacked convolution and pooling layers. Because each DNN training is time-consuming, researchers tried to reduce the search space by adding many constraints preventing from finding irrelevant architectures. It affects the flexibility of the designed search spaces and limits the hyperparameters optimization.