Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train Couple left red-faced after buying $25K'dirt alley' at auction thinking it was bargain San Francisco home LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Troubled 350lb son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Doctors appalled by North West's new body modification warn parents to stop children from chasing the dangerous fad Alex appeared to have the dream Manhattan mom life. But she was hiding a dark secret... and it almost killed her Shocking extent America has turned on ICE is revealed as Joe Rogan breaks from conservatives still cheering Trump's army of masked men Sir Richard Branson's wife Joan dies: 'Heartbroken' Virgin tycoon pays tribute to his'best friend' after she passed away Trump gives Thanksgiving turkeys scathing nicknames and calls Pritzker a'fat slob' in fiery White House holiday speech How to tell if a man is using'therapy speak' to manipulate you: If he says any of these 15 toxic phrases, run for the hills... I'll tell you what he REALLY means: JANA HOCKING I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY As many as 172 black bears are at risk of death in Florida after a judge approved the first hunt in a decade. Leon County Circuit Judge Angela Dempsey rejected a request from Bear Warriors United, a Central Florida-based nonprofit, to halt this year's hunt, saying the group had failed to show a'substantial likelihood of success on the merits' in its lawsuit. The hunt is scheduled for December 6 through 28 on lands outside the wildlife management area system.

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

The Federal Aviation Administration (FAA) has suspended certain operations by a Texas-based drone company after a Florida boy was injured during a holiday airshow last week and had to undergo heart surgery. The FAA confirmed to Fox News Digital that it suspended the Part 107 Waiver for Sky Elements Drones. The waiver allows drone operators to fly at night, fly over people, and operate drones outside the line of sight. Thus, with this waiver suspended, Sky Elements Drones legally cannot perform its shows. It's not clear how long the pause will remain in place.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. A 7-year-old Florida boy who was injured when drones collided and fell into a crowd at a holiday airshow over the weekend underwent open heart surgery, his parents said. Adriana Edgerton and Jessica Lumsden, parents of Alexander, said one of the red and green-lit drones struck him and knocked him out upon impact, causing a chest injury, Fox Orlando reported. Hundreds of drones being used as part of a Saturday night aerial light show in Lake Eola Park in downtown Orlando appeared to be flying into position before several started falling from the sky before slamming to the ground, according to videos posted online. Alexander, a 7-year-old boy, has undergone heart surgery after he was struck by a falling drone during a holiday airshow in Orlando, his parents said.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. A child was hospitalized on Saturday after being hit by a drone that was part of an Orlando, Florida holiday drone show. According to the Orlando Fire Department, a 7-year-old boy was transported to the hospital because of injuries sustained from the falling drones, FOX 35 in Orlando reported. In a video posted online by X user MosquitoCoFl, hundreds of drones being used as part of an aerial light show appeared to be flying into position before several started falling from the sky before slamming to the ground. A man could be heard saying to children nearby, "Oh no! I don't believe they're supposed to be falling."

As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increased complexity of these HPC systems and application tuning, empirical performance tuning, such as autotuning, has emerged as a promising approach in recent years. Despite its effectiveness, autotuning is often a computationally expensive approach. Transfer learning (TL)-based autotuning seeks to address this issue by leveraging the data from prior tuning. Current TL methods for autotuning spend significant time modeling the relationship between parameter configurations and performance, which is ineffective for few-shot (that is, few empirical evaluations) tuning on new tasks. We introduce the first generative TL-based autotuning approach based on the Gaussian copula (GC) to model the high-performing regions of the search space from prior data and then generate high-performing configurations for new tasks. This allows a sampling-based approach that maximizes few-shot performance and provides the first probabilistic estimation of the few-shot budget for effective TL-based autotuning. We compare our generative TL approach with state-of-the-art autotuning techniques on several benchmarks. We find that the GC is capable of achieving 64.37% of peak few-shot performance in its first evaluation. Furthermore, the GC model can determine a few-shot transfer budget that yields up to 33.39$\times$ speedup, a dramatic improvement over the 20.58$\times$ speedup using prior techniques.

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation Algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration.

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.

The problem of identifying the k-Nearest Neighbors (kNNS) of a point has proven to be very useful both as a standalone application and as a subroutine in larger applications. Given its far-reaching applicability in areas such as machine learning and point clouds, extensive research has gone into leveraging GPU acceleration to solve this problem. Recent work has shown that using Ray Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared to traditional acceleration using shader cores. However, the existing translation of kNNS to a ray tracing problem imposes a constraint on the search space for neighbors. Due to this, we can only use RT cores to accelerate fixed-radius kNNS, which requires the user to set a search radius a priori and hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we incrementally grow the search space until all points have found their k neighbors. We show that our approach is orders of magnitude faster than existing approaches and can even be used to accelerate fixed-radius neighbor searches.

We consider two simple asynchronous opinion dynamics on arbitrary graphs where every node $u$ has an initial value $\xi_u(0)$. In the first process, the NodeModel, at each time step $t\ge 0$, a random node $u$ and a random sample of $k$ of its neighbours $v_1,v_2,\cdots,v_k$ are selected. Then, $u$ updates its current value $\xi_u(t)$ to $\xi_u(t+1) = \alpha \xi_u(t) + \frac{(1-\alpha)}{k} \sum_{i=1}^k \xi_{v_i}(t)$, where $\alpha \in (0,1)$ and $k\ge 1$ are parameters of the process. In the second process, the EdgeModel, at each step a random pair of adjacent nodes $(u,v)$ is selected, and then node $u$ updates its value equivalently to the NodeModel with $k=1$ and $v$ as the selected neighbour. For both processes, the values of all nodes converge to $F$, a random variable depending on the random choices made in each step. For the NodeModel and regular graphs, and for the EdgeModel and arbitrary graphs, the expectation of $F$ is the average of the initial values $\frac{1}{n}\sum_{u\in V} \xi_u(0)$. For the NodeModel and non-regular graphs, the expectation of $F$ is the degree-weighted average of the initial values. Our results are two-fold. We consider the concentration of $F$ and show tight bounds on the variance of $F$ for regular graphs. We show that, when the initial values do not depend on the number of nodes, then the variance is negligible, hence the nodes are able to estimate the initial average of the node values. Interestingly, this variance does not depend on the graph structure. For the proof we introduce a duality between our processes and a process of two correlated random walks. We also analyse the convergence time for both models and for arbitrary graphs, showing bounds on the time $T_\varepsilon$ required to make all node values `$\varepsilon$-close' to each other. Our bounds are asymptotically tight under assumptions on the distribution of the initial values.