International series in pure and applied mathematics.

A surge of applicants vying for a chance to be chosen for a voucher worth up to 2,000 for the California E-Bike Incentive Program triggered an error in the program's website, blocking everyone from applying. Officials say they've fixed the glitch for the next round of applications next week. The California E-Bike Incentive Program, launched by the California Air Resources Board, was established to help lower cost barriers to alternative methods of transportation such as e-bikes, with the goal of getting cars off the road and reduce greenhouse gas emissions. Eligible residents must be 18 years or older with an annual household income less than 300% of the Federal Poverty Level. The vouchers can be used toward the purchase of an electric bike.

A routine training flight over Arizona in January 2023 took an unusual turn when a U.S. Air Force F-16D was struck by what was initially reported as an unidentified object, but now U.S. defense officials say was a small drone. Fox News confirmed that the incident, which occurred near Gila Bend, Arizona, on Jan. 19, 2023, was a routine training mission and was witnessed by the instructor pilot seated in the rear of the two-seat aircraft. According to a U.S. defense official, the pilot observed a "mostly white and orange object" collide with the left side of the aircraft canopy, the transparent covering over the cockpit. Initially, the object was thought to be a bird, a common hazard for aircraft. But after conducting checks during the flight and a detailed inspection upon landing at Tucson International Airport, the crew found "zero evidence" of a bird strike.

As soon as Echo Park Eats opened on the corner of Sunset Boulevard and Douglas Street in the fall of 2023, Sandy Romero said her neighborhood became overrun with delivery drivers. "The first day that they opened business it was chaotic, unorganized and it's just such a nuisance now," she said. Echo Park Eats is a ghost kitchen, a meal preparation hub for app-based delivery orders. It rents its kitchens to 26 different food vendors. The facility is part of CloudKitchens, led by Travis Kalanick, co-founder of Uber Technologies, which has kitchen locations across the nation including 11 in Los Angeles County.

Multi-agent control is a central theme in the Cyber-Physical Systems (CPS). However, current control methods either receive non-Markovian states due to insufficient sensing and decentralized design, or suffer from poor convergence. This paper presents the Delayed Propagation Transformer (DePT), a new transformerbased model that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world. DePT induces a cone-shaped spatial-temporal attention prior, which injects the information propagation and aggregation principles and enables a global view. With physical constraint inductive bias baked into its design, our DePT is ready to plug and play for a broad class of multi-agent systems. The experimental results on one of the most challenging CPS - network-scale traffic signal control system in the open world - show that our model outperformed the state-of-the-art expert methods on synthetic and real-world datasets.

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i.e., cyclic sequences). We train DACT using Proximal Policy Optimization and design a curriculum learning strategy for better sample efficiency. We apply DACT to solve the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Results show that our DACT outperforms existing Transformer based improvement models, and exhibits much better generalization performance across different problem sizes on synthetic and benchmark instances, respectively.

Large language models such as GPT, Llama, Claude, and DeepSeek can be so fluent that people feel it as a "you," and it answers encouragingly as an "I." The models can write poetry in nearly any given form, read a set of political speeches and promptly sift out and share all the jokes, draw a chart, code a website. How do they do these and so many other things that were just recently the sole realm of humans? Practitioners are left explaining jaw-dropping conversational rabbit-from-a-hat extractions with arm-waving that the models are just predicting one word at a time from an unthinkably large training set scraped from every recorded written or spoken human utterance that can be found--fair enough--or a with a small shrug and a cryptic utterance of "fine-tuning" or "transformers!" These aren't very satisfying answers for how these models can converse so intelligently, and how they sometimes err so weirdly.

This paper solves three NP-hard routing problems, traveling salesman problem (TSP), prize collecting TSP (PCTSP), and capacitated vehicle routing problem (CVRP). This section provides detailed descriptions of PCTSP and CVRP (for TSP, see section 3). The PCTSP is similar to TSP, while there are differences in that we do not have to visit all the nodes and that the destination is not the first node but the depot node, i.e., a tour is not a cycle. Let N be the number of nodes. R is the prize of visited node.

Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to learn complex environment conditions and make better decisions. However, such "black-box" policies lack interpretability and reliability, and often, they need to operate outside the traditional TCP datapath due to the use of complex NNs. This paper proposes a novel two-stage solution to achieve the best of both worlds: first to train a deep RL agent, then distill its (over-)parameterized NN policy into white-box, light-weight rules in the form of symbolic expressions that are much easier to understand and to implement in constrained environments. At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree. The distilled symbolic rules preserve and often improve performance over state-of-the-art NN policies while being faster and simpler than a standard neural network. We validate the performance of our distilled symbolic rules on both simulation and emulation environments.

It was the first time an electric vehicle led the field at NASCAR's most famous race. Chevrolet made history at the 67th Daytona 500 by introducing the 2025 Blazer EV SS as the official pace car. This marked the first time an electric vehicle led the field at NASCAR's most iconic race, a striking symbol of how the automotive world is shifting toward electrification while still honoring its racing heritage. The Blazer EV SS isn't just any electric SUV; it's the quickest SS model Chevrolet has ever built, and it turned heads both on and off the track. JOIN THE FREE "CYBERGUY REPORT": GET MY EXPERT TECH TIPS, CRITICAL SECURITY ALERTS AND EXCLUSIVE DEALS, PLUS INSTANT ACCESS TO MY FREE "ULTIMATE SCAM SURVIVAL GUIDE" WHEN YOU SIGN UP!