Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.

Household-travel surveys (HTSs) have long provided the empirical backbone for this work by coupling rich trip diaries with respondent characteristics such as age, gender, income, and household composition. Analyses drawing on these surveys consistently show that, after accounting for the built environment, so-ciodemographic traits still correlate with car ownership, mode choice, trip frequency, and trip-chaining behavior (Bhat and Koppelman, 1994; Lee et al., 2007; Lu and Pas, 1999; McGuckin and Murakami, 1999; Mokhtarian and Chen, 2004) In the past dozen years, the ubiquity of GPS-enabled smartphones has spawned a parallel, industry-scale source of mobility evidence in passively-generated mobile data, which includes call-detail records (CDR), location-based service (LBS) pings, connected-vehicle traces, and the like (Chen et al., 2016). These datasets dwarf HTSs in both sample size and temporal length, are refreshed continuously, and can often be licensed at a fraction of the cost of running a tailored survey. Their content, however, is almost exclusively spatial temporal; they record where and when a device was observed but remain agnostic about who was holding it. This missing dimension limits many distributional and behavioral analyses, including those that require understanding how travel patterns vary across population subgroups. Despite this blind spot, public agencies have been keen on experimenting with mobile data products (Ugurel et al., 2024). Metropolitan planning organizations (MPOs) see potential in using them to stitch origin-destination matrices (Alexander et al., 2015; Iqbal et al., 2014), site electric-vehicle chargers (Yang et al., 2017), and evaluate complete-street retrofits (Bian et al., 2023). Yet the lack of respondent attributes imposes two related hazards.

Location-tracking data from the Automatic Identification System, much of which is publicly available, plays a key role in a range of maritime safety and monitoring applications. However, the data suffers from missing values that hamper downstream applications. Imputing the missing values is challenging because the values of different heterogeneous attributes are updated at diverse rates, resulting in the occurrence of multi-scale dependencies among attributes. Existing imputation methods that assume similar update rates across attributes are unable to capture and exploit such dependencies, limiting their imputation accuracy. We propose MH-GIN, a Multi-scale Heterogeneous Graph-based Imputation Network that aims improve imputation accuracy by capturing multi-scale dependencies. Specifically, MH-GIN first extracts multi-scale temporal features for each attribute while preserving their intrinsic heterogeneous characteristics. Then, it constructs a multi-scale heterogeneous graph to explicitly model dependencies between heterogeneous attributes to enable more accurate imputation of missing values through graph propagation. Experimental results on two real-world datasets find that MH-GIN is capable of an average 57% reduction in imputation errors compared to state-of-the-art methods, while maintaining computational efficiency. The source code and implementation details of MH-GIN are publicly available https://github.com/hyLiu1994/MH-GIN.

Amazon's fall Prime Day sale has come and gone, but a few of the best deals are still available. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Amazon Prime's Latest Prime Day sale has come and gone. If you are a Prime member who missed out, there's some good news--there are some leftover deals still going strong. We're still keeping you updated here with all the best markdowns on our favorite tech gear and gadgets that are still available, from Alexa-enabled speakers to robot vacs to laptops and tablets. The WIRED Reviews team tests products year-round, and at sales events like this, we only recommend deals on stuff we have actually used and approved. We sorted through thousands of deals by hand to make these picks. The Fire HD 10 is Amazon's best tablet for most people . The current model dates from 2023, but the Octa Core processor is plenty fast enough for consuming Amazon Prime content, which is really the primary reason to buy a Fire tablet. The full HD (1080p) screen won't win any awards, but it's good enough for streaming movies. Fire tablets can do double duty as an Echo speaker, too. Turn on Show Mode (swipe down on the notification overlay and check the Show Mode box) and you can query Alexa to your heart's content.

This Philips Norelco is already a champion of versatility matched with low cost--a Swiss army knife of beard, head, burns, and eyebrow guards with a nose trimmer to boot. The trimmer is high-rpm, but still quiet. The guardless blade shaves closer than most, and the shaving foil is even better. The battery lasts more than five hours. Its metal chassis offers comforting durability and heft. And unlike Philips' 9000 series, it can trim while plugged into the wall. The only real drawback is all those guards are difficult to sort and keep track of.

Generative Engine Marketing (GEM) is an emerging ecosystem for monetizing generative engines, such as LLM-based chatbots, by seamlessly integrating relevant advertisements into their responses. At the core of GEM lies the generation and evaluation of ad-injected responses. However, existing benchmarks are not specifically designed for this purpose, which limits future research. To address this gap, we propose GEM-Bench, the first comprehensive benchmark for ad-injected response generation in GEM. GEM-Bench includes three curated datasets covering both chatbot and search scenarios, a metric ontology that captures multiple dimensions of user satisfaction and engagement, and several baseline solutions implemented within an extensible multi-agent framework. Our preliminary results indicate that, while simple prompt-based methods achieve reasonable engagement such as click-through rate, they often reduce user satisfaction. In contrast, approaches that insert ads based on pre-generated ad-free responses help mitigate this issue but introduce additional overhead. These findings highlight the need for future research on designing more effective and efficient solutions for generating ad-injected responses in GEM. The benchmark and all related resources are publicly available at https://gem-bench.org/.

This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior. Our approach improves on the four-step travel model by employing publicly available microdata files and machine learning methods to predict travel behavior for a representative, synthetic population at small geographic areas. This approach enables high-resolution estimation of trip generation, trip distribution, mode choice, and route assignment. Validation using ACS/PUMS work-commute datasets demonstrates that our framework achieves higher accuracy compared to conventional approaches. The resulting granular insights enable the tailoring of interventions to address localized situations and support a range of policy applications and targeted interventions, including the optimal placement of micro-fulfillment centers, effective curb-space management, and the design of more inclusive transportation solutions particularly for vulnerable communities.

To automatically tune configurations for the best possible system performance (e.g., runtime or throughput), much work has been focused on designing intelligent heuristics in a tuner. However, existing tuner designs have mostly ignored the presence of complex performance requirements (e.g., the latency shall ideally be 2 seconds), but simply assume that better performance is always more preferred. This would not only waste valuable information in a requirement but might also consume extensive resources to tune for a goal with little gain. Yet, prior studies have shown that simply incorporating the requirement as a tuning objective is problematic since the requirement might be too strict, harming convergence; or its highly diverse satisfactions might lead to premature convergence. In this paper, we propose CoTune, a tool that takes the information of a given target performance requirement into account through co-evolution. CoTune is unique in the sense that it creates an auxiliary performance requirement to be co-evolved with the configurations, which assists the target performance requirement when it becomes ineffective or even misleading, hence allowing the tuning to be guided by the requirement while being robust to its harm. Experiment results on 162 cases (nine systems and 18 requirements) reveal that CoTune considerably outperforms existing tuners, ranking as the best for 90% cases (against the 0%--35% for other tuners) with up to 2.9x overall improvements, while doing so under a much better efficiency.

Accurate modeling of ridesourcing mode choices is essential for designing and implementing effective traffic management policies for reducing congestion, improving mobility, and allocating resources more efficiently. Existing models for predicting ridesourcing mode choices often suffer from limited predictive accuracy due to their inability to capture key psychological factors, and are further challenged by severe class imbalance, as ridesourcing trips comprise only a small fraction of individuals' daily travel. To address these limitations, this paper introduces the Synthesizing Attitudes, Predicting Actions (SAPA) framework, a hierarchical approach that uses Large Language Models (LLMs) to synthesize theory-grounded latent attitudes to predict ridesourcing choices. SAPA first uses an LLM to generate qualitative traveler personas from raw travel survey data and then trains a propensity-score model on demographic and behavioral features, enriched by those personas, to produce an individual-level score. Next, the LLM assigns quantitative scores to theory-driven latent variables (e.g., time and cost sensitivity), and a final classifier integrates the propensity score, latent-variable scores (with their interaction terms), and observable trip attributes to predict ridesourcing mode choice. Experiments on a large-scale, multi-year travel survey show that SAPA significantly outperforms state-of-the-art baselines, improving ridesourcing choice predictions by up to 75.9% in terms of PR-AUC on a held-out test set. This study provides a powerful tool for accurately predicting ridesourcing mode choices, and provides a methodology that is readily transferable to various applications.