Traffic flow prediction remains a cornerstone for intelligent transportation systems ITS, influencing both route optimization and environmental efforts. While Recurrent Neural Networks RNN and traditional Convolutional Neural Networks CNN offer some insights into the spatial temporal dynamics of traffic data, they are often limited when navigating sparse and extended spatial temporal patterns. In response, the CNN-GRUSKIP model emerges as a pioneering approach. Notably, it integrates the GRU-SKIP mechanism, a hybrid model that leverages the Gate Recurrent Unit of GRU capabilities to process sequences with the SKIP feature of ability to bypass and connect longer temporal dependencies, making it especially potent for traffic flow predictions with erratic and extended patterns. Another distinctive aspect is its non-standard 6-layer CNN, meticulously designed for in-depth spatiotemporal correlation extraction. The model comprises (1) the specialized CNN feature extraction, (2) the GRU-SKIP enhanced long-temporal module adept at capturing extended patterns, (3) a transformer module employing encoder-decoder and multi-attention mechanisms to hone prediction accuracy and trim model complexity, and (4) a bespoke prediction module. When tested against real-world datasets from California of Caltrans Performance Measurement System PeMS, specifically PeMS districts 4 and 8, the CNN-GRUSKIP consistently outperformed established models such as ARIMA, Graph Wave Net, HA, LSTM, STGCN, and APTN. With its potent predictive prowess and adaptive architecture, the CNN-GRUSKIP model stands to redefine ITS applications, especially where nuanced traffic dynamics are in play.

Sparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition of polysemantic neurons into monosemantic features and composing a sparse dictionary of words. However, traditional performance metrics like Mean Squared Error and L0 sparsity ignore the evaluation of the semantic representational power of SAEs -- whether they can acquire interpretable monosemantic features while preserving the semantic relationship of words. For instance, it is not obvious whether a learned sparse feature could distinguish different meanings in one word. In this paper, we propose a suite of evaluations for SAEs to analyze the quality of monosemantic features by focusing on polysemous words. Our findings reveal that SAEs developed to improve the MSE-L0 Pareto frontier may confuse interpretability, which does not necessarily enhance the extraction of monosemantic features. The analysis of SAEs with polysemous words can also figure out the internal mechanism of LLMs; deeper layers and the Attention module contribute to distinguishing polysemy in a word. Our semantics focused evaluation offers new insights into the polysemy and the existing SAE objective and contributes to the development of more practical SAEs.

Deploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy concerns or impractical due to network connection. Recent advancements (MLC, 2023a; Gerganov, 2023) have facilitated the local deployment of LLMs. However, local deployment also presents challenges, particularly in balancing quality (generative performance), latency, and throughput within the hardware constraints of mobile devices. In this paper, we introduce our lightweight, all-in-one automated benchmarking framework that allows users to evaluate LLMs on mobile devices. We provide a comprehensive benchmark of various popular LLMs with different quantization configurations (both weights and activations) across multiple mobile platforms with varying hardware capabilities. Unlike traditional benchmarks that assess full-scale models on high-end GPU clusters, we focus on evaluating resource efficiency (memory and power consumption) and harmful output for compressed models on mobile devices. Our key observations include i) differences in energy efficiency and throughput across mobile platforms; ii) the impact of quantization on memory usage, GPU execution time, and power consumption; and iii) accuracy and performance degradation of quantized models compared to their non-quantized counterparts; and iv) the frequency of hallucinations and toxic content generated by compressed LLMs on mobile devices.

In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models leverage contextual features (e.g., weather) to identify unusual crowd mobility patterns and enhance prediction accuracy. However, the best practice for incorporating contextual features remains unclear due to inconsistent usage of contextual features in different papers. Developing a multifaceted dataset with rich types of contextual features and STCFP scenarios is crucial for establishing a principled context modeling paradigm. Existing open crowd flow datasets lack an adequate range of contextual features, which poses an urgent requirement to build a multifaceted dataset to fill these research gaps. To this end, we create STContext, a multifaceted dataset for developing context-aware STCFP models. Specifically, STContext provides nine spatio-temporal datasets across five STCFP scenarios and includes ten contextual features, including weather, air quality index, holidays, points of interest, road networks, etc. Besides, we propose a unified workflow for incorporating contextual features into deep STCFP methods, with steps including feature transformation, dependency modeling, representation fusion, and training strategies. Through extensive experiments, we have obtained several useful guidelines for effective context modeling and insights for future research. The STContext is open-sourced at https://github.com/Liyue-Chen/STContext.

Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl, and social disconnection, undermining broader social and environmental sustainability objectives. This study investigates the adoption and impact of form-based codes (FBCs), which aim to promote sustainable, compact, and mixed-use urban forms as a solution to these issues. Using Natural Language Processing (NLP) techniques, we analyzed zoning documents from over 2000 U.S. census-designated places to identify linguistic patterns indicative of FBC principles. Our findings reveal widespread adoption of FBCs across the country, with notable variations within regions. FBCs are associated with higher floor-to-area ratios, narrower and more consistent street setbacks, and smaller plots. We also find that places with FBCs have improved walkability, shorter commutes, and a higher share of multi-family housing. Our findings highlight the utility of NLP for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.

However, in the early exploration of multivariate time series forecasting tasks, the Transformer did not demonstrate significant superiority[7]. Nevertheless, Nie et al.[8] soon revealed that a major reason for this issue lies in the single-point token construction method adopted by most approaches (as shown in Figure 1(b1)), which struggles to represent key temporal features such as trends and distributions within a single token. To address this problem, PatchTST was proposed in [8], which significantly improved the performance of the Transformer in multivariate time series forecasting through the use of patch tokens and a channelindependent (CI) forward propagation strategy (as shown in Figure 1(b3)). Subsequently, other patchbased multivariate time series forecasting Transformers, such as Crossformer[9] and TimeXer[10], have also achieved competitive performance. However, according to common assumptions in previous studies, the explicit extraction of crossvariable dependencies is crucial for multivariate time series modeling, as there are often correlations or causal relationships between variables. Research [11] and [12] conducted a more detailed comparison between CI and channel-dependent strategies(CD), and the results revealed that, on almost all deep neural network backbones, including Transformers, the generalization ability of methods based on CI strategy significantly outperforms most CD-based methods. The conclusions of these studies suggest that the CI strategy should become the primary approach for multivariate time series forecasting. However, iTransformer[13], a method that treats each variable sequence as a token and only explicitly extracts cross-variable dependencies, has outperformed PatchTST on many mainstream datasets[10][13][14], achieving SOTA performance.

Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitoring. However, the lack of large-scale, high-quality training samples has become a major bottleneck in advancing phytoplankton tracking. In this paper, we propose a challenging benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation. The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos. To enable accurate real-time observation of phytoplankton, we introduce a multi-object tracking method, Deviation-Corrected Multi-Scale Feature Fusion Tracker(DSFT), which addresses issues such as focus shifts during tracking and the loss of small target information when computing frame-to-frame similarity. Specifically, we introduce an additional feature extractor to predict the residuals of the standard feature extractor's output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor. Extensive experiments on the MPT have demonstrated the validity of the dataset and the superiority of DSFT in tracking phytoplankton, providing an effective solution for phytoplankton monitoring.

Scientists have warned that an underwater volcano off the coast of the northwestern US is likely to blow sometime in 2025. The volcano, called Axial Seamount, is more than 3,600-feet-tall and sits half a mile underwater just 300 miles off the coast of Oregon. Experts made the prediction on December 10 after detecting seafloor swelling around Axial that mimicked a level seen immediately before an eruption in 2015. Seismic activity has also increased, with hundreds of earthquakes generated around the volcano per day and earthquake swarms greater than 500 per day. 'Based on the current trends, and the assumption that Axial will be primed to erupt when it reaches the 2015 inflation threshold, our current eruption forecast window is between now (July 2024) and the end of 2025,' researchers said in the new study.

Language agents [1-4] are AI agents [5] that integrate LLMs [6-8] as core components. LLMs excel at zero-shot generalization [9, 10], providing a notable advantage over traditional AI agents, such as those based on handcrafted rules or reinforcement learning, which often struggle to generalize to new environments [11]. While LLMs can exhibit flawed reasoning and logic when used in isolation [12-14], constructing a language agent by grounding LLMs in an environment with observational feedback can mitigate these issues. Early work on language agents used LLMs to directly output actions in the external environment [15-17], while more recently, language agents have been augmented with internal reasoning [18, 19] and planning [20, 21] procedures, as well as long-term memory storage [22, 23]. An emergent research challenge is to pose a theoretical description of the learning problem solved by language agents [4, 24] and to develop efficient methods to optimize the components of a language agent [24-26]. Here, we define common language agent tasks as language decision processes (LDPs) and frame language agents as stochastic computation graphs [27] that may be trained to solve LDPs. We show that pre-existing agents [18, 19, 21] can be implemented within our stochastic computation graph framework and introduce a simple and extensible software package named LDP that enables modular interchange of environments, agents, and optimizers, simplifying experimentation across a variety of settings. These authors jointly supervise technical work at FutureHouse.

Whether a large and fuzzy social media sensation or deep-sea slug slunking around the ocean's Midnight Zone, there are still so many exciting animals on Earth just waiting for their close-up. In that spirit, here are the 11 of the most exciting animal stories that Popular Science covered this year. A wildlife filmmaker and biology doctoral student took what could be the first picture of a newborn great white shark. Filmmaker Carlos Gauna and University of California, Riverside biology doctoral student Phillip Sternes were looking for sharks near Santa Barbara on California's central coast. Most great whites are gray on top with white bellies, but Gauana's drone camera showed a roughly 5-foot-long shark pup that had more white on its body than normal.