Miracle-dz/UniFAR-FTUs huggingface dataset:
GPT-4V Takes the Wheel: Evaluating Promise and Challenges for Pedestrian Behavior Prediction

Existing pedestrian behavior prediction methods rely primarily on deep neural networks that utilize features extracted from video frame sequences. Although these vision-based models have shown promising results, they face limitations in effectively capturing and utilizing the dynamic spatio-temporal interactions between the target pedestrian and its surrounding traffic elements, crucial for accurate reasoning. Additionally, training these models requires manually annotating domain-specific datasets, a process that is expensive, time-consuming, and difficult to generalize to new environments and scenarios. The recent emergence of Large Multimodal Models (LMMs) offers potential solutions to these limitations due to their superior visual understanding and causal reasoning capabilities, which can be harnessed through semi-supervised training. GPT-4V(ision), the latest iteration of the state-of-the-art Large-Language Model GPTs, now incorporates vision input capabilities. This report provides a comprehensive evaluation of the potential of GPT-4V for pedestrian behavior prediction in autonomous driving using publicly available datasets: JAAD, PIE, and WiDEVIEW. Quantitative and qualitative evaluations demonstrate GPT-4V(ision)’s promise in zero-shot pedestrian behavior prediction and driving scene understaning ability for autonomous driving. However, it still falls short of the state-of-the-art traditional domain-specific models. Challenges include difficulties in handling small pedestrians and vehicles in motion. These limitations highlight the need for further research and development in this area.
Nuclear Science and

: The Compartment Model allows modeling the system flow to zones visually, in which each zone is characterized by a combination of basic mixing compartments. The localization of the flow zones, as well as the calculation of the volume zones and exchange rate between zones, was done based on the velocity field determined from the numerical simulation model. This study presents the results of applying the CM method to analyze the 2D basic non-reactive tank with dimensions of 100 cm x 100 cm x 10 cm and an inlet flow of about 3 - 6 L/min. The results show that the obtained CM model has 3 main flow zones including the convection zone, circulation zone and slow flow zone. The results of comparing the tracer response curves from the CM model and experiment with the root-mean-square error below 0,11 allow confirming the established CM model.
Bi-level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty

To realize the safe, economical and low-carbon operation of the pelagic island microgrid group, this paper develops a bi-level energy management framework in a joint energy-reserve market where the microgrid group (MG) operator and renewable and storage aggregators (RSA) are independent stakeholders with their own interests. In the upper level, MG operator determines the optimal transaction prices with aggregators to minimize MG operation cost while ensuring all safety constraints are satisfied under uncertainty. In the lower level, aggregators utilize vessels for batteries swapping and transmission among islands in addition to energy arbitrage by participating in energy and reserve market to maximize their own revenue. An upper bound tightening iterative algorithm is proposed for the formulated problem with nonlinear terms and integer variables in the lower level to improve the efficiency and reduce the gap between upper bound and lower bound compared with existing reformulation and decomposition algorithm. Case studies validate the effectiveness of the proposed approach and demonstrate its advantage of the proposed approach in terms of optimality and computation efficiency, compared with other methods.
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields

In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To correctly exploit the provided information by each sensor in a single common frame, it is essential for these sensors to be accurately calibrated. In this paper, we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration. By designing a partitioning approach based on the visible part of the scene for each sensor, we formulate the calibration problem using only the overlapping areas. This strategy results in a more robust and accurate calibration that is less prone to failure. We demonstrate that our approach works on outdoor urban scenes by validating it on multiple established driving datasets. Results show that our method is able to get better accuracy and robustness compared to existing methods.
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition

Large-scale visual-language pre-trained models have achieved significant success in various video tasks. However, most existing methods follow an"adapt then align"paradigm, which adapts pre-trained image encoders to model video-level representations and utilizes one-hot or text embedding of the action labels for supervision. This paradigm overlooks the challenge of mapping from static images to complicated activity concepts. In this paper, we propose a novel"Align before Adapt"(ALT) paradigm. Prior to adapting to video representation learning, we exploit the entity-to-region alignments for each frame. The alignments are fulfilled by matching the region-aware image embeddings to an offline-constructed text corpus. With the aligned entities, we feed their text embeddings to a transformer-based video adapter as the queries, which can help extract the semantics of the most important entities from a video to a vector. This paradigm reuses the visual-language alignment of VLP during adaptation and tries to explain an action by the underlying entities. This helps understand actions by bridging the gap with complex activity semantics, particularly when facing unfamiliar or unseen categories. ALT demonstrates competitive performance while maintaining remarkably low computational costs. In fully supervised experiments, it achieves 88.1% top-1 accuracy on Kinetics-400 with only 4947 GFLOPs. Moreover, ALT outperforms the previous state-of-the-art methods in both zero-shot and few-shot experiments, emphasizing its superior generalizability across various learning scenarios.
RECONSTRUCTION OF AN APPARATUS OF NEOSTRACHANOGNATHUS TAHOENSIS FROM ORITATE, JAPAN AND SPECIES OF NEOSTRACHANOGNATHUS FROM OMAN

Abstract: The apparatus of an Early Triassic conodont Neostrachanognathus tahoensis Koike, 1998 from Oritate, Kumamoto Prefecture, Japan, and a species of Neostrachanognathus from Oman were reconstructed. On the basis of five natural assemblages from the Oritate area, the three‐dimensional apparatus model of N. tahoensis is interpreted as bilaterally symmetrical and composed of 14 elements consisting of pairs of P1, P2, P3, S1, S2, S3, and S4 elements. The P1 and P2 elements are coniform elements, the P3 elements are digyrate forms, and the S elements are bipennate ramiforms. The S elements are arranged rostrally in the apparatus and the pairs of the P1, P2, and P3 elements are subvertically arranged caudally and ventrally to the S array. One of the natural assemblages was formed by rostrocaudal collapse of the apparatus on the sea floor, whereas the other assemblages indicate that conodont animals came to rest nearly parallel with the substrate prior to burial. A collection of isolated elements from Jabal Safra, Oman, includes a second species of Neostrachanognathus with a comparable apparatus.
Tascade: Hardware Support for Atomic-free, Asynchronous and Efficient Reduction Trees

As system parallelism at chip- and server-level increases, challenges that arose with network-level systems a decade ago, are now being encountered with these massively parallel systems that have become an important workhorse for Machine Learning workloads as well as Graph and Sparse workloads. To tackle the communication bottlenecks, recent works have introduced task-based parallelization schemes to accelerate graph search and sparse data-structure traversal, where some solutions scale up to thousands of processing units (PUs) on a single chip. However, existing communication schemes do not scale to larger than thousands of processing tiles. To address these challenges we propose Tascade, a system that offers hardware-supported, efficient and balanced reduction trees to reduce communication overheads in task-based parallelization schemes and scales up to a million PUs. Tascade achieves this by implementing an execution model utilizing proxy regions and cascading updates, along with a supporting hardware design that enables the execution of the reduction tree at the chip level. The Tascade approach reduces overall communication and improves load balancing. We evaluate six applications and four datasets to provide a detailed analysis of Tascade's performance, power, and traffic-reduction gains over prior work. Our parallelization of Breadth-First-Search with RMAT-26 across a million PUs, the largest of the literature, reaches 5305 GTEPS.
Functional Diffusion

We propose a new class of generative diffusion models, called functional diffusion. In contrast to previous work, functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images, videos, audio, 3D shapes, deformations, \etc, can be handled by the same framework with minimal changes. In addition, functional diffusion is especially suited for irregular data or data defined in non-standard domains. In our work, we derive the necessary foundations for functional diffusion and propose a first implementation based on the transformer architecture. We show generative results on complicated signed distance functions and deformation functions defined on 3D surfaces.
The Battleship Approach to the Low Resource Entity Matching Problem

Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity. Recent advances in deep learning methods, using pre-trained language models, were proposed for resolving entity matching. Although demonstrating unprecedented results, these solutions suffer from a major drawback as they require large amounts of labeled data for training, and, as such, are inadequate to be applied to low resource entity matching problems. To overcome the challenge of obtaining sufficient labeled data we offer a new active learning approach, focusing on a selection mechanism that exploits unique properties of entity matching. We argue that a distributed representation of a tuple pair indicates its informativeness when considered among other pairs. This is used consequently in our approach that iteratively utilizes space-aware considerations. Bringing it all together, we treat the low resource entity matching problem as a Battleship game, hunting indicative samples, focusing on positive ones, through awareness of the latent space along with careful planning of next sampling iterations. An extensive experimental analysis shows that the proposed algorithm outperforms state-of-the-art active learning solutions to low resource entity matching, and although using less samples, can be as successful as state-of-the-art fully trained known algorithms.
Function-constrained Program Synthesis

This work introduces (1) a technique that allows large language models (LLMs) to leverage user-provided code when solving programming tasks and (2) a method to iteratively generate modular sub-functions that can aid future code generation attempts when the initial code generated by the LLM is inadequate. Generating computer programs in general-purpose programming languages like Python poses a challenge for LLMs when instructed to use code provided in the prompt. Code-specific LLMs (e.g., GitHub Copilot, CodeLlama2) can generate code completions in real-time by drawing on all code available in a development environment. However, restricting code-specific LLMs to use only in-context code is not straightforward, as the model is not explicitly instructed to use the user-provided code and users cannot highlight precisely which snippets of code the model should incorporate into its context. Moreover, current systems lack effective recovery methods, forcing users to iteratively re-prompt the model with modified prompts until a sufficient solution is reached. Our method differs from traditional LLM-powered code-generation by constraining code-generation to an explicit function set and enabling recovery from failed attempts through automatically generated sub-functions. When the LLM cannot produce working code, we generate modular sub-functions to aid subsequent attempts at generating functional code. A by-product of our method is a library of reusable sub-functions that can solve related tasks, imitating a software team where efficiency scales with experience. We also introduce a new"half-shot"evaluation paradigm that provides tighter estimates of LLMs' coding abilities compared to traditional zero-shot evaluation. Our proposed evaluation method encourages models to output solutions in a structured format, decreasing syntax errors that can be mistaken for poor coding ability.
Evolutionary City: Towards a Flexible, Agile and Symbiotic System

Urban growth sometimes leads to rigid infrastructure that struggles to adapt to changing demand. This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand. It identifies the limitations arising from the complexity and inflexibility of existing urban systems. A framework is presented for enhancing the city's adaptability perception through advanced sensing technologies, conducting parallel simulation via graph-based techniques, and facilitating autonomous decision-making across domains through decentralized and autonomous organization and operation. Notably, a symbiotic mechanism is employed to implement these technologies practically, thereby making urban management more agile and responsive. In the case study, we explore how this approach can optimize traffic flow by adjusting lane allocations. This case not only enhances traffic efficiency but also reduces emissions. The proposed evolutionary city offers a new perspective on sustainable urban development, highliting the importance of integrated intelligence within urban systems.
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence

Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, including pruning and quantization, can reduce the size of the network while preserving its accuracy. Several recent studies have addressed the relationship between model compression and adversarial robustness, while some experiments have reported contradictory results. This work summarizes available evidence and discusses possible explanations for the observed effects.
A Choice-Driven Service Network Design and Pricing Including Heterogeneous Behaviors

The design and pricing of services are two of the most important decisions faced by any intermodal transport operator. The key success factor lies in the ability of meeting the needs of the shippers. Therefore, making full use of the available information about the demand helps to come up with good design and pricing decisions. With this in mind, we propose a Choice-Driven approach, incorporating advanced choice models directly into a Service Network Design and Pricing problem. We evaluate this approach considering three different mode choice models: one deterministic with 4 attributes (cost, time, frequency and accessibility); and two stochastic also accounting for unobserved attributes and shippers heterogeneity respectively. To reduce the computational time for the stochastic instances, we propose a predetermination heuristic. These models are compared to a benchmark, where shippers are solely cost-minimizers. Results show that the operator profits can be significantly improved, even with the deterministic version. The two stochastic versions further increase the realized profits, but considering heterogeneity allows a better estimation of the demand.
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism

Explicit Caption Editing (ECE) -- refining reference image captions through a sequence of explicit edit operations (e.g., KEEP, DETELE) -- has raised significant attention due to its explainable and human-like nature. After training with carefully designed reference and ground-truth caption pairs, state-of-the-art ECE models exhibit limited generalization ability beyond the original training data distribution, i.e., they are tailored to refine content details only in in-domain samples but fail to correct errors in out-of-domain samples. To this end, we propose a new Diffusion-based Explicit Caption editing method: DECap. Specifically, we reformulate the ECE task as a denoising process under the diffusion mechanism, and introduce innovative edit-based noising and denoising processes. Thanks to this design, the noising process can help to eliminate the need for meticulous paired data selection by directly introducing word-level noises for training, learning diverse distribution over input reference caption. The denoising process involves the explicit predictions of edit operations and corresponding content words, refining reference captions through iterative step-wise editing. To further efficiently implement our diffusion process and improve the inference speed, DECap discards the prevalent multi-stage design and directly generates edit operations and content words simultaneously. Extensive ablations have demonstrated the strong generalization ability of DECap in various scenarios. More interestingly, it even shows great potential in improving the quality and controllability of caption generation.
On Young diagrams of maximum dimension

We study the problem of finding Young diagrams of maximum dimension, i. e. those with the largest number of Young tableaux of their shapes. Consider a class of Young diagrams that differ from a symmetric diagram by no more than one box $(i,j)$ in each row and column. It is proven that when moving boxes $(i,j), i>j$ to symmetric positions $(j,i)$, the original diagram is transformed into another diagram of the same size, but with a greater or equal dimension. A conjecture is formulated that generalizes the above fact to the case of arbitrary Young diagrams. Based on this conjecture, we developed an algorithm applied to obtain new Young diagrams of sizes up to 42 thousand boxes with large and maximum dimensions.
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision. The code is available at https://github.com/Justherozen/FreeAL .
Replay across Experiments: A Natural Extension of Off-Policy RL

Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to extend the use of replays across multiple experiments, minimally adapting the RL workflow for sizeable improvements in controller performance and research iteration times. At its core, Replay Across Experiments (RaE) involves reusing experience from previous experiments to improve exploration and bootstrap learning while reducing required changes to a minimum in comparison to prior work. We empirically show benefits across a number of RL algorithms and challenging control domains spanning both locomotion and manipulation, including hard exploration tasks from egocentric vision. Through comprehensive ablations, we demonstrate robustness to the quality and amount of data available and various hyperparameter choices. Finally, we discuss how our approach can be applied more broadly across research life cycles and can increase resilience by reloading data across random seeds or hyperparameter variations.
Exact Matrix Completion via High-Rank Matrices in Sum-of-Squares Relaxations

We study exact matrix completion from partially available data with hidden connectivity patterns. Exact matrix completion was shown to be possible recently by Cosse and Demanet in 2021 with Lasserre's relaxation using the trace of the variable matrix as the objective function with given data structured in a chain format. In this study, we introduce a structure for the objective function so that the resulting sum-of-squares (SOS) relaxation, the dual of Lasserre's SDP relaxation, produces a rank-($N$-1) solution, where $N$ denotes the size of variable matrix in the SOS relaxation. Specifically, the arrowhead structure is employed for the coefficient matrix of the objective function. We show that a matrix can be exactly completed through the SOS relaxation when the connectivity of given data is not explicitly displayed or follows a chain format. The theoretical exactness is proved using the rank of the Gram matrix for the SOS relaxation. We also present numerical algorithms designed to find the coefficient matrix in the SOS relaxation. Numerical experiments illustrate the validity of the proposed algorithm.