Miracle-dz/UniFAR-FTUs huggingface dataset:
Bicycling preferences and behavior in Salt Lake City

Improving our understanding of cycling behaviors in urban areas is an important step in producing a more sustainable transportation system. Based on a stated preference survey in Salt Lake City, Utah, this paper studies the influence of attitudes on bicycling behavior. A travel preference factor analysis indicates four attitudinal factors concerning bicycling: safety, direct benefits, comfort, and timesaving. The decision to cycle is positively correlated with the timesaving and convenience factors, whereas preferences on travel comfort level negatively affected bicycling frequencies. Besides attitude factors, bicycling level is the highest among groups with higher education, single and living without a family, do not have access to a car, and who have a positive attitude on bicycling. We also apply a route optimization method to further analyze bicyclists’ route choice behavior and preferences toward transportation link level characteristics (e.g., bike lane, slope, traffic speed). The results indicated an influential effect of separated bike lanes. These findings indicate that attitudes, bike lanes, and other demographic factors have a strong impact on bicycling behaviors.
Three-dimensional DDA and DLSM coupled approach for rock cutting and rock penetration

AbstractRock cutting and rock penetration are typical problems in civil, mining, petroleum, and geothermal engineering disciplines. They involve dynamic fracturing and fragmentation of rock, high-speed movements of a cutter/impactor, and complex dynamic contacts between the cutter/impactor and the rock. In this study a new three-dimensional (3D) coupled approach is developed to address these problems. The distinct lattice spring model (DLSM) is used to simulate the dynamic fracturing process of the rock, and the discontinuous deformation analysis (DDA) is adopted to model the high-speed motion of the cutter/impactor. An explicit-implicit coupling scheme is developed to bridge DLSM and DDA. Moreover, to take account of interaction between DLSM and DDA, a 3D simplex sphere-to-block contact method is introduced. Finally, a number of numerical examples are conducted to verify the implementation of the coupled approach and its ability to model rock cutting and rock penetration problems.
BAFLineDP: Code Bilinear Attention Fusion Framework for Line-Level Defect Prediction

Software defect prediction aims to identify defect-prone code, aiding developers in optimizing testing resource allocation. Most defect prediction approaches primarily focus on coarse-grained, file-level defect prediction, which fails to provide developers with the precision required to locate defective code. Recently, some researchers have proposed fine-grained, line-level defect prediction methods. However, most of these approaches lack an in-depth consideration of the contextual semantics of code lines and neglect the local interaction information among code lines. To address the above issues, this paper presents a line-level defect prediction method grounded in a code bilinear attention fusion framework (BAFLineDP). This method discerns defective code files and lines by integrating source code line semantics, line-level context, and local interaction information between code lines and line-level context. Through an extensive analysis involving within- and cross-project defect prediction across 9 distinct projects encompassing 32 releases, our results demonstrate that BAFLineDP outperforms current advanced file-level and line-level defect prediction approaches.
Non-Radial Free Geodesics. II. In Spatially Curved FLRW Spacetime

This paper presents an in-depth exploration of timelike free geodesics in spatially curved Friedmann-Lema\^itre-Robertson-Walker (FLRW) spacetime. A unified approach for these geodesics encompassing both radial and non-radial trajectories across Euclidean, spherical, and hyperbolic geometries is employed. Using the symmetry properties of the system, two constants of motion related to this dynamical system are derived. This treatment facilitates the explicit computation of radial and angular peculiar velocities, along with the evolution of the comoving radial distance and angle over time. The study introduces three distinct methods for characterizing geodesic solutions, further delving into the flatness limit, the null geodesic limit, the radial geodesic limit, the comoving geodesic limit, and the circular orbits. Additionally, we analyze Killing vectors, reflecting the symmetries inherent in the dynamical system. This study provides a more profound understanding of the behavior of free particles as observed from a comoving reference frame.
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue

Tuning pretrained language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling other language generation tasks, ignoring the role disparities between two speakers and the multi-round interactive process that dialogues ought to be. Such a manner leads to unsatisfactory chat consistency of the built agent. In this work, we emphasize the interactive, communicative nature of dialogue and argue that it is more feasible to model the speaker roles of agent and user separately, enabling the agent to adhere to its role consistently. We propose an efficient Multi-round Interactive Dialogue Tuning (Midi-Tuning) framework. It models the agent and user individually with two adapters built upon large language models, where they utilize utterances round by round in alternating order and are tuned via a round-level memory caching mechanism. Extensive experiments demonstrate that, our framework performs superior to traditional fine-tuning and harbors the tremendous potential for improving dialogue consistency.
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach

The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.
Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples

The frequent occurrence of cyber-attacks has made webshell attacks and defense gradually become a research hotspot in the field of network security. However, the lack of publicly available benchmark datasets and the over-reliance on manually defined rules for webshell escape sample generation have slowed down the progress of research related to webshell escape sample generation strategies and artificial intelligence-based webshell detection algorithms. To address the drawbacks of weak webshell sample escape capabilities, the lack of webshell datasets with complex malicious features, and to promote the development of webshell detection technology, we propose the Hybrid Prompt algorithm for webshell escape sample generation with the help of large language models. As a prompt algorithm specifically developed for webshell sample generation, the Hybrid Prompt algorithm not only combines various prompt ideas including Chain of Thought, Tree of Thought, but also incorporates various components such as webshell hierarchical module and few-shot example to facilitate the LLM in learning and reasoning webshell escape strategies. Experimental results show that the Hybrid Prompt algorithm can work with multiple LLMs with excellent code reasoning ability to generate high-quality webshell samples with high Escape Rate (88.61% with GPT-4 model on VIRUSTOTAL detection engine) and Survival Rate (54.98% with GPT-4 model).
Quantum reservoir probing of quantum phase transitions

Quantum phase transitions are highly remarkable phenomena manifesting in quantum many-body systems. However, their precise identifications in equilibrium systems pose significant theoretical and experimental challenges. Thus far, nonequilibrium detection protocols utilizing global quantum quenches have been proposed, yet the real-time signatures of these transitions are imprinted in intricate spatiotemporal correlations, compromising the simplicity and versatility of the protocols. Here, we propose a framework for dynamical detection of quantum phase transitions based on local quantum quenches. While the resultant dynamics are influenced by both the local quench operation and the intrinsic dynamics of the quantum system, the effects of the former are exclusively extracted through the cutting-edge framework called quantum reservoir probing (QRP). We illustrate that the impacts of the local quenches vary across different quantum phases, and are subdued by intrinsic fluctuations amplified in proximity to quantum critical points. Consequently, the QRP can detect quantum phase transitions in the paradigmatic integrable and nonintegrable quantum systems, all while utilizing single-site operators. Furthermore, we also show that topological quantum phase transitions can be detected using the identical framework. The broad applicability of the QRP, along with its experimental feasibility and design flexibility, highlights its universal effectiveness in identifying various quantum phase transitions.
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection

Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes utilize safety-agnostic metrics to argue sufficient detection performance. Hence, it is imperative to leverage supplementary domain knowledge to accentuate safety-critical misdetections during evaluation tasks. To tackle the underspecification, this paper introduces a novel credibility metric, called c-flow, for pedestrian bounding boxes. To this end, c-flow relies on a complementary optical flow signal from image sequences and enhances the analyses of safety-critical misdetections without requiring additional labels. We implement and evaluate c-flow with a state-of-the-art pedestrian detector on a large AD dataset. Our analysis demonstrates that c-flow allows developers to identify safety-critical misdetections.
Second-Order Transport Coefficients in Neutron Star Mergers

In neutron stars, flavor-changing weak interactions determine the equilibrium fraction of protons over neutrons. In binary neutron-star mergers, violent changes in density modify this equilibrium value at timescales of milliseconds, comparable to those required for weak interactions to take place. As a result, the fraction of protons evolves out of phase with the density oscillations, giving rise to irreversible processes. The corresponding shift in pressure leads to dissipative work that can be modeled as an effective bulk-viscous correction. In this work, we derive the relevant equations of motion of Israel-Stewart hydrodynamics within this context. Using a toy model, we compute the second-order transport coefficients. Finally, we comment on the use of a realistic equation of state. Our results are expected to be useful for the study of viscous effects in numerical simulations of binary mergers.
The effects of interparticle cohesion on the collapse of granular columns

The presence of interparticle cohesion can drastically change the behavior of granular materials. For instance, powders are challenging to handle, and one can make a sandcastle using wet grains. In this study, we report experimental results for columns of model cohesive grains collapsing under their own weight in air and spreading on a rough horizontal surface. The effects of two different sources of interparticle cohesion on two collapse geometries are compared and rationalized in a common framework. Grains are made cohesive by adding a small amount of water, such that they are in the pendular state, or by applying a polymer coating. The effects of cohesion are reported for a cylindrical column that spreads unconfined axisymmetrically and a confined rectangular column that flows in a single direction. A dimensionless number, comparing macroscopic cohesive strength to particle weight, is shown to capture the effects of cohesion on the final morphology. To this end, a characterization of the cohesive strength of the granular materials is obtained, independent of the physical source of cohesion at the particle scale. Such a framework allows for a common description of cohesive granular materials with different sources of cohesion.
Scaling Intelligent Agents in Combat Simulations for Wargaming

Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate our research and development in artificial intelligence (AI) for wargaming. More importantly, leveraging machine learning for intelligent combat behavior development will be key to one day achieving superhuman performance in this domain--elevating the quality and accelerating the speed of our decisions in future wars. Although deep reinforcement learning (RL) continues to show promising results in intelligent agent behavior development in games, it has yet to perform at or above the human level in the long-horizon, complex tasks typically found in combat modeling and simulation. Capitalizing on the proven potential of RL and recent successes of hierarchical reinforcement learning (HRL), our research is investigating and extending the use of HRL to create intelligent agents capable of performing effectively in these large and complex simulation environments. Our ultimate goal is to develop an agent capable of superhuman performance that could then serve as an AI advisor to military planners and decision-makers. This papers covers our ongoing approach and the first three of our five research areas aimed at managing the exponential growth of computations that have thus far limited the use of AI in combat simulations: (1) developing an HRL training framework and agent architecture for combat units; (2) developing a multi-model framework for agent decision-making; (3) developing dimension-invariant observation abstractions of the state space to manage the exponential growth of computations; (4) developing an intrinsic rewards engine to enable long-term planning; and (5) implementing this framework into a higher-fidelity combat simulation.
From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration

The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

Causal inference from observational data has recently found many applications in machine learning. While sound and complete algorithms exist to compute causal effects, many of these algorithms require explicit access to conditional likelihoods over the observational distribution, which is difficult to estimate in the high-dimensional regime, such as with images. To alleviate this issue, researchers have approached the problem by simulating causal relations with neural models and obtained impressive results. However, none of these existing approaches can be applied to generic scenarios such as causal graphs on image data with latent confounders, or obtain conditional interventional samples. In this paper, we show that any identifiable causal effect given an arbitrary causal graph can be computed through push-forward computations of conditional generative models. Based on this result, we devise a diffusion-based approach to sample from any (conditional) interventional distribution on image data. To showcase our algorithm's performance, we conduct experiments on a Colored MNIST dataset having both the treatment ($X$) and the target variables ($Y$) as images and obtain interventional samples from $P(y|do(x))$. As an application of our algorithm, we evaluate two large conditional generative models that are pre-trained on the CelebA dataset by analyzing the strength of spurious correlations and the level of disentanglement they achieve.
Policy Improvement using Language Feedback Models

We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following. To train LFMs, we obtain feedback from Large Language Models (LLMs) on visual trajectories verbalized to language descriptions. First, by using LFMs to identify desirable behaviour to imitate, we improve in task-completion rate over strong behavioural cloning baselines on three distinct language grounding environments (Touchdown, ScienceWorld, and ALFWorld). Second, LFMs outperform using LLMs as experts to directly predict actions, when controlling for the number of LLM output tokens. Third, LFMs generalize to unseen environments, improving task-completion rate by 3.5-12.0% through one round of adaptation. Finally, LFM can be modified to provide human-interpretable feedback without performance loss, allowing human verification of desirable behaviour for imitation learning.
Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study

Orthogonal matrices play an important role in probability and statistics, particularly in high-dimensional statistical models. Parameterizing these models using orthogonal matrices facilitates dimension reduction and parameter identification. However, establishing the theoretical validity of statistical inference in these models from a frequentist perspective is challenging, leading to a preference for Bayesian approaches because of their ability to offer consistent uncertainty quantification. Markov chain Monte Carlo methods are commonly used for numerical approximation of posterior distributions, and sampling on the Stiefel manifold, which comprises orthogonal matrices, poses significant difficulties. While various strategies have been proposed for this purpose, gradient-based Markov chain Monte Carlo with parameterizations is the most efficient. However, a comprehensive comparison of these parameterizations is lacking in the existing literature. This study aims to address this gap by evaluating numerical efficiency of the four alternative parameterizations of orthogonal matrices under equivalent conditions. The evaluation was conducted for four problems. The results suggest that polar expansion parameterization is the most efficient, particularly for the high-dimensional and complex problems. However, all parameterizations exhibit limitations in significantly high-dimensional or difficult tasks, emphasizing the need for further advancements in sampling methods for orthogonal matrices.
Top-K ranking with a monotone adversary

In this paper, we address the top-$K$ ranking problem with a monotone adversary. We consider the scenario where a comparison graph is randomly generated and the adversary is allowed to add arbitrary edges. The statistician's goal is then to accurately identify the top-$K$ preferred items based on pairwise comparisons derived from this semi-random comparison graph. The main contribution of this paper is to develop a weighted maximum likelihood estimator (MLE) that achieves near-optimal sample complexity, up to a $\log^2(n)$ factor, where n denotes the number of items under comparison. This is made possible through a combination of analytical and algorithmic innovations. On the analytical front, we provide a refined $\ell_\infty$ error analysis of the weighted MLE that is more explicit and tighter than existing analyses. It relates the $\ell_\infty$ error with the spectral properties of the weighted comparison graph. Motivated by this, our algorithmic innovation involves the development of an SDP-based approach to reweight the semi-random graph and meet specified spectral properties. Additionally, we propose a first-order method based on the Matrix Multiplicative Weight Update (MMWU) framework. This method efficiently solves the resulting SDP in nearly-linear time relative to the size of the semi-random comparison graph.
EmoWear: Exploring Emotional Teasers for Voice Message Interaction on Smartwatches

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored"Emotional Teasers"-pre-retrieval cues offering a glimpse into an awaiting message's emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders' choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.