Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjacent-category models for ordinal time series and their application to climate-dependent spruce budworm defoliation dynamics

This work proposes an adjacent-category autoregressive model for time series of ordinal variables. We apply this model to dendrochronological records to study the effect of climate on the intensity of spruce budworm defoliation during outbreaks in two sites in eastern Canada. The model's parameters are estimated using the maximum likelihood approach. We show that this estimator is consistent and asymptotically Gaussian distributed. We also propose a Portemanteau test for goodness-of-fit. Our study shows that the seasonal ranges of maximum daily temperatures in the spring and summer have a significant quadratic effect on defoliation. The study reveals that for both regions, a greater range of summer daily maximum temperatures is associated with lower levels of defoliation up to a threshold estimated at 22.7C (CI of 0-39.7C at 95%) in T\'emiscamingue and 21.8C (CI of 0-54.2C at 95%) for Matawinie. For Matawinie, a greater range in spring daily maximum temperatures increased defoliation, up to a threshold of 32.5C (CI of 0-80.0C). We also present a statistical test to compare the autoregressive parameter values between different fits of the model, which allows us to detect changes in the defoliation dynamics between the study sites in terms of their respective autoregression structures.
Adaptive REST API Testing with Reinforcement Learning

Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from a potentially large set of parameters, and sampling values from the virtually infinite parameter input space. Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally (i.e., not considering their importance or complexity) and lacking prioritization strategies. Furthermore, these tools struggle when response schemas are absent in the specification or exhibit variants. To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize operations and parameters during exploration. Our approach dynamically analyzes request and response data to inform dependent parameters and adopts a sampling-based strategy for efficient processing of dynamic API feedback. We evaluated our technique on ten RESTful services, comparing it against state-of-the-art REST testing tools with respect to code coverage achieved, requests generated, operations covered, and service failures triggered. Additionally, we performed an ablation study on prioritization, dynamic feedback analysis, and sampling to assess their individual effects. Our findings demonstrate that our approach outperforms existing REST API testing tools in terms of effectiveness, efficiency, and fault-finding ability.
Class-Incremental Grouping Network for Continual Audio-Visual Learning

Continual learning is a challenging problem in which models need to be trained on non-stationary data across sequential tasks for class-incremental learning. While previous methods have focused on using either regularization or rehearsal-based frameworks to alleviate catastrophic forgetting in image classification, they are limited to a single modality and cannot learn compact class-aware cross-modal representations for continual audio-visual learning. To address this gap, we propose a novel class-incremental grouping network (CIGN) that can learn category-wise semantic features to achieve continual audio-visual learning. Our CIGN leverages learnable audio-visual class tokens and audio-visual grouping to continually aggregate class-aware features. Additionally, it utilizes class tokens distillation and continual grouping to prevent forgetting parameters learned from previous tasks, thereby improving the model’s ability to capture discriminative audiovisual categories. We conduct extensive experiments on VGG-Sound-Instruments, VGGSound-100, and VGG-Sound Sources benchmarks. Our experimental results demonstrate that the CIGN achieves state-of-the-art audio-visual class-incremental learning performance. Code is available at https://github.com/stoneMo/CIGN.
Nontrivial upper bound for chemical distance on the planar random cluster model

We extend the upper bounds derived for the horizontal and radial chemical distance for 2d Bernoulli percolation in [DHS21, SR20] to the planar random cluster model with cluster weight $1 \le q \le 4$. Along the way, we provide a complete proof of the strong arm separation lemma for the random cluster model.
Reconciling Canopy Interception Parameterization and Rainfall Forcing Frequency in the Community Land Model for Simulating Evapotranspiration of Rainforests and Oil Palm Plantations in Indonesia

By mediating evapotranspiration processes, plant canopies play an important role in the terrestrial water cycle and regional climate. Substantial uncertainties exist in modeling canopy water interception and related hydrological processes due to rainfall forcing frequency selection and varying canopy traits. Here we design a new time interpolation method “zero” to better represent convective‐type precipitation in tropical regions. We also implement and recalibrate plant functional type‐specific interception parameters for rainforests and oil palm plantations, where oil palms express higher water interception capacity than forests, using the Community Land Model (CLM) versions 4.5 and 5.0 with CLM‐Palm embedded. Reconciling the interception scheme with realistic precipitation forcing produces more accurate canopy evaporation and transpiration for both plant functional types, which in turn improves simulated evapotranspiration and energy partitioning when benchmarked against observations from our study sites in Indonesia and an extensive literature review. Regional simulations for Sumatra and Kalimantan show that industrial oil palm plantations have 18–27% higher transpiration and 15–20% higher evapotranspiration than forests on an annual regional average basis across different ages or successional stages, even though the forests experience higher average precipitation according to reanalysis data. Our land‐only modeling results indicate that current oil palm plantations in Sumatra and Kalimantan use 15–20% more water (mean 220 mm or 20 Gt) per year compared to lowland rainforests of the same extent. The extra water use by oil palm reduces soil moisture and runoff that could affect ecosystem services such as productivity of staple crops and availability of drinking water in rural areas.
Mutation-based Fault Localization of Deep Neural Networks

Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in ton-1 position.
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing

We propose an optimal iterative scheme for federated transfer learning, where a central planner has access to datasets ${\cal D}_1,\dots,{\cal D}_N$ for the same learning model $f_{\theta}$. Our objective is to minimize the cumulative deviation of the generated parameters $\{\theta_i(t)\}_{t=0}^T$ across all $T$ iterations from the specialized parameters $\theta^\star_{1},\ldots,\theta^\star_N$ obtained for each dataset, while respecting the loss function for the model $f_{\theta(T)}$ produced by the algorithm upon halting. We only allow for continual communication between each of the specialized models (nodes/agents) and the central planner (server), at each iteration (round). For the case where the model $f_{\theta}$ is a finite-rank kernel regression, we derive explicit updates for the regret-optimal algorithm. By leveraging symmetries within the regret-optimal algorithm, we further develop a nearly regret-optimal heuristic that runs with $\mathcal{O}(Np^2)$ fewer elementary operations, where $p$ is the dimension of the parameter space. Additionally, we investigate the adversarial robustness of the regret-optimal algorithm showing that an adversary which perturbs $q$ training pairs by at-most $\varepsilon>0$, across all training sets, cannot reduce the regret-optimal algorithm's regret by more than $\mathcal{O}(\varepsilon q \bar{N}^{1/2})$, where $\bar{N}$ is the aggregate number of training pairs. To validate our theoretical findings, we conduct numerical experiments in the context of American option pricing, utilizing a randomly generated finite-rank kernel.
From Internet of Things to Internet of Data Apps

We introduce the Internet of Data Apps (IoDA), representing the next natural progression of the Internet, Big Data, AI, and the Internet of Things. Despite advancements in these fields, the full potential of universal data access - the capability to seamlessly consume and contribute data via data applications - remains stifled by organizational and technological silos. To address these constraints, we propose the designs of an IoDA layer borrowing inspirations from the standard Internet protocols. This layer facilitates the interconnection of data applications across different devices and domains. This short paper serves as an invitation to dialogue over this proposal.
A Compact Optical Six-Axis Force/Torque Sensor for Legged Robots Using a Polymorphic Calibration Method

This paper presents a novel design for a compact, lightweight 6-axis force/torque sensor intended for use in legged robots. The design promotes easy manufacturing and cost reduction, while introducing innovative calibration methods that simplify the calibration process and minimize effort. The sensor's advantages are achieved by streamlining the structure for durability, implementing noncontact sensors, and providing a wider sensing range compared to commercial sensors. To maintain a simple structure, the paper proposes a force sensing scheme using photocouplers where the sensing elements are aligned in-plane. This strategy enables all sensing elements to be fabricated on a single printed circuit board, eliminating manual labor tasks such as bonding and coating the sensing elements. The prototype sensor contains only four parts, costs less than $250, and exhibits high response frequency and performance. Traditional calibration methods present challenges, such as the need for specialized equipment and extensive labor. To facilitate easy calibration without the need for specialized equipment, a new method using optimal control is proposed. To verify the feasibility of these ideas, a prototype six-axis F/T sensor was manufactured. Its performance was evaluated and compared to a reference F/T sensor and previous calibration methods.
Personalized Search Via Neural Contextual Semantic Relevance Ranking

Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to personalize document ranking results by leveraging the signals to capture how the document fits into users' context. In particular, it models the relationships between document content and user query context using both lexical representations and semantic embeddings such that the user's intent can be better understood by data enrichment of personalized query context information. Extensive experiments performed on the search dataset, demonstrate the effectiveness of the proposed method.
From Reversible Computation to Checkpoint-Based Rollback Recovery for Message-Passing Concurrent Programs

The reliability of concurrent and distributed systems often depends on some well-known techniques for fault tolerance. One such technique is based on checkpointing and rollback recovery. Checkpointing involves processes to take snapshots of their current states regularly, so that a rollback recovery strategy is able to bring the system back to a previous consistent state whenever a failure occurs. In this paper, we consider a message-passing concurrent programming language and propose a novel rollback recovery strategy that is based on some explicit checkpointing operators and the use of a (partially) reversible semantics for rolling back the system.
Differentiable Simulation of a Liquid Argon Time Projection Chamber

Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.
Style Generation: Image Synthesis based on Coarsely Matched Texts

Previous text-to-image synthesis algorithms typically use explicit textual instructions to generate/manipulate images accurately, but they have difficulty adapting to guidance in the form of coarsely matched texts. In this work, we attempt to stylize an input image using such coarsely matched text as guidance. To tackle this new problem, we introduce a novel task called text-based style generation and propose a two-stage generative adversarial network: the first stage generates the overall image style with a sentence feature, and the second stage refines the generated style with a synthetic feature, which is produced by a multi-modality style synthesis module. We re-filter one existing dataset and collect a new dataset for the task. Extensive experiments and ablation studies are conducted to validate our framework. The practical potential of our work is demonstrated by various applications such as text-image alignment and story visualization. Our datasets are published at https://www.kaggle. com/datasets/mengyaocui/style-generation.
Diffusio-phoretic fast swelling of chemically responsive hydrogels

Acid-induced release of stored ions from polyacrylic acid hydrogels (with a free surface fully permeable to the ion and acid flux) was observed to increase the gel osmotic pressure that leads to rapid, temporary swelling faster than the characteristic solvent absorption rate of the gel. Here we develop a continuum poroelastic theory that quantitatively explains the experiments by introducing a"gel diffusio-phoresis"mechanism: Steric repulsion between the gel polymers and released ions can induce a diffusio-osmotic solvent intake counteracted by the diffusio-phoretic expansion of the gel network. For applications ranging from drug delivery to soft robotics, engineering the gel diffusio-phoresis may enable stimuli-responsive hydrogels with amplified strain rates and power output.
SIM-Sync: From Certifiably Optimal Synchronization over the 3D Similarity Group to Scene Reconstruction with Learned Depth

This paper presents SIM-Sync, a certifiably optimal algorithm that estimates camera trajectory and 3D scene structure directly from multiview image keypoints. SIM-Sync fills the gap between pose graph optimization and bundle adjustment; the former admits efficient global optimization but requires relative pose measurements and the latter directly consumes image keypoints but is difficult to optimize globally (due to camera projective geometry). The bridge to this gap is a pretrained depth prediction network. Given a graph with nodes representing monocular images taken at unknown camera poses and edges containing pairwise image keypoint correspondences, SIM-Sync first uses a pretrained depth prediction network to lift the 2D keypoints into 3D scaled point clouds, where the scaling of the per-image point cloud is unknown due to the scale ambiguity in monocular depth prediction. SIM-Sync then seeks to synchronize jointly the unknown camera poses and scaling factors (i.e., over the 3D similarity group). The SIM-Sync formulation, despite nonconvex, allows designing an efficient certifiably optimal solver that is almost identical to the SE-Sync algorithm. We demonstrate the tightness, robustness, and practical usefulness of SIM-Sync in both simulated and real experiments. In simulation, we show (i) SIM-Sync compares favorably with SE-Sync in scale-free synchronization, and (ii) SIM-Sync can be used together with robust estimators to tolerate a high amount of outliers. In real experiments, we show (a) SIM-Sync achieves similar performance as Ceres on bundle adjustment datasets, and (b) SIM-Sync performs on par with ORB-SLAM3 on the TUM dataset with zero-shot depth prediction.
Gas-phase metallicity of local AGN in the GASP and MaNGA surveys: the role of ram-pressure stripping

Growing evidence in support of a connection between Active Galactic Nuclei (AGN) activity and the Ram-Pressure Stripping (RPS) phenomenon has been found both observationally and theoretically in the past decades. In this work, we further explore the impact of RPS on the AGN activity by estimating the gas-phase metallicity of nuclear regions and the mass-metallicity relation of galaxies at $z \leq$ 0.07 and with stellar masses $\log {\rm M}_* / {\rm M}_\odot \geq 9.0 $, either experiencing RPS or not. To measure oxygen abundances, we exploit Integral Field Spectroscopy data from the GASP and MaNGA surveys, photoionization models generated with the code CLOUDY and the code Nebulabayes to compare models and observations. In particular, we build CLOUDY models to reproduce line ratios induced by photoionization from stars, AGN, or a contribution of both. We find that the distributions of metallicity and [O III]$\lambda$5007 luminosity of galaxies undergoing RPS are similar to the ones of undisturbed galaxies. Independently of the RPS, we do not find a correlation between stellar mass and AGN metallicity in the mass range $\log {\rm M}_* / {\rm M}_\odot \geq 10.4$, while for the star-forming galaxies we observe the well-known mass-metallicity relation (MZR) between $ 9.0 \leq \log \ {\rm M}_* /{\rm M}_\odot \leq 10.8$ with a scatter mainly driven by the star-formation rate (SFR) and a plateau around $\log {\rm M}_* / {\rm M}_\odot \sim 10.5$. The gas-phase metallicity in the nuclei of AGN hosts is enhanced with respect to those of SF galaxies by a factor of $\sim$ 0.05 dex regardless of the RPS.
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, student behavior detection, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.
Mirror-Aware Neural Humans

Human motion capture either requires multi-camera systems or is unreliable using single-view input due to depth ambiguities. Meanwhile, mirrors are readily available in urban environments and form an affordable alternative by recording two views with only a single camera. However, the mirror setting poses the additional challenge of handling occlusions of real and mirror image. Going beyond existing mirror approaches for 3D human pose estimation, we utilize mirrors for learning a complete body model, including shape and dense appearance. Our main contributions are extending articulated neural radiance fields to include a notion of a mirror, making it sample-efficient over potential occlusion regions. Together, our contributions realize a consumer-level 3D motion capture system that starts from off-the-shelf 2D poses by automatically calibrating the camera, estimating mirror orientation, and subsequently lifting 2D keypoint detections to 3D skeleton pose that is used to condition the mirror-aware NeRF. We empirically demonstrate the benefit of learning a body model and accounting for occlusion in challenging mirror scenes.