Miracle-dz/UniFAR-FTUs huggingface dataset:
State Estimation Using Single Body-Frame Bearing Measurements

This paper addresses the problem of simultaneous estimation of the position, linear velocity and orientation of a rigid body using single bearing measurements. We introduce a Riccati observer-based estimator that fuses measurements from a 3-axis accelerometer, a 3-axis gyroscope, a single body-frame vector observation (e.g., magnetometer), and a single bearing-to-landmark measurement to obtain the full vehicle's state (position, velocity, orientation). The proposed observer guarantees global exponential convergence under some persistency of excitation (PE) condition on the vehicle's motion. Simulation results are presented to show the effectiveness of the proposed approach.
Semialgebraic Range Stabbing, Ray Shooting, and Intersection Counting in the Plane

Polynomial partitioning techniques have recently led to improved geometric data structures for a variety of fundamental problems related to semialgebraic range searching and intersection searching in 3D and higher dimensions (e.g., see [Agarwal, Aronov, Ezra, and Zahl, SoCG 2019; Ezra and Sharir, SoCG 2021; Agarwal, Aronov, Ezra, Katz, and Sharir, SoCG 2022]). They have also led to improved algorithms for offline versions of semialgebraic range searching in 2D, via lens-cutting [Sharir and Zahl (2017)]. In this paper, we show that these techniques can yield new data structures for a number of other 2D problems even for online queries: 1. Semialgebraic range stabbing. We present a data structure for $n$ semialgebraic ranges in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of ranges containing a query point in $O(n^{1/4+\varepsilon})$ time, for an arbitrarily small constant $\varepsilon>0$. 2. Ray shooting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can find the first arc hit by a query (straight-line) ray in $O(n^{1/4+\varepsilon})$ time. 3. Intersection counting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of intersection points with a query algebraic arc of constant description complexity in $O(n^{1/2+\varepsilon})$ time. In particular, this implies an $O(n^{3/2+\varepsilon})$-time algorithm for counting intersections between two sets of $n$ algebraic arcs in 2D.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation

This paper studies a new open-set problem, the open-vocabulary category-level object pose and size estimation. Given human text descriptions of arbitrary novel object categories, the robot agent seeks to predict the position, orientation, and size of the target object in the observed scene image. To enable such generalizability, we first introduce OO3D-9D, a large-scale photorealistic dataset for this task. Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation. It includes additional annotations for the symmetry axis of each category, which help resolve symmetric ambiguity. Apart from the large-scale dataset, we find another key to enabling such generalizability is leveraging the strong prior knowledge in pre-trained visual-language foundation models. We then propose a framework built on pre-trained DinoV2 and text-to-image stable diffusion models to infer the normalized object coordinate space (NOCS) maps of the target instances. This framework fully leverages the visual semantic prior from DinoV2 and the aligned visual and language knowledge within the text-to-image diffusion model, which enables generalization to various text descriptions of novel categories. Comprehensive quantitative and qualitative experiments demonstrate that the proposed open-vocabulary method, trained on our large-scale synthesized data, significantly outperforms the baseline and can effectively generalize to real-world images of unseen categories. The project page is at https://ov9d.github. io.
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition

Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions, but also contextual regions important for action recognition. Lack of interpretability erodes trust in these new technologies. We highlight the limitations of current paradigms and propose a solution: Human selected privacy templates that yield interpretability by design, an obfuscation scheme that selectively hides attributes and also induces temporal consistency, which is important in action recognition. Our approach is architecture agnostic and directly modifies input imagery, while existing approaches generally require architecture training. Our approach offers more flexibility, as no retraining is required, and outperforms alternatives on three widely used datasets.
Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts

Pretrained Language Models (PLMs) have advanced Natural Language Processing (NLP) tasks significantly, but finetuning PLMs on low-resource datasets poses significant challenges such as instability and overfitting. Previous methods tackle these issues by finetuning a strategically chosen subnetwork on a downstream task, while keeping the remaining weights fixed to the pretrained weights. However, they rely on a suboptimal criteria for sub-network selection, leading to suboptimal solutions. To address these limitations, we propose a regularization method based on attention-guided weight mixup for finetuning PLMs. Our approach represents each network weight as a mixup of task-specific weight and pretrained weight, controlled by a learnable attention parameter, providing finer control over sub-network selection. Furthermore, we employ a bi-level optimization (BLO) based framework on two separate splits of the training dataset, improving generalization and combating overfitting. We validate the efficacy of our proposed method through extensive experiments, demonstrating its superiority over previous methods, particularly in the context of finetuning PLMs on low-resource datasets.
Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model

A first-principle single-object model is proposed for pedestrian tracking. It is assumed that the extent of the moving object can be described via known statistics in 3D, such as pedestrian height. The proposed model thus need not constrain the object motion in 3D to a common ground plane, which is usual in 3D visual tracking applications. A nonlinear filter for this model is implemented using the unscented Kalman filter (UKF) and tested using the publicly available MOT-17 dataset. The proposed solution yields promising results in 3D while maintaining perfect results when projected into the 2D image. Moreover, the estimation error covariance matches the true one. Unlike conventional methods, the introduced model parameters have convenient meaning and can readily be adjusted for a problem.
Contextual AD Narration with Interleaved Multimodal Sequence

The Audio Description (AD) task aims to generate descriptions of visual elements for visually impaired individuals to help them access long-form video contents, like movie. With video feature, text, character bank and context information as inputs, the generated ADs are able to correspond to the characters by name and provide reasonable, contextual descriptions to help audience understand the storyline of movie. To achieve this goal, we propose to leverage pre-trained foundation models through a simple and unified framework to generate ADs with interleaved multimodal sequence as input, termed as Uni-AD. To enhance the alignment of features across various modalities with finer granularity, we introduce a simple and lightweight module that maps video features into the textual feature space. Moreover, we also propose a character-refinement module to provide more precise information by identifying the main characters who play more significant role in the video context. With these unique designs, we further incorporate contextual information and a contrastive loss into our architecture to generate more smooth and contextual ADs. Experiments on the MAD-eval dataset show that Uni-AD can achieve state-of-the-art performance on AD generation, which demonstrates the effectiveness of our approach. Code will be available at https://github.com/MCG-NJU/Uni-AD.
Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees

With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of sustainable energy sources. Besides traditional sources, the residential sector is another major and largely untapped source of flexibility, driven by the increased adoption of solar PV, home batteries, and EVs. However, unlocking this residential flexibility is challenging as it requires a control framework that can effectively manage household energy consumption, and maintain user comfort while being readily scalable across different, diverse houses. We aim to address this challenging problem and introduce a reinforcement learning-based approach using differentiable decision trees. This approach integrates the scalability of data-driven reinforcement learning with the explainability of (differentiable) decision trees. This leads to a controller that can be easily adapted across different houses and provides a simple control policy that can be explained to end-users, further improving user acceptance. As a proof-of-concept, we analyze our method using a home energy management problem, comparing its performance with commercially available rule-based baseline and standard neural network-based RL controllers. Through this preliminary study, we show that the performance of our proposed method is comparable to standard RL-based controllers, outperforming baseline controllers by ~20% in terms of daily cost savings while being straightforward to explain.
Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis

Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-supervised multi-modal diagnosis framework. In particular, we introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising minimal labels. We first introduce a bilevel hypergraph optimisation framework that jointly learns a graph augmentation policy and a semi-supervised classifier. This dual learning strategy is hypothesised to enhance the robustness and generalisation capabilities of the model by fostering new pathways for information propagation. Secondly, we introduce a novel strategy for generating pseudo-labels more effectively via a gradient-driven flow. Our experimental results demonstrate the superior performance of our framework over current techniques in diagnosing Alzheimer's disease.
Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models

In many real natural language processing application scenarios, practitioners not only aim to maximize predictive performance but also seek faithful explanations for the model predictions. Rationales and importance distribution given by feature attribution methods (FAs) provide insights into how different parts of the input contribute to a prediction. Previous studies have explored how different factors affect faithfulness, mainly in the context of monolingual English models. On the other hand, the differences in FA faithfulness between multilingual and monolingual models have yet to be explored. Our extensive experiments, covering five languages and five popular FAs, show that FA faithfulness varies between multilingual and monolingual models. We find that the larger the multilingual model, the less faithful the FAs are compared to its counterpart monolingual models.Our further analysis shows that the faithfulness disparity is potentially driven by the differences between model tokenizers. Our code is available: https://github.com/casszhao/multilingual-faith.
Blockchain-Empowered Immutable and Reliable Delivery Service (BIRDS) Using UAV Networks

Exploiting unmanned aerial vehicles (UAVs) for delivery services is expected to reduce delivery time and human resource costs. However, the proximity of these UAVs to the ground can make them an ideal target for opportunistic criminals. Consequently, UAVs may be hacked, diverted from their destinations, or used for malicious purposes. Furthermore, as a decentralized (peer-to-peer) technology, the blockchain has immense potential to enable secure, decentralized, and cooperative communication among UAVs. With this goal in mind, we propose the Blockchain-Empowered, Immutable, and Reliable Delivery Service (BIRDS) framework to address data security challenges. BIRDS deploys communication hubs across a scalable network. Following the registration phase of BIRDS, UAV node selection is carried out based on a specific consensus proof-of-competence (PoC), where UAVs are evaluated solely on their credibility. The chosen finalist is awarded a certificate for the BIRDS global order fulfillment system. The simulation results demonstrate that BIRDS requires fewer UAVs compared to conventional solutions, resulting in reduced costs and emissions. The proposed BIRDS framework caters to the requirements of numerous users while necessitating less network traffic and consuming low energy.
Stability of Locally Optimal Solutions

Necessary and sufficient conditions are obtained for the Lipschitzian stability of local solutions to finite-dimensional parameterized optimization problems in a very general setting. Properties of prox-regularity of the essential objective function and positive definiteness of its coderivative Hessian are the keys to these results. A previous characterization of tilt stability arises as a special case.
Bounds on the photon mass via the Shapiro effect in the solar system

We study the effects of a finite mass for the photon on its propagation in a weak gravitational field. In particular, we analyse the gravitational time delay, also known as the Shapiro effect. We work in isotropic coordinates in the weak-field limit and find that the mass-dependent corrections enhance the gravitational time delay. Doppler-tracking data from the Cassini mission allow us to set upper bounds on the photon mass at the level of $m_\gamma \lesssim 3 \times 10^{-7} \, {\rm eV/c^2}$. We also discuss next-generation solar-system tests of general relativity that could improve this upper limit, potentially reaching $m_\gamma \lesssim 8 \times 10^{-8} \, {\rm eV/c^2}$. Though not competitive with the currently best limits, our projected bounds are at the ballpark of earlier ones based on the gravitational deflection angle.
Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model

Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing the downstream task. Despite being effective, most existing works struggle to achieve cross-domain SSL pre-training, missing valuable opportunities to integrate patterns and features from different domains. The main challenge lies in the significant differences in the characteristics of time-series data across different domains, such as variations in the number of channels and temporal resolution scales. To address this challenge, we propose CrossTimeNet, a novel cross-domain SSL learning framework to learn transferable knowledge from various domains to largely benefit the target downstream task. One of the key characteristics of CrossTimeNet is the newly designed time series tokenization module, which could effectively convert the raw time series into a sequence of discrete tokens based on a reconstruction optimization process. Besides, we highlight that predicting a high proportion of corrupted tokens can be very helpful for extracting informative patterns across different domains during SSL pre-training, which has been largely overlooked in past years. Furthermore, unlike previous works, our work treats the pre-training language model (PLM) as the initialization of the encoder network, investigating the feasibility of transferring the knowledge learned by the PLM to the time series area. Through these efforts, the path to cross-domain pre-training of a generic time series model can be effectively paved. We conduct extensive experiments in a real-world scenario across various time series classification domains. The experimental results clearly confirm CrossTimeNet's superior performance.
DEE: Dual-stage Explainable Evaluation Method for Text Generation

Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems. Conventional methods tend to grapple with a lack of explainability, issuing a solitary numerical score to signify the assessment outcome. Recent advancements have sought to mitigate this limitation by incorporating large language models (LLMs) to offer more detailed error analyses, yet their applicability remains constrained, particularly in industrial contexts where comprehensive error coverage and swift detection are paramount. To alleviate these challenges, we introduce DEE, a Dual-stage Explainable Evaluation method for estimating the quality of text generation. Built upon Llama 2, DEE follows a dual-stage principle guided by stage-specific instructions to perform efficient identification of errors in generated texts in the initial stage and subsequently delves into providing comprehensive diagnostic reports in the second stage. DEE is fine-tuned on our elaborately assembled dataset AntEval, which encompasses 15K examples from 4 real-world applications of Alipay that employ generative systems. The dataset concerns newly emerged issues like hallucination and toxicity, thereby broadening the scope of DEE's evaluation criteria. Experimental results affirm that DEE's superiority over existing evaluation methods, achieving significant improvements in both human correlation as well as efficiency.
Scaling limit of heavy tailed nearly unstable INAR($\infty$) processes and rough fractional diffusions

In this paper, we investigated the scaling limit of heavy-tailed unstable INAR($\infty$) processes. These processes exhibit a power law tail of the form $n^{-(1+\alpha)}$, with $\alpha \in (\frac{1}{2}, 1)$, where the $\ell^1$ norm of the kernel vector is close to $1$. The result is in contrast to scaling limit of the continuous-time heavy tailed unstable Hawkes processes and the one of INAR($p$) processes. We show that the discrete-time scaling limit also has long-memory property and can also be seen as an integrated fractional Cox-Ingersoll-Ross process.
A framework for unsupervised training of object detectors from unlabeled surveillance video

Object detection is a critical step in automated surveillance. A common approach to building object detectors involves statistical learning techniques using as input large annotated data sets. However, due to inevitable limitations of a typical training data set, this supervised approach is unsuitable for building a generic surveillance system applicable to a wide variety of scenes, objects and camera setups. To make a step towards a more generic object detection solution, we propose in this paper an unsupervised method capable of learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In the first step of the method, a coarse object detector is built to identify candidate dominant objects based on motion segmentation results obtained for the observed scene. Then, clustering and cluster validation are applied to refine the output of the coarse detector and to select a subset of this output that can be used to train a more sophisticated dominant object detector. Finally, we deploy this trained detector to find further instances of the dominant object class in the observed scene. We demonstrate the effectiveness of our method experimentally on four representative video sequences.
Wildfire danger prediction optimization with transfer learning

Convolutional Neural Networks (CNNs) have proven instrumental across various computer science domains, enabling advancements in object detection, classification, and anomaly detection. This paper explores the application of CNNs to analyze geospatial data specifically for identifying wildfire-affected areas. Leveraging transfer learning techniques, we fine-tuned CNN hyperparameters and integrated the Canadian Fire Weather Index (FWI) to assess moisture conditions. The study establishes a methodology for computing wildfire risk levels on a scale of 0 to 5, dynamically linked to weather patterns. Notably, through the integration of transfer learning, the CNN model achieved an impressive accuracy of 95\% in identifying burnt areas. This research sheds light on the inner workings of CNNs and their practical, real-time utility in predicting and mitigating wildfires. By combining transfer learning and CNNs, this study contributes a robust approach to assess burnt areas, facilitating timely interventions and preventative measures against conflagrations.
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator

Based on multilingual pre-trained models, cross-lingual transfer with prompt learning has shown promising effectiveness, where soft prompt learned in a source language is transferred to target languages for downstream tasks, particularly in the low-resource scenario. To efficiently transfer soft prompt, we propose a novel framework, Multilingual Prompt Translator (MPT), where a multilingual prompt translator is introduced to properly process crucial knowledge embedded in prompt by changing language knowledge while retaining task knowledge. Concretely, we first train prompt in source language and employ translator to translate it into target prompt. Besides, we extend an external corpus as auxiliary data, on which an alignment task for predicted answer probability is designed to convert language knowledge, thereby equipping target prompt with multilingual knowledge. In few-shot settings on XNLI, MPT demonstrates superiority over baselines by remarkable improvements. MPT is more prominent compared with vanilla prompting when transferring to languages quite distinct from source language.