Miracle-dz/UniFAR-FTUs huggingface dataset:
Obtaining a scalar fifth force for matter via a conformal coupling to the scalar field

In the framework of special relativity (SR), I propose that matter conformally couples to a scalar field through the Lagrangian density of matter, whether matter is characterized by classical or by quantum (statistical) mechanics. The largest interaction strength of the scalar-mediated force can achieve the order of $1/\Lambda_E^2$ with the cosmological constant $\Lambda_E\approx 2.4\,\rm{meV}$. This is about 60 orders of magnitude larger than Newtonian gravity, i.e., ${(M_{\mathrm{Pl}}/\Lambda_E)}^2\sim 10^{60}$, where $M_{\mathrm{Pl}}\approx2.4\times 10^{18}\, \rm{GeV} $ is the reduced Planck energy. However, the discrete $\mathbb{Z}_2$ symmetry of the conformal coupling does not allow a massless scalar field, satisfying the constraint that no evidence of the long range force is observed. The interaction range of the force is inversely proportional to the square root of the ambient density. Therefore, in the general situation in which the ambient density is larger than the current cosmic density, the interaction range is always smaller than $1/\Lambda_E \approx 80\, \mu\rm{m}$. The form of general relativity (GR) still holds even when the energy-momentum tensors of the quintessence and the conformal interaction are included. The main purpose here is to overcome the puzzle in \cite{hcz2}, i.e., which frame is physical.
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction

This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO, based on an iterated extended Kalman filter (iEKF) framework. We adapt the sweep reconstruction method, which segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequency. We found that such method can effectively reduce the time interval for each iterated state update, improving the state estimation accuracy and enabling the usage of iEKF framework for fusing high-frequency IMU and low-frequency LiDAR. To prevent inaccurate trajectory caused by multiple distortion correction to a particular point, we further propose to perform distortion correction for each segment. Experimental results on four public datasets demonstrate that our SR-LIO outperforms all existing state-of-the-art methods on accuracy, and reducing the time interval of iterated state update via the proposed sweep reconstruction can improve the accuracy and frequency of estimated states. The source code of SR-LIO is publicly available for the development of the community.
Gravitational duality, Palatini variation and boundary terms: a synopsis

We consider f(R) gravity and Born–Infeld–Einstein (BIE) gravity in formulations where the metric and connection are treated independently and integrate out the metric to find the corresponding models solely in terms of the connection, the archetypical treatment being that of Eddington–Schrödinger (ES) duality between cosmological Einstein and Eddington theories. For dimensions D≠2 , we find that this requires f(R) to have a specific form which makes the model Weyl invariant, and that its Eddington reduction is then equivalent to that of BIE with certain parameters. For D = 2 dimensions, where ES duality is not applicable, we find that both models are Weyl invariant and equivalent to a first order formulation of the bosonic string. We also discuss the form of the boundary terms needed for the variational principle to be well defined on manifolds with non-null boundaries. This requires a modification of the Gibbons–Hawking–York (GHY) boundary term for gravity. This modification also means that the dualities between metric and connection formulations are consistent and include the boundary terms.
A Survey of Active Learning for Natural Language Processing

In this work, we provide a literature review of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.
Deep neural network expressivity for optimal stopping problems

This article studies deep neural network expression rates for optimal stopping problems of discrete-time Markov processes on high-dimensional state spaces. A general framework is established in which the value function and continuation value of an optimal stopping problem can be approximated with error at most $\varepsilon$ by a deep ReLU neural network of size at most $\kappa d^{\mathfrak{q}} \varepsilon^{-\mathfrak{r}}$. The constants $\kappa,\mathfrak{q},\mathfrak{r} \geq 0$ do not depend on the dimension $d$ of the state space or the approximation accuracy $\varepsilon$. This proves that deep neural networks do not suffer from the curse of dimensionality when employed to solve optimal stopping problems. The framework covers, for example, exponential L\'evy models, discrete diffusion processes and their running minima and maxima. These results mathematically justify the use of deep neural networks for numerically solving optimal stopping problems and pricing American options in high dimensions.
Analysis of Variance for Functional Data

Introduction Functional Data Motivating Functional Data Why Is Functional Data Analysis Needed? Overview of the Book Implementation of Methodologies Options for Reading This Book Nonparametric Smoothers for a Single Curve Introduction Local Polynomial Kernel Smoothing Regression Splines Smoothing Splines P-Splines Reconstruction of Functional Data Introduction Reconstruction Methods Accuracy of LPK Reconstructions Accuracy of LPK Reconstruction in FLMs Stochastic Processes Introduction Stochastic Processes x2-Type Mixtures F-Type Mixtures One-Sample Problem for Functional Data ANOVA for Functional Data Introduction Two-Sample Problem One-Way ANOVA Two-Way ANOVA Linear Models with Functional Responses Introduction Linear Models with Time-Independent Covariates Linear Models with Time-Dependent Covariates Ill-Conditioned Functional Linear Models Introduction Generalized Inverse Method Reparameterization Method Side-Condition Method Diagnostics of Functional Observations Introduction Residual Functions Functional Outlier Detection Influential Case Detection Robust Estimation of Coefficient Functions Outlier Detection for a Sample of Functions Heteroscedastic ANOVA for Functional Data Introduction Two-Sample Behrens-Fisher Problems Heteroscedastic One-Way ANOVA Heteroscedastic Two-Way ANOVA Test of Equality of Covariance Functions Introduction Two-Sample Case Multi-Sample Case Bibliography Index Technical Proofs, Concluding Remarks, Bibliographical Notes, and Exercises appear at the end of most chapters.
A kernel Stein test of goodness of fit for sequential models

We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein operator: current operators used in testing apply to fixed-dimensional spaces. As our main contribution, we extend the KSD to the variable-dimension setting by identifying appropriate Stein operators, and propose a novel KSD goodness-of-fit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class of models. Our test is shown to perform well in practice on discrete sequential data benchmarks.
Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Models

The impressive capacity shown by recent text-to-image diffusion models to generate high-quality pictures from textual input prompts has leveraged the debate about the very definition of art. Nonetheless, these models have been trained using text data collected from content-based labelling protocols that focus on describing the items and actions in an image but neglect any subjective appraisal. Consequently, these automatic systems need rigorous descriptions of the elements and the pictorial style of the image to be generated, otherwise failing to deliver. As potential indicators of the actual artistic capabilities of current generative models, we characterise the sentimentality, objectiveness and degree of abstraction of publicly available text data used to train current text-to-image diffusion models. Considering the sharp difference observed between their language style and that typically employed in artistic contexts, we suggest generative models should incorporate additional sources of subjective information in their training in order to overcome (or at least to alleviate) some of their current limitations, thus effectively unleashing a truly artistic and creative generation.
Hope of Delivery: Extracting User Locations From Mobile Instant Messengers

Mobile instant messengers such as WhatsApp use delivery status notifications in order to inform users if a sent message has successfully reached its destination. This is useful and important information for the sender due to the often asynchronous use of the messenger service. However, as we demonstrate in this paper, this standard feature opens up a timing side channel with unexpected consequences for user location privacy. We investigate this threat conceptually and experimentally for three widely spread instant messengers. We validate that this information leak even exists in privacy-friendly messengers such as Signal and Threema. Our results show that, after a training phase, a messenger user can distinguish different locations of the message receiver. Our analyses involving multiple rounds of measurements and evaluations show that the timing side channel persists independent of distances between receiver locations -- the attack works both for receivers in different countries as well as at small scale in one city. For instance, out of three locations within the same city, the sender can determine the correct one with more than 80% accuracy. Thus, messenger users can secretly spy on each others' whereabouts when sending instant messages. As our countermeasure evaluation shows, messenger providers could effectively disable the timing side channel by randomly delaying delivery confirmations within the range of a few seconds. For users themselves, the threat is harder to prevent since there is no option to turn off delivery confirmations.
Comparison of Varied 2D Mapping Approaches by Using Practice-Oriented Evaluation Criteria

A key aspect of the precision of a mobile robots localization is the quality and aptness of the map it is using. A variety of mapping approaches are available that can be employed to create such maps with varying degrees of effort, hardware requirements and quality of the resulting maps. To create a better understanding of the applicability of these different approaches to specific applications, this paper evaluates and compares three different mapping approaches based on simultaneous localization and mapping, terrestrial laser scanning as well as publicly accessible building contours.
A Segment-Wise Gaussian Process-Based Ground Segmentation With Local Smoothness Estimation

In terrestrial and extraterrestrial environments, the precise and informative model of the ground and the surface ahead is crucial for navigation and obstacle avoidance. The ground surface is not always flat, the functional relationship of the surface-related features may vary in different areas of the ground, and the measurement of point clouds derived from the ground surface does not necessarily reflect smoothness. Thus, ground-related features of the ground surface must be calculated based on local or point estimates. This paper proposes a segment-wise Gaussian Process (GP)-based ground estimation method with input-dependent smoothness estimation. The value of the length scale is estimated locally for each data point resulting in a more precise estimate for the rough scenes while being not computationally complex and robust to under-segmentation, sparsity, and under-represent-ability issues. The segment-wise task estimates a partial continuous model of the ground for each radial range segment. The simulation results show that it can give a precise continuous estimate of the ground surface in rough and bumpy scenes while being fast enough for real-time applications.
Progressive Video Summarization via Multimodal Self-supervised Learning

Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-fitting of the deep models. Considering that the annotation of large-scale datasets is time-consuming, we propose a multimodal self-supervised learning framework to obtain semantic representations of videos, which benefits the video summarization task. Specifically, the self-supervised learning is conducted by exploring the semantic consistency between the videos and text in both coarse-grained and fine-grained fashions, as well as recovering masked frames in the videos. The multimodal framework is trained on a newly-collected dataset that consists of video-text pairs. Additionally, we introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. Extensive experiments have proved the effectiveness and superiority of our method in rank correlation coefficients and F-score1.
Initial Orbit Determination from Only Heading Measurements

This work introduces the problem of initial orbit determination (IOD) from only heading measurements. Such a problem occurs in practice when estimating the orbit of a spacecraft using visual odometry measurements from an optical camera. After reviewing the problem geometry, a simple solution is developed in the form of an iterative scheme on the parameters describing the orbital hodograph. Numerical results are presented for an example spacecraft in low lunar orbit. The principal intent of this brief study is to communicate the existence of a new class of IOD problem to the community and to encourage the broader study of hodographs and heading-only IOD.
Bidiagonal Decompositions of Vandermonde-Type Matrices of Arbitrary Rank

We present a method to derive new explicit expressions for bidiagonal decompositions of Vandermonde and related matrices such as the (q-, h-) Bernstein-Vandermonde ones, among others. These results generalize the existing expressions for nonsingular matrices to matrices of arbitrary rank. For totally nonnegative matrices of the above classes, the new decompositions can be computed efficiently and to high relative accuracy componentwise in floating point arithmetic. In turn, matrix computations (e.g., eigenvalue computation) can also be performed efficiently and to high relative accuracy.
EnTDA: Entity-to-Text based Data Augmentation Approach for Named Entity Recognition Tasks

Data augmentation techniques have been used to improve the generalization capability of models in the named entity recognition (NER) tasks. Existing augmentation methods either manipulate the words in the original text that require hand-crafted in-domain knowledge, or leverage generative models which solicit dependency order among entities. To alleviate the excessive reliance on the dependency order among entities in existing augmentation paradigms, we develop an entity-to-text instead of text-to-entity based data augmentation method named EnTDA to decouple the dependencies between entities by adding, deleting, replacing and swapping entities, and adopt these augmented data to bootstrap the generalization ability of the NER model. Furthermore, we introduce a diversity beam search to increase the diversity of the augmented data. Experiments on thirteen NER datasets across three tasks (ﬂat NER, nested NER, and discontinuous NER) and two settings (full data NER and low resource NER) show that EnTDA could consistently outperform the baselines.
The modified K\"ahler-Ricci flow, II

We improve the understanding of both finite time and infinite time singularities of the modified K\"ahler-Ricci flow as initiated by the second author of this paper in [26]. This is done by relating the modified K\"ahler-Ricci flow with the recent studies on the classic K\"ahler-Ricci flow and the degenerate complex Monge-Amp\`ere equation.
Dispersion relations in quantum electrodynamics on the noncommutative Minkowski space

We study field theories on the noncommutative Minkowski space with noncommuting time. The focus lies on dispersion relations in quantized interacting models in the Yang-Feldman formalism. In particular, we compute the two-point correlation function of the field strength in noncommutative quantum electrodynamics to second order. At this, we take into account the covariant coordinates that allow the construction of local gauge invariant quantities (observables). It turns out that this does not remove the well-known severe infrared problem, as one might have hoped. Instead, things become worse, since nonlocal divergences appear. We also show that these cancel in a supersymmetric version of the theory if the covariant coordinates are adjusted accordingly. Furthermore, we study the phi^3 and the Wess-Zumino model and show that the distortion of the dispersion relations is moderate for parameters typical for the Higgs field. We also disuss the formulation of gauge theories on noncommutative spaces and study classical electrodynamics on the noncommutative Minkowski space using covariant coordinates. In particular, we compute the change of the speed of light due to nonlinear effects in the presence of a background field. Finally, we examine the so-called twist approach to quantum field theory on the noncommutative Minkowski space and point out some conceptual problems of this approach.
Learning to Discover and Detect Objects

We tackle the problem of novel class discovery and localization (NCDL). In this setting, we assume a source dataset with supervision for only some object classes. Instances of other classes need to be discovered, classified, and localized automatically based on visual similarity without any human supervision. To tackle NCDL, we propose a two-stage object detection network Region-based NCDL (RNCDL) that uses a region proposal network to localize regions of interest (RoIs). We then train our network to learn to classify each RoI, either as one of the known classes, seen in the source dataset, or one of the novel classes, with a long-tail distribution constraint on the class assignments, reflecting the natural frequency of classes in the real world. By training our detection network with this objective in an end-to-end manner, it learns to classify all region proposals for a large variety of classes, including those not part of the labeled object class vocabulary. Our experiments conducted using COCO and LVIS datasets reveal that our method is significantly more effective than multi-stage pipelines that rely on traditional clustering algorithms. Furthermore, we demonstrate the generality of our approach by applying our method to a large-scale Visual Genome dataset, where our network successfully learns to detect various semantic classes without direct supervision.
On History-Deterministic One-Counter Nets

We consider the model of history-deterministic one-counter nets (OCNs). History-determinism is a property of transition systems that allows for a limited kind of non-determinism which can be resolved 'on-the-fly'. Token games, which have been used to characterise history-determinism over various models, also characterise history-determinism over OCNs. By reducing 1-token games to simulation games, we are able to show that checking for history-determinism of OCNs is decidable. Moreover, we prove that this problem is PSPACE-complete for a unary encoding of transitions, and EXPSPACE-complete for a binary encoding. We then study the language properties of history-deterministic OCNs. We show that the resolvers of non-determinism for history-deterministic OCNs are eventually periodic. As a consequence, for a given history-deterministic OCN, we construct a language equivalent deterministic one-counter automaton. We also show the decidability of comparing languages of history-deterministic OCNs, such as language inclusion and language universality.
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design

We study reserve price optimization in multi-phase second price auctions, where seller's prior actions affect the bidders' later valuations through a Markov Decision Process (MDP). Compared to the bandit setting in existing works, the setting in ours involves three challenges. First, from the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders who aim to manipulates seller's policy. Second, we want to minimize the seller's revenue regret when the market noise distribution is unknown. Third, the seller's per-step revenue is unknown, nonlinear, and cannot even be directly observed from the environment. We propose a mechanism addressing all three challenges. To address the first challenge, we use a combination of a new technique named"buffer periods"and inspirations from Reinforcement Learning (RL) with low switching cost to limit bidders' surplus from untruthful bidding, thereby incentivizing approximately truthful bidding. The second one is tackled by a novel algorithm that removes the need for pure exploration when the market noise distribution is unknown. The third challenge is resolved by an extension of LSVI-UCB, where we use the auction's underlying structure to control the uncertainty of the revenue function. The three techniques culminate in the $\underline{\rm C}$ontextual-$\underline{\rm L}$SVI-$\underline{\rm U}$CB-$\underline{\rm B}$uffer (CLUB) algorithm which achieves $\tilde{ \mathcal{O}}(H^{5/2}\sqrt{K})$ revenue regret when the market noise is known and $\tilde{ \mathcal{O}}(H^{3}\sqrt{K})$ revenue regret when the noise is unknown with no assumptions on bidders' truthfulness.
Spherical Gaussian mixture model and object tracking system for PTZ camera

Recently, pan-tilt-zoom(PTZ) camera is widely used in extensive-area surveillance applications. A number of background modeling methods have been proposed within existing object detection and tracking systems. However, conventional background modeling methods for PTZ camera have difficulties in covering extensive field of view(FOV). This paper presents a novel object tracking system based on a spherical background model for PTZ camera. The proposed system has two components: The first one is the spherical Gaussian mixture model(S-GMM) that learns background for all the view angles in the PTZ camera. Also, Gaussian parameters in each pixel in the S-GMM are learned and updated. The second one is object tracking system with foreground detection using the S-GMM in real-time. The proposed system is suitable to cover wide FOV compared to a conventional background modeling system for PTZ camera, and is able to exactly track moving objects. We demonstrate the advantages of the proposed S-GMM for object tracking system using PTZ camera. Also, we expect to build a more advanced surveillance applications via the proposed system.
Arabic Word-level Readability Visualization for Assisted Text Simplification

This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization. The add-on includes a lemmatization component that is connected to a five-level readability lexicon and Arabic WordNet-based substitution suggestions. The add-on can be used for assessing the reading difficulty of a text and identifying difficult words as part of the task of manual text simplification. We make our add-on and its code publicly available.