Miracle-dz/UniFAR-FTUs huggingface dataset:
Agnostic black hole spectroscopy: Quasinormal mode content of numerical relativity waveforms and limits of validity of linear perturbation theory

Black hole spectroscopy is the program to measure the complex gravitational-wave frequencies of merger remnants, and to quantify their agreement with the characteristic frequencies of black holes computed at linear order in black hole perturbation theory. In a"weaker"(non-agnostic) version of this test, one assumes that the frequencies depend on the mass and spin of the final Kerr black hole as predicted in perturbation theory. Linear perturbation theory is expected to be a good approximation only at late times, when the remnant is close enough to a stationary Kerr black hole. However, it has been claimed that a superposition of overtones with frequencies fixed at their asymptotic values in linear perturbation theory can reproduce the waveform strain even at the peak. Is this overfitting, or are the overtones physically present in the signal? To answer this question, we fit toy models of increasing complexity, waveforms produced within linear perturbation theory, and full numerical relativity waveforms using both agnostic and non-agnostic ringdown models. We find that higher overtones are unphysical: their role is mainly to"fit away"features such as initial data effects, power-law tails, and (when present) nonlinearities. We then identify physical quasinormal modes by fitting numerical waveforms in the original, agnostic spirit of the no-hair test. We find that a physically meaningful ringdown model requires the inclusion of higher multipoles, quasinormal mode frequencies induced by spherical-spheroidal mode mixing, and nonlinear quasinormal modes. Even in this"infinite signal-to-noise ratio"version of the original spectroscopy test, there is convincing evidence for the first overtone of the dominant multipole only well after the peak of the radiation.
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations

In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations. The method utilizes an inherently interpretable algorithm, genetic programming based symbolic regression. Unlike conventional accuracy measures in machine learning we demonstrate the ability to recover true analytic solutions, as opposed to a numerical approximation. The method is verified by assessing its ability to recover known analytic solutions for two separate differential equations. The developed method is compared to a conventional, purely data-driven genetic programming based symbolic regression algorithm. The reliability of successful evolution of the true solution, or an algebraic equivalent, is demonstrated.
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.
The multi-wavelength view of shocks in the fastest nova V1674 Her

Classical novae are shock-powered multi-wavelength transients triggered by a thermonuclear runaway on an accreting white dwarf. V1674 Her is the fastest nova ever recorded (time to declined by two magnitudes is t2 = 1.1 d) that challenges our understanding of shock formation in novae. We investigate the physical mechanisms behind nova emission from GeV γ-rays to cm-band radio using coordinated Fermi-LAT, NuSTAR, Swift and VLA observations supported by optical photometry. Fermi-LAT detected short-lived (18 h) 0.1-100 GeV emission from V1674 Her that appeared 6 h after the eruption began; this was at a level of (1.6 ± 0.4) × 10−6 photons cm−2 s−1. Eleven days later, simultaneous NuSTAR and Swift X-ray observations revealed optically thin thermal plasma shock-heated to kTshock = 4 keV. The lack of a detectable 6.7 keV Fe Kα emission suggests super-solar CNO abundances. The radio emission from V1674 Her was consistent with thermal emission at early times and synchrotron at late times. The radio spectrum steeply rising with frequency may be a result of either free-free absorption of synchrotron and thermal emission by unshocked outer regions of the nova shell or the Razin-Tsytovich effect attenuating synchrotron emission in dense plasma. The development of the shock inside the ejecta is unaffected by the extraordinarily rapid evolution and the intermediate polar host of this nova.
First-Order Model Checking on Structurally Sparse Graph Classes

A class of graphs is structurally nowhere dense if it can be constructed from a nowhere dense class by a first-order transduction. Structurally nowhere dense classes vastly generalize nowhere dense classes and constitute important examples of monadically stable classes. We show that the first-order model checking problem is fixed-parameter tractable on every structurally nowhere dense class of graphs. Our result builds on a recently developed game-theoretic characterization of monadically stable graph classes. As a second key ingredient of independent interest, we provide a polynomial-time algorithm for approximating weak neighborhood covers (on general graphs). We combine the two tools into a recursive locality-based model checking algorithm. This algorithm is efficient on every monadically stable graph class admitting flip-closed sparse weak neighborhood covers, where flip-closure is a mild additional assumption. Thereby, establishing efficient first-order model checking on monadically stable classes is reduced to proving the existence of flip-closed sparse weak neighborhood covers on these classes -- a purely combinatorial problem. We complete the picture by proving the existence of the desired covers for structurally nowhere dense classes: we show that every structurally nowhere dense class can be sparsified by contracting local sets of vertices, enabling us to lift the existence of covers from sparse classes.
The satisficing secretary problem: when closed-form solutions meet simulated annealing

The secretary problem has been a focus of extensive study with a variety of extensions that offer useful insights into the theory of optimal stopping. The original solution is to set one stopping threshold that gives rise to an immediately rejected sample $r$ out of the candidate pool of size $n$ and to accept the first candidate that is subsequently interviewed and bests the prior $r$ rejected. In reality, it is not uncommon to draw a line between job candidates to distinguish those above the line vs those below the line. Here we consider such satisficing sectary problem that views suboptimal choices (finding any of the top $d$ candidates) also as hiring success. We use a multiple stopping criteria $(r_1, r_2, \cdots, r_d)$ that sequentially lowers the expectation when the prior selection criteria yields no choice. We calculate the probability of securing the top $d$ candidate in closed form solutions which are in excellent agreement with computer simulations. The exhaustive search for optimal stopping has to deal with large parameter space and thus can quickly incur astronomically large computational times. In light of this, we apply the effective computational method of simulated annealing to find optimal $r_i$ values which performs reasonably well as compared with the exhaustive search despite having significantly less computing time. Our work sheds light on maximizing the likelihood of securing satisficing (suboptimal) outcomes using adaptive search strategy in combination with computation methods.
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR

In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance $\tau$. Starting with multi-arm bandits (MABs), we show the minimax CVaR regret rate is $\Omega(\sqrt{\tau^{-1}AK})$, where $A$ is the number of actions and $K$ is the number of episodes, and that it is achieved by an Upper Confidence Bound algorithm with a novel Bernstein bonus. For online RL in tabular Markov Decision Processes (MDPs), we show a minimax regret lower bound of $\Omega(\sqrt{\tau^{-1}SAK})$ (with normalized cumulative rewards), where $S$ is the number of states, and we propose a novel bonus-driven Value Iteration procedure. We show that our algorithm achieves the optimal regret of $\widetilde O(\sqrt{\tau^{-1}SAK})$ under a continuity assumption and in general attains a near-optimal regret of $\widetilde O(\tau^{-1}\sqrt{SAK})$, which is minimax-optimal for constant $\tau$. This improves on the best available bounds. By discretizing rewards appropriately, our algorithms are computationally efficient.
S4R: Self-Supervised Semantic Scene Reconstruction from RGB-D Scans

Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction, using a fully self-supervised approach. To this end, we design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics. Our key technical innovation is to leverage differentiable rendering of color and semantics, using the ob-served RGB images and a generic semantic segmentation model as color and semantics supervision, respectively. We additionally develop a method to synthesize an augmented set of virtual training views complementing the original real captures, enabling more efﬁcient self-supervision for semantics. In this work we propose an end-to-end trainable
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning

Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity. Such problems prevent learners from fast adaptation since they may forget useful features or have difficulty learning new ones. Hence, these methods are rendered ineffective for continual learning. This paper proposes Utility-based Perturbed Gradient Descent (UPGD), an online learning algorithm well-suited for continual learning agents. UPGD protects useful weights or features from forgetting and perturbs less useful ones based on their utilities. Our empirical results show that UPGD helps reduce forgetting and maintain plasticity, enabling modern representation learning methods to work effectively in continual learning.
Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning

On end-to-end driving, a large amount of expert driving demonstrations is used to train an agent that mimics the expert by predicting its control actions. This process is self-supervised on vehicle signals ( e.g ., steering angle, acceleration) and does not require extra costly supervision (human labeling). Yet, the improvement of existing self-supervised end-to-end driving models has mostly given room to modu-lar end-to-end models where labeling data intensive format such as semantic segmentation are required during training time. However, we argue that the latest self-supervised end-to-end models were developed in sub-optimal conditions with low-resolution images and no attention mechanisms. Further, those models are conﬁned with limited ﬁeld of view and far from the human visual cognition which can quickly attend far-apart scene features, a trait that provides an use-ful inductive bias. In this context, we present a new end-to-end model, trained by self-supervised imitation learning, leveraging a large ﬁeld of view and a self-attention mechanism. These settings are more contributing to the agent’s understanding of the driving scene, which brings a better imitation of human drivers. With only self-supervised training data, our model yields almost expert performance in CARLA’s Nocrash metrics and could be rival to the SOTA models requiring large amounts of human labeled data. To facilitate further research, our code will be released.
"I am the follower, also the boss": Exploring Different Levels of Autonomy and Machine Forms of Guiding Robots for the Visually Impaired

Guiding robots, in the form of canes or cars, have recently been explored to assist blind and low vision (BLV) people. Such robots can provide full or partial autonomy when guiding. However, the pros and cons of different forms and autonomy for guiding robots remain unknown. We sought to fill this gap. We designed autonomy-switchable guiding robotic cane and car. We conducted a controlled lab-study (N=12) and a field study (N=9) on BLV. Results showed that full autonomy received better walking performance and subjective ratings in the controlled study, whereas participants used more partial autonomy in the natural environment as demanding more control. Besides, the car robot has demonstrated abilities to provide a higher sense of safety and navigation efficiency compared with the cane robot. Our findings offered empirical evidence about how the BLV community perceived different machine forms and autonomy, which can inform the design of assistive robots.
Multi-Task Deep Recommender Systems: A Survey

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.
Structured Generative Models for Scene Understanding

This position paper argues for the use of \emph{structured generative models} (SGMs) for scene understanding. This requires the reconstruction of a 3D scene from an input image, whereby the contents of the image are causally explained in terms of models of instantiated objects, each with their own type, shape, appearance and pose, along with global variables like scene lighting and camera parameters. This approach also requires scene models which account for the co-occurrences and inter-relationships of objects in a scene. The SGM approach has the merits that it is compositional and generative, which lead to interpretability. To pursue the SGM agenda, we need models for objects and scenes, and approaches to carry out inference. We first review models for objects, which include ``things'' (object categories that have a well defined shape), and ``stuff'' (categories which have amorphous spatial extent). We then move on to review \emph{scene models} which describe the inter-relationships of objects. Perhaps the most challenging problem for SGMs is \emph{inference} of the objects, lighting and camera parameters, and scene inter-relationships from input consisting of a single or multiple images. We conclude with a discussion of issues that need addressing to advance the SGM agenda.
Conformal measure rigidity for representations via self-joinings

Let $\Gamma$ be a Zariski dense discrete subgroup of a connected simple real algebraic group $G_1$. We discuss a rigidity problem for discrete faithful representations $\rho:\Gamma\to G_2$ and a surprising role played by higher rank conformal measures of the associated self-joining group. Our approach recovers rigidity theorems of Sullivan, Tukia and Yue, as well as applies to Anosov representations, including Hitchin representations. More precisely, for a given representation $\rho$ with a boundary map $f$ defined on the limit set $\Lambda$, we ask whether the extendability of $\rho$ to $G$ can be detected by the property that $f$ pushes forward some $\Gamma$-conformal measure class $[\nu_\Gamma]$ to a $\rho(\Gamma)$-conformal measure class $[\nu_{\rho(\Gamma)}]$. When $\Gamma$ is of divergence type in a rank one group or when $\rho$ arises from an Anosov representation, we give an affirmative answer by showing that if the self-joining $\Gamma_\rho=(\text{id} \times \rho)(\Gamma)$ is Zariski dense in $G_1\times G_2$, then the push-forward measures $(\text{id}\times f)_*\nu_\Gamma$ and $(f^{-1}\times \text{id})_*\nu_{\rho(\Gamma)}$, which are higher rank $\Gamma_\rho$-conformal measures, cannot be in the same measure class.
Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model

Over the past decade, programmatic advertising has received a great deal of attention in the online advertising industry. A real-time bidding (RTB) system is rapidly becoming the most popular method to buy and sell online advertising impressions. Within the RTB system, demand-side platforms (DSP) aim to spend advertisers' campaign budgets efficiently while maximizing profit, seeking impressions that result in high user responses, such as clicks or installs. In the current study, we investigate the process of predicting a mobile gaming app installation from the point of view of a particular DSP, while paying attention to user privacy, and exploring the trade-off between privacy preservation and model performance. There are multiple levels of potential threats to user privacy, depending on the privacy leaks associated with the data-sharing process, such as data transformation or de-anonymization. To address these concerns, privacy-preserving techniques were proposed, such as cryptographic approaches, for training privacy-aware machine-learning models. However, the ability to train a mobile gaming app installation prediction model without using user-level data, can prevent these threats and protect the users' privacy, even though the model's ability to predict may be impaired. Additionally, current laws might force companies to declare that they are collecting data, and might even give the user the option to opt out of such data collection, which might threaten companies' business models in digital advertising, which are dependent on the collection and use of user-level data. We conclude that privacy-aware models might still preserve significant capabilities, enabling companies to make better decisions, dependent on the privacy-efficacy trade-off utility function of each case.
Five policy uses of algorithmic transparency and explainability

The notion that algorithmic systems should be"transparent"and"explainable"is common in the many statements of consensus principles developed by governments, companies, and advocacy organizations. But what exactly do policy and legal actors want from these technical concepts, and how do their desiderata compare with the explainability techniques developed in the machine learning literature? In hopes of better connecting the policy and technical communities, we provide case studies illustrating five ways in which algorithmic transparency and explainability have been used in policy settings: specific requirements for explanations; in nonbinding guidelines for internal governance of algorithms; in regulations applicable to highly regulated settings; in guidelines meant to increase the utility of legal liability for algorithms; and broad requirements for model and data transparency. The case studies span a spectrum from precise requirements for specific types of explanations to nonspecific requirements focused on broader notions of transparency, illustrating the diverse needs, constraints, and capacities of various policy actors and contexts. Drawing on these case studies, we discuss promising ways in which transparency and explanation could be used in policy, as well as common factors limiting policymakers' use of algorithmic explainability. We conclude with recommendations for researchers and policymakers.
Sketching Robot Programs On the Fly

Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.
Adaptive Aggregation for Safety-Critical Control

Safety has been recognized as the central obstacle to preventing the use of reinforcement learning (RL) for real-world applications. Different methods have been developed to deal with safety concerns in RL. However, learning reliable RL-based solutions usually require a large number of interactions with the environment. Likewise, how to improve the learning efficiency, specifically, how to utilize transfer learning for safe reinforcement learning, has not been well studied. In this work, we propose an adaptive aggregation framework for safety-critical control. Our method comprises two key techniques: 1) we learn to transfer the safety knowledge by aggregating the multiple source tasks and a target task through the attention network; 2) we separate the goal of improving task performance and reducing constraint violations by utilizing a safeguard. Experiment results demonstrate that our algorithm can achieve fewer safety violations while showing better data efficiency compared with several baselines.