Miracle-dz/UniFAR-FTUs huggingface dataset:
29P/Schwassmann–Wachmann 1: A Rosetta Stone for Amorphous Water Ice and CO ↔ CO2 Conversion in Centaurs and Comets?

Centaur 29P/Schwassmann–Wachmann 1 (SW1) is a highly active object orbiting in the transitional “Gateway” region between the Centaur and Jupiter-family comet (JFC) regions. SW1 is unique among the Centaurs in that it experiences quasi-regular major outbursts and produces CO emission continuously; however, the source of the CO is unclear. We argue that, due to its very large size (∼32 km radius), SW1 is likely still responding, via amorphous water ice (AWI) conversion to crystalline water ice (CWI), to the “sudden” change in its external thermal environment produced by its Myrs-long dynamical migration from the Kuiper Belt to its current location at the inner edge of the Centaur region. It is this conversion process that is the source of the abundant CO and dust released from the object during its quiescent and outburst phases. If correct, these arguments have a number of important predictions testable via remote sensing and in situ spacecraft characterization, including the quick release on Myr timescales of CO from AWI conversion for any few kilometer-scale scattered disk Kuiper Belt Objects transiting into the inner system; that to date SW1 has only converted between 50% and 65% of its nuclear AWI to CWI; that volume changes on AWI conversion could have caused subsidence and cave-ins, but not significant mass wasting or crater loss; that SW1's coma should contain abundant amounts of CWI+CO2 “dust” particles; and that when SW1 transits into the inner system within the next 10,000 yr, it will be a very different kind of JFC.
Universal Proof Theory: Constructive Rules and Feasible Admissibility

Visser’s rules have an essential role in intermediate logics. They form a basis for the admissible rules of intuitionistic logic and any intermediate logic in which they are admissible. In this paper, we follow the universal proof theory program introduced and developed in [1, 2, 24, 25] to establish a connection between the form of the rules in a sequent calculus for an intuitionistic modal logic and the admissibility of Visser’s rules in that logic. More precisely, by investigating the form of the constructively acceptable rules, we first introduce a very general family of rules called the constructive rules. Then, defining a constructive sequent calculus as a calculus consisting of constructive rules and some basic modal rules, we prove that any constructive sequent calculus stronger than CK satisfying a mild technical condition, feasibly admits all Visser’s rules, i.e., there is a polynomial time algorithm that reads a proof of the premise of a Visser’s rule and provides a proof for its conclusion. This connection has two types of applications. On the positive side, it proves the feasible admissibility of Visser’s rules in the sequent system for several intuitionistic modal logics, including CK, IK, their extensions by the usual modal axioms of T , B, 4, 5, the modal axioms of bounded width and depth and the propositional lax logic. On the negative side, though, it shows that if an intuitionistic modal logic satisfying a mild technical condition does not admit Visser’s rules, then it cannot have a constructive sequent calculus.
Is Stochastic Gradient Descent Near Optimal?

The success of neural networks over the past decade has established them as effective models for many relevant data generating processes. Statistical theory on neural networks indicates graceful scaling of sample complexity. For example, Joen&Van Roy (arXiv:2203.00246) demonstrate that, when data is generated by a ReLU teacher network with $W$ parameters, an optimal learner needs only $\tilde{O}(W/\epsilon)$ samples to attain expected error $\epsilon$. However, existing computational theory suggests that, even for single-hidden-layer teacher networks, to attain small error for all such teacher networks, the computation required to achieve this sample complexity is intractable. In this work, we fit single-hidden-layer neural networks to data generated by single-hidden-layer ReLU teacher networks with parameters drawn from a natural distribution. We demonstrate that stochastic gradient descent (SGD) with automated width selection attains small expected error with a number of samples and total number of queries both nearly linear in the input dimension and width. This suggests that SGD nearly achieves the information-theoretic sample complexity bounds of Joen&Van Roy (arXiv:2203.00246) in a computationally efficient manner. An important difference between our positive empirical results and the negative theoretical results is that the latter address worst-case error of deterministic algorithms, while our analysis centers on expected error of a stochastic algorithm.
Modeling the flare in NGC 1097 from 1991 to 2004 as a tidal disruption event

In the Letter, interesting evidence is reported to support a central tidal disruption event (TDE) in the known AGN NGC 1097. Considering the motivations of TDE as one probable origination of emission materials of double-peaked broad emission lines and also as one probable explanation to changing-look AGN, it is interesting to check whether are there clues to support a TDE in NGC 1097, not only a changing-look AGN but also an AGN with double-peaked broad emission lines. Under the assumption that the onset of broad H$\alpha$ emission was due to a TDE, the 13years-long (1991-2004) variability of double-peaked broad H$\alpha$ line flux in NGC 1097 can be well predicted by theoretical TDE model, with a $(1-1.5){\rm M_\odot}$ main-sequence star tidally disrupted by the central BH with TDE model determined mass about $(5-8)\times10^7{\rm M_\odot}$. The results provide interesting evidence to not only support TDE-related origin of double-peaked broad line emission materials but also support TDE as an accepted physical explanation to physical properties of changing-look AGN.
Adaptive Attitude Control for Foldable Quadrotors

Recent quadrotors have transcended conventional designs, emphasizing more on foldable and reconfigurable bodies. The state of the art still focuses on the mechanical feasibility of such designs with limited discussions on the tracking performance of the vehicle during configuration switching. In this letter, we first present a common framework to analyse the attitude errors of a folding quadrotor via the theory of switched systems. We then employ this framework to investigate the attitude tracking performance for two case scenarios - one with a conventional geometric controller for precisely known system dynamics; and second, with our proposed morphology-aware adaptive controller that accounts for any modeling uncertainties and disturbances. Finally, we cater to the desired switching requirements from our stability analysis by exploiting the trajectory planner to obtain superior tracking performance while switching. Simulation results are presented that validate the proposed control and planning framework for a foldable quadrotor’s flight through a passageway.
Learning Distinct and Representative Modes for Image Captioning

Over the years, state-of-the-art (SoTA) image captioning methods have achieved promising results on some evaluation metrics (e.g., CIDEr). However, recent findings show that the captions generated by these methods tend to be biased toward the"average"caption that only captures the most general mode (a.k.a, language pattern) in the training corpus, i.e., the so-called mode collapse problem. Affected by it, the generated captions are limited in diversity and usually less informative than natural image descriptions made by humans. In this paper, we seek to avoid this problem by proposing a Discrete Mode Learning (DML) paradigm for image captioning. Our innovative idea is to explore the rich modes in the training caption corpus to learn a set of"mode embeddings", and further use them to control the mode of the generated captions for existing image captioning models. Specifically, the proposed DML optimizes a dual architecture that consists of an image-conditioned discrete variational autoencoder (CdVAE) branch and a mode-conditioned image captioning (MIC) branch. The CdVAE branch maps each image caption to one of the mode embeddings stored in a learned codebook, and is trained with a pure non-autoregressive generation objective to make the modes distinct and representative. The MIC branch can be simply modified from an existing image captioning model, where the mode embedding is added to the original word embeddings as the control signal. In the experiments, we apply the proposed DML to two widely used image captioning models, Transformer and AoANet. The results show that the learned mode embedding successfully facilitates these models to generate high-quality image captions with different modes, further leading to better performance for both diversity and quality on the MSCOCO dataset.
Necessity specifications for robustness

Robust modules guarantee to do only what they are supposed to do – even in the presence of untrusted malicious clients, and considering not just the direct behaviour of individual methods, but also the emergent behaviour from calls to more than one method. Necessity is a language for specifying robustness, based on novel necessity operators capturing temporal implication, and a proof logic that derives explicit robustness specifications from functional specifications. Soundness and an exemplar proof are mechanised in Coq.
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at https://github.com/uyzhang/JSeg (Jittor) and https://github.com/Visual-Attention-Network/SegNeXt (Pytorch).
The Chocolate Chip Cookie Model: Dust Geometry of Milky Way–like Disk Galaxies

We present a new two-component dust geometry model, the Chocolate Chip Cookie model, where the clumpy nebular regions are embedded in a diffuse stellar/interstellar medium disk, like chocolate chips in a cookie. By approximating the binomial distribution of the clumpy nebular regions with a continuous Gaussian distribution and omitting the dust scattering effect, our model solves the dust attenuation process for both the emission lines and stellar continua via analytical approaches. Our Chocolate Chip Cookie model successfully fits the inclination dependence of both the effective dust reddening of the stellar components derived from stellar population synthesis and that of the emission lines characterized by the Balmer decrement for a large sample of Milky Way–like (MW-like) disk galaxies selected from the main galaxy sample of the Sloan Digital Sky Survey. Our model shows that the clumpy nebular disk is about 0.55 times thinner and 1.6 times larger than the stellar disk for MW-like galaxies, whereas each clumpy region has a typical optical depth of τ cl,V ∼ 0.5 in the V band. After considering the aperture effect, our model prediction on the inclination dependence of dust attenuation is also consistent with observations. Not only that, in our model, the dust attenuation curve of the stellar population naturally depends on the inclination, and its median case is consistent with the classical Calzetti law. As the modeling constraints are from the optical wavelengths, our model is unaffected by the optically thick dust component, which however could bias the model’s prediction of the infrared emissions.
Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition

Spiking neural networks have significant potential utility in robotics due to their high energy efficiency on specialized hardware, but proof-of-concept implementations have not yet typically achieved competitive performance or capability with conventional approaches. In this paper, we tackle one of the key practical challenges of scalability by introducing a novel modular ensemble network approach, where compact, localized spiking networks each learn and are solely responsible for recognizing places in a local region of the environment only. This modular approach creates a highly scalable system. However, it comes with a high-performance cost where a lack of global regularization at deployment time leads to hyperactive neurons that erroneously respond to places outside their learned region. Our second contribution introduces a regularization approach that detects and removes these problematic hyperactive neurons during the initial environmental learning phase. We evaluate this new scalable modular system on benchmark localization datasets Nordland and Oxford RobotCar, with comparisons to standard techniques NetVLAD, DenseVLAD, and SAD, and a previous spiking neural network system. Our system substantially outperforms the previous SNN system on its small dataset, but also maintains performance on 27 times larger benchmark datasets where the operation of the previous system is computationally infeasible, and performs competitively with the conventional localization systems.
Membership Inference Attacks and Generalization: A Causal Perspective

Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are they a natural consequence of imperfect generalization only? Which underlying causes should we address during training to mitigate these attacks? Towards answering such questions, we propose the first approach to explain MI attacks and their connection to generalization based on principled causal reasoning. We offer causal graphs that quantitatively explain the observed MI attack performance achieved for 6 attack variants. We refute several prior non-quantitative hypotheses that over-simplify or over-estimate the influence of underlying causes, thereby failing to capture the complex interplay between several factors. Our causal models also show a new connection between generalization and MI attacks via their shared causal factors. Our causal models have high predictive power (0.90), i.e., their analytical predictions match with observations in unseen experiments often, which makes analysis via them a pragmatic alternative.
Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood

The computational prediction of wave propagation in dam-break floods is a conventional numerical models based on Saint-Venant equations are the dominant approaches. Here we show that a machine learning model that is well-trained on a minimal amount of data, can help predict the long-term dynamic behavior of a one-dimensional dam-break flood with satisfactory accuracy. For this purpose, we solve the Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax-Wendroff numerical scheme and train the reservoir computing echo state network (RC-ESN) with the dataset by the simulation results consisting of time-sequence flow depths. We demonstrate a good prediction ability of the RC-ESN model, which ahead predicts wave propagation behavior 286 time-steps in the dam-break flood with a root mean square error (RMSE) smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model which reaches a comparable RMSE of only 81 time-steps ahead. To show the performance of the RC-ESN model, we also provide a sensitivity analysis of the prediction accuracy concerning the key parameters including training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN are less dependent on the training set size, a medium reservoir size � = 1200~2600 is sufficient. We confirm that the spectral radius � shows a complex influence on the prediction accuracy and suggest a smaller spectral radius � currently. By changing the initial flow depth of dam break, we also obtained the conclusion that the prediction horizon of RC-ESN is larger than that of LSTM.
n-Tuple relations and topologies on function spaces

In some results concerning S-splitting, S-jointly continuous, D-splitting and D-jointly continuous topologies are considered, where S and D are the Sierpinski space and the double-point space, respectively. Here we generalize these results replacing the spaces S and D by any finite space.
Quest for graphs of Frank number 3

In an orientation $O$ of the graph $G$, the edge $e$ is deletable if and only if $O-e$ is strongly connected. For a $3$-edge-connected graph $G$, H\"orsch and Szigeti defined the Frank number as the minimum $k$ for which $G$ admits $k$ orientations such that every edge $e$ of $G$ is deletable in at least one of the $k$ orientations. They conjectured the Frank number is at most $3$ for every $3$-edge-connected graph $G$. They proved the Petersen graph has Frank number $3$, but this was the only example with this property. We show an infinite class of graphs having Frank number $3$. H\"orsch and Szigeti showed every $3$-edge-colorable $3$-edge-connected graph has Frank number at most $3$. It is tempting to consider non-$3$-edge-colorable graphs as candidates for having Frank number greater than $2$. Snarks are sometimes a good source of finding critical examples or counterexamples. One might suspect various snarks should have Frank number $3$. However, we prove several candidate infinite classes of snarks have Frank number $2$. As well as the generalized Petersen Graphs $GP(2s+1,s)$. We formulate numerous conjectures inspired by our experience.
Ding stability and K\"ahler-Einstein metrics on manifolds with big anticanonical class

We introduce a notion of uniform Ding stability for a projective manifold with big anticanonical class, and prove that the existence of a unique K\"ahler-Einstein metric on such a manifold implies uniform Ding stability. The main new techniques are to develop a general theory of Deligne functionals-and corresponding slope formulas-for singular metrics, and to prove a slope formula for the Ding functional in the big setting. This extends work of Berman in the Fano situation, when the anticanonical class is actually ample, and proves one direction of the analogue of the Yau-Tian-Donaldson conjecture in this setting. We also speculate about the relevance of uniform Ding stability and K-stability to moduli in the big setting.
Bumpless Topology Transition

The topology transition problem of transmission networks is becoming increasingly crucial with topological flexibility more widely leveraged to promote high renewable penetration. This paper proposes a novel methodology to address this problem. Aiming at achieving a bumpless topology transition regarding both static and dynamic performance, this methodology utilizes various eligible control resources in transmission networks to cooperate with the optimization of line-switching sequence. Mathematically, a composite formulation is developed to efficiently yield bumpless transition schemes with AC feasibility and stability both ensured. With linearization of all non-convexities involved and tractable bumpiness metrics, a convex mixed-integer program firstly optimizes the line-switching sequence and partial control resources. Then, two nonlinear programs recover AC feasibility, and optimize the remaining control resources by minimizing the $\mathcal {H}_{2}$-norm of associated linearized systems, respectively. The final transition scheme is selected by accurate evaluation including stability verification using time-domain simulations. Finally, numerical studies demonstrate the effectiveness and superiority of the proposed methodology to achieve bumpless topology transition.
Latent Plans for Task-Agnostic Offline Reinforcement Learning

Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still impose a major challenge in offline robot control. While a number of prior methods aimed to address this setting with variants of imitation and offline reinforcement learning, the learned behavior is typically narrow and often struggles to reach configurable long-horizon goals. As both paradigms have complementary strengths and weaknesses, we propose a novel hierarchical approach that combines the strengths of both methods to learn task-agnostic long-horizon policies from high-dimensional camera observations. Concretely, we combine a low-level policy that learns latent skills via imitation learning and a high-level policy learned from offline reinforcement learning for skill-chaining the latent behavior priors. Experiments in various simulated and real robot control tasks show that our formulation enables producing previously unseen combinations of skills to reach temporally extended goals by"stitching"together latent skills through goal chaining with an order-of-magnitude improvement in performance upon state-of-the-art baselines. We even learn one multi-task visuomotor policy for 25 distinct manipulation tasks in the real world which outperforms both imitation learning and offline reinforcement learning techniques.
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data

In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexity, previous results studied a relaxed setting where the server has access to some additional public but unlabeled data. We continue in this direction. Specifically, we consider the problem under the standard setting instead of the large margin setting studied before. Under different mild assumptions on the underlying data distribution, we propose two approaches that are based on the Massart noise model and self-supervised learning and show that it is possible to achieve sample complexities that are only linear in the dimension and polynomial in other terms for both private and public data, which significantly improve the previous results. Our methods could also be used for other private PAC learning problems.
Neural Wavelet-domain Diffusion for 3D Shape Generation

This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets, and formulate a pair of neural networks: a generator based on the diffusion model to produce diverse shapes in the form of coarse coefficient volumes; and a detail predictor to further produce compatible detail coefficient volumes for enriching the generated shapes with fine structures and details. Both quantitative and qualitative experimental results manifest the superiority of our approach in generating diverse and high-quality shapes with complex topology and structures, clean surfaces, and fine details, exceeding the 3D generation capabilities of the state-of-the-art models.