Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Embedded Contact Homology in Morse-Bott Settings

Given a contact three manifold Y with a nondegenerate contact form λ, and an almost complex structure J compatible with λ, its embedded contact homology ECH(Y, λ) is defined ([Hut14]) and only depends on the contact structure. In this paper we explain how to compute ECH for Morse-Bott contact forms whose Reeb orbits appear in S families, assuming the almost complex structure J can be chosen to satisfy certain transversality conditions (this is the case for instance for boundaries of concave or convex toric domains, or if all the curves of ECH index one have genus zero). We define the ECH chain complex for a Morse-Bott contact form via an enumeration of ECH index one cascades. We prove using gluing results from [Yao22] that this chain complex computes the ECH of the contact manifold. This paper and [Yao22] fill in some technical foundations for previous calculations in the literature ([Cho16], [HS06]).
Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning

We study the problem of learning good heuristic functions for classical planning tasks with neural networks based on samples represented by states with their cost-to-goal estimates. The heuristic function is learned for a state space and goal condition with the number of samples limited to a fraction of the size of the state space, and must generalize well for all states of the state space with the same goal condition. Our main goal is to better understand the influence of sample generation strategies on the performance of a greedy best-first heuristic search (GBFS) guided by a learned heuristic function. In a set of controlled experiments, we find that two main factors determine the quality of the learned heuristic: which states are included in the sample set and the quality of the cost-to-goal estimates. These two factors are dependent: having perfect cost-to-goal estimates is insufficient if the samples are not well distributed across the state space. We also study other effects, such as adding samples with high-value estimates. Based on our findings, we propose practical strategies to improve the quality of learned heuristics: three strategies that aim to generate more representative states and two strategies that improve the cost-to-goal estimates. Our practical strategies almost double the mean coverage of a GBFS algorithm guided by a learned heuristic.
Differentially Private Image Classification from Features

Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent works have found that privately training just the last layer of a pre-trained model provides the best utility with DP. While past studies largely rely on algorithms like DP-SGD for training large models, in the specific case of privately learning from features, we observe that computational burden is low enough to allow for more sophisticated optimization schemes, including second-order methods. To that end, we systematically explore the effect of design parameters such as loss function and optimization algorithm. We find that, while commonly used logistic regression performs better than linear regression in the non-private setting, the situation is reversed in the private setting. We find that linear regression is much more effective than logistic regression from both privacy and computational aspects, especially at stricter epsilon values ($\epsilon<1$). On the optimization side, we also explore using Newton's method, and find that second-order information is quite helpful even with privacy, although the benefit significantly diminishes with stricter privacy guarantees. While both methods use second-order information, least squares is effective at lower epsilons while Newton's method is effective at larger epsilon values. To combine the benefits of both, we propose a novel algorithm called DP-FC, which leverages feature covariance instead of the Hessian of the logistic regression loss and performs well across all $\epsilon$ values we tried. With this, we obtain new SOTA results on ImageNet-1k, CIFAR-100 and CIFAR-10 across all values of $\epsilon$ typically considered. Most remarkably, on ImageNet-1K, we obtain top-1 accuracy of 88\% under (8, $8 * 10^{-7}$)-DP and 84.3\% under (0.1, $8 * 10^{-7}$)-DP.
Adaptive Attention Link-based Regularization for Vision Transformers

Although transformer networks are recently employed in var- ious vision tasks with outperforming performance, extensive training data and a lengthy training time are required to train a model to disregard an inductive bias. Using trainable links between the channel-wise spatial attention of a pre-trained Con- volutional Neural Network (CNN) and the attention head of Vision Transformers (ViT), we present a regularization tech- nique to improve the training efﬁciency of ViT. The trainable links are referred to as the attention augmentation module, which is trained simultaneously with ViT, boosting the training of ViT and allowing it to avoid the overﬁtting issue caused by a lack of data. From the trained attention augmentation module, we can extract the relevant relationship between each CNN activation map and each ViT attention head, and based on this, we also propose an advanced attention augmentation module. Consequently, even with a small amount of data, the suggested method considerably improves the performance of ViT while achieving faster convergence during training.
Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image

A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, namely Generative Radiance Manifolds (GRAM) [13], which has shown strong 3D consistency at multiview image generation of virtual subjects via the radiance manifolds representation. However, simply learning an encoder to map a real image into the latent space of GRAM can only reconstruct coarse radiance manifolds without faithful fine details, while improving the reconstruction fidelity via instance-specific optimization is time-consuming. We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction. The 3D priors derived from the coarse radiance manifolds are used to regulate the learned details to ensure reasonable synthesized results at novel views. Trained on in-the-wild 2D images, our method achieves high-fidelity and 3D-consistent portrait synthesis largely outperforming the prior art. Project page: https://yudeng.github. io/GRAMInverter/
Stability for the Sobolev inequality: existence of a minimizer

We prove that the stability inequality associated to Sobolev's inequality and its set of optimizers $\mathcal M$ and given by \[ \frac{\|\nabla f\|_{L^2(\mathbb R^d)}^2 - S_d \|f\|_{L^\frac{2d}{d-2}(\mathbb R^d)}^2}{ \inf_{h \in \mathcal M} \|\nabla (f - h)\|_{L^2(\mathbb R^d)}^2 } \geq c_{BE}>0 \qquad \text{ for every } f \in \dot{H}^1(\mathbb R^d),\] which is due to Bianchi and Egnell, admits a minimizer for every $d \geq 3$. Our proof consists in an appropriate refinement of a classical strategy going back to Brezis and Lieb. As a crucial ingredient, we establish the strict inequality $c_{BE}<2 - 2^\frac{d-2}{d}$, which means that a sequence of two asymptotically non-interacting bubbles cannot be minimizing. Our arguments cover in fact the analogous stability inequality for the fractional Sobolev inequality for arbitrary fractional exponent $s \in (0, d/2)$ and dimension $d \geq 2$.
Water and an Escaping Helium Tail Detected in the Hazy and Methane-depleted Atmosphere of HAT-P-18b from JWST NIRISS/SOSS

JWST is here. The early release observation (ERO) program provides us with the first look at the scientific data and the spectral capabilities. One of the targets from the ERO is HAT-P-18b, an inflated Saturn-mass planet with an equilibrium temperature of ∼850 K. We present the NIRISS/SOSS transmission spectrum of HAT-P-18b from 0.6 to 2.8 μm and reveal the planet in the infrared beyond 1.6 μm for the first time. From the spectrum, we see clear water and escaping helium tail features in an otherwise very hazy atmosphere. Our free chemistry retrievals with ATMO show moderate Bayesian evidence (3.79) supporting the presence of methane, but the spectrum does not display any clearly identifiable methane absorption features. The retrieved methane abundance is ∼2 orders of magnitude lower than that of solar composition. The methane-depleted atmosphere strongly rejects simple equilibrium chemistry forward models with solar metallicity and a C/O ratio and disfavors high metallicity (100 times) and a low C/O ratio (0.3). This calls for additional physical processes such as vertical mixing and photochemistry, which can remove methane from the atmosphere.
Some Applications of Affine in Velocities Lagrangians in Two-Dimensional Systems

The two-dimensional inverse problem for first-order systems is analysed and a method to construct an affine Lagrangian for such systems is developed. The determination of such Lagrangians is based on the theory of the Jacobi multiplier for the system of differential equations. We illustrate our analysis with several examples of families of forces that are relevant in mechanics, on one side, and of some relevant biological systems, on the other.
Acoustic emission amplitude distribution during the martensitic transformation of Cu-Zn-Al alloys

The acoustic emission generated during the forward and reverse martensitic transformations of a Cu-Zn-Al alloy has been studied using the ring-down counting technique. A method is proposed to determine from ring-down counting data, the amplitude distribution of the signals generated during the transformation. The results obtained are compared with those given in the literature.
Diffraction by a Right-Angled No-Contrast Penetrable Wedge: Analytical Continuation of Spectral Functions

We study the problem of diffraction by a right-angled no-contrast penetrable wedge by means of a two-complex-variable Wiener–Hopf approach. Specifically, the analyticity properties of the unknown (spectral) functions of the two-complex-variable Wiener–Hopf equation are studied. We show that these spectral functions can be analytically continued onto a two-complex dimensional manifold, and unveil their singularities in C2. To do so, integral representation formulae for the spectral functions are given and thoroughly used. It is shown that the novel concept of additive crossing holds for the penetrable wedge diffraction problem, and that we can reformulate the physical diffraction problem as a functional problem using this concept.
A Strong Baseline for Generalized Few-Shot Semantic Segmentation

This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes. With a simple training process, our inference model can be applied on top of any segmentation network trained on base classes. The proposed inference yields substantial improvements on the popular few-shot segmentation benchmarks, PASCAL-5i and COCO-20i. Particularly, for novel classes, the improvement gains range from 7% to 26% (PASCAL-5i) and from 3% to 12% (COCO-20i) in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a more challenging setting, where performance gaps are further exacerbated. Our code is publicly available at https://github.com/sinahmr/DIaM.
A hybrid model for the dark sector

Diverse cosmological and astrophysical observations strongly hint at the presence of dark matter and dark energy in the Universe. One of the main goals of Cosmology is to explain the nature of these two components. It may well be that both dark matter and dark energy have a common origin. In this paper, we develop a model in which the dark sector arises due to an interplay between two interacting scalar ﬁelds. Employing a hybrid inﬂation potential, we show that the model can be described as a system of a pressureless ﬂuid coupled to a light scalar ﬁeld. We discuss this setup’s cosmological consequences and the observational signatures in the cosmic microwave background radiation and the large-scale structures.
Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods

Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text. We describe our entity recognition methods developed as part of the DEAL (Detecting Entities in the Astrophysics Literature) shared task. The aim of the task is to build a system that can identify Named Entities in a dataset composed by scholarly articles from astrophysics literature. We planned our participation such that it enables us to conduct an empirical comparison between word-based tagging and span-based classification methods. When evaluated on two hidden test sets provided by the organizer, our best-performing submission achieved F1 scores of 0.8307 (validation phase) and 0.7990 (testing phase).
Do all Sun-like stars have planets? Inferences from the disc mass reservoirs of Class 0 protostars

Protostars ∼0.1 Myr old are heavily obscured, but their circumstellar dust discs can be studied by millimetre interferometry that resolves out the obscuring envelope. Consistent estimates are made for the disc masses of Class 0 protostars, and these range over 7–660 MJup. A simple grain coagulation model reproduces the mass distributions of more evolved discs around Class I protostars and Class II T Tauri stars (at ≲1, ≲5 Myr), implying that their observed dust is remnant material. The conversion of most dust grains into planetesimals thus occurs very early, at ∼0.1 Myr. As this is concurrent with the formation of the star itself, much of the disc is expected to accrete on to the central object, and a correlation of bolometric luminosity and disc mass is observed that agrees well with disc models. The observed Class 0 discs all contain upwards of 20 M of dust, allowing for the formation of ‘super-Earths’ around many Sun-like stars. Only 10 per cent of the dust mass needs to be converted into planetary cores to match the range of such core masses presently known.
Rebuttal to Bell non-locality

Bell’s theorem is habitually used as an advertisement for non-local formulations of quantum mechanics and the statement that local realist interpretations are untenable. We identify four categories of interpretations that are permissible by Bell’s theorem. Many local and deterministic descriptions remain seemingly ignored. For three of those categories, we present an example of an interpretation where a local ﬂow of quantum information is possible. We assess whether current experimental proposals and an improved philosophy of science can contrast interpretations and distinguish between them.
Better Trees for Santa Claus

We revisit the problem max-min degree arborescence, which was introduced by Bateni et al. [STOC’09] as a central special case of the general Santa Claus problem, which constitutes a notorious open question in approximation algorithms. In the former problem we are given a directed graph with sources and sinks and our goal is to find vertex disjoint arborescences rooted in the sources such that at each non-sink vertex of an arborescence the out-degree is at least k, where k is to be maximized. This problem is of particular interest, since it appears to capture much of the difficulty of the Santa Claus problem: (1) like in the Santa Claus problem the configuration LP has a large integrality gap in this case and (2) previous progress by Bateni et al. was quickly generalized to the Santa Claus problem (Chakrabarty et al. [FOCS’09]). These results remain the state-of-the-art both for the Santa Claus problem and for max-min degree arborescence and they yield a polylogarithmic approximation in quasi-polynomial time. We present an exponential improvement to this, a poly(loglogn)-approximation in quasi-polynomial time for the max-min degree arborescence problem. To the best of our knowledge, this is the first example of breaking the logarithmic barrier for a special case of the Santa Claus problem, where the configuration LP cannot be utilized. The main technical novelty of our result are locally good solutions: informally, we show that it suffices to find a poly(logn)-approximation that locally has stronger guarantees. We use a lift-and-project type of LP and randomized rounding, which were also used by Bateni et al., but unlike previous work we integrate careful pruning steps in the rounding. In the proof we extensively apply Lovász Local Lemma and a local search technique, both of which were previously used only in the context of the configuration LP.
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt

Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. Specifically, we first introduce a novel pretraining objective to generate free text diagnoses and procedures using the SOAP structure, the medical logic physicians use for note documentation. Second, instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions, which then infers ICD codes. Third, we designed a novel prompt template for multi-label classification. We evaluate our Generation with Prompt (GPsoap) model with the benchmark of all code assignment (MIMIC-III-full) and few shot ICD code assignment evaluation benchmark (MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with a marco F130.2, which substantially outperforms the previous MIMIC-III-full SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross-attention reranker with prompts, to integrate previous SOTA and our best few-shot coding predictions. Experiments on MIMIC-III-full show that our ensemble learner substantially improves both macro and micro F1, from 10.4 to 14.6 and from 58.2 to 59.1, respectively.
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction

Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain. Due to this limitation, model scalability is constrained as well as reconstruction performance. Towards addressing the challenge, we introduce Mesh Pre-Training (MPT), an effective pre-training strategy that leverages large amounts of MoCap data to effectively perform pre-training at scale. We introduce the use of MoCap-generated heatmaps as input representations to the mesh regression transformer and propose a Masked Heatmap Modeling approach for improving pre-training performance. This study demonstrates that pre-training using the proposed MPT allows our models to perform effective inference without requiring fine-tuning. We further show that fine-tuning the pre-trained MPT model considerably improves the accuracy of human mesh reconstruction from single images. Experimental results show that MPT outperforms previous state-of-the-art methods on Human3.6M and 3DPW datasets. As a further application, we benchmark and study MPT on the task of 3D hand reconstruction, showing that our generic pre-training scheme generalizes well to hand pose estimation and achieves promising reconstruction performance.
MPCViT: Searching for MPC-friendly Vision Transformer with Heterogeneous Attention

—Secure multi-party computation (MPC) enables computation directly on encrypted data on non-colluding untrusted servers and protects both data and model privacy in deep learning inference. However, existing neural network (NN) architectures, including Vision Transformers (ViTs), are not designed or optimized for MPC protocols and incur signiﬁcant latency overhead due to the Softmax function in the multi-head attention (MHA). In this paper, we propose an MPC-friendly ViT, dubbed MPCViT, to enable accurate yet efﬁcient ViT inference in MPC. We systematically compare different attention variants in MPC and propose a heterogeneous attention search space, which combines the high-accuracy and MPC-efﬁcient attentions with diverse structure granularities. We further propose a simple yet effective differentiable neural architecture search (NAS) algorithm for fast ViT optimization. MPCViT signiﬁcantly outperforms prior-art ViT variants in MPC. With the proposed NAS algorithm, our extensive experiments demonstrate that MPCViT achieves 7.9 × and 2.8 × latency reduction with better accuracy compared to Linformer and MPCFormer on the Tiny-ImageNet dataset, respectively. Further, with proper knowledge distillation (KD), MPCViT even achieves 1.9% better accuracy compared to the baseline ViT with 9.9 × latency reduction on the Tiny-ImageNet dataset.
CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully exploited the correlation and complementarity between the two tasks of scene completion and semantic segmentation. In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. Specifically, we design (i) a global completion module (GCM) to produce an upsampled and completed but coarse point set, (ii) a semantic segmentation module (SSM) to predict the per-point semantic labels of the completed points generated by GCM, and (iii) a local refinement module (LRM) to further refine the coarse completed points and the associated labels from a local perspective. We organize the above three modules via dense feature fusion in each level, and cascade a total of four levels, where we also employ feature fusion between each level for sufficient information usage. Both quantitative and qualitative results on our compiled two point-based datasets validate the effectiveness and superiority of our CasFusionNet compared to state-of-the-art methods in terms of both scene completion and semantic segmentation. The codes and datasets are available at: https://github.com/JinfengX/CasFusionNet.