Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitfalls of Climate Network Construction: A Statistical Perspective

Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behaviour as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has major impact on network characteristics. When the data has locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 reanalysis data. Moreover we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future.
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification

In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image. In particular, we firstly evaluate different low complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2, NASNetMobile, and EfficientNetB0, which present a number of trainable parameters lower than 5 Million (M) or occupy 20 MB memory. After indicating the best network architecture, we further improve the network performance by applying attention schemes to multiple feature maps extracted from middle layers of the network. To deal with the issue of increasing the model footprint due to using attention schemes, we apply the quantization technique to satisfy the maximum memory occupation of 20 MB. By conducting extensive experiments on the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity model, which is very competitive to the state-of-the-art systems and potential for real-life applications on edge devices.
The \alpha-Invariant on Toric Fano Manifolds

The global holomorphic \alpha-invariant introduced by Tian is closely related with the study in the existence of Kahler-Einstein metric. We apply the result of Tian, Lu and Zelditch on polarized Kahler metrics to approximate plurisubharmonic functions and compute the \alpha-invariant of toric Fano manifolds.
General Solution to Sequential Linear Conformable Fractional Differential Equations With Constant Coefficients

In this work, we give the general solution sequential linear conformable fractional differential equations in the case of constant coefficients for α ∈ (0,1]. In homogeneous case, we use a fractional exponential function which generalizes the corresponding ordinary function. In non-homogeneous case, we present to fractional the method of variation of parameters for a particular solution of sequential linear conformable fractional differential equations. MSC: 26A33
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets

Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study’s contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges. Speciﬁcally, we conducted a series of experiments with these objectives; a ) generating a realistic synthetic dataset, b ) simulating data missingness, c ) recovering the missing data, and d ) analyzing imputation performance. Our methodology used a gaussian mixture model whose parameters were learned from a cleaned subset of a real demographic and health dataset to generate the synthetic data. We simulated various missingness degrees ranging from 10% , 20% , 30% , and 40% under the missing completely at random scheme MCAR. We used an integrated performance analysis framework involving clustering, classiﬁcation and direct imputation analysis. Our results show that models trained on synthetic and imputed datasets could make predictions with an accuracy of 83% and 80% on a ) an unseen real dataset and b ) an unseen reserved synthetic test dataset, respectively. Moreover, the models that used the DAE method for imputation yielded the lowest log loss an indication of good performance, even though the accuracy measures were slightly lower. In conclusion, our work demonstrates that using our methodology, one can reverse engineer a solution to resolve missingness on an unseen dataset with missingness. Moreover, though we used a health dataset, our methodology can be utilized in other contexts.
Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.
Limit laws in the lattice problem. V. The case of analytic and stricly convex sets

We study the error of the number of points of a unimodular lattice that fall in a strictly convex and analytic set having the origin and that is dilated by a factor $t$. The aim is to generalize the result of a previous article. We first show that the study of the error, when it is normalized by $\sqrt{t}$, when this parameter tends to infinity and when the considered lattice is random, is reduced to the study of a Siegel transform $\mathcal{S}(f_{t})(L)$ which depends on $t$. Then, we come back to the study of the asymptotic behaviour of a Siegel transform with random weights, $\mathcal{S}(F)(\theta,L)$ where $\theta$ is a second random parameter. Then, we show that this last quantity converges almost surely and we study the existence of moments of its law. Finally, we show that this result is still valid if we translate, after dilation, the strictly convex set of a fixed vector $\alpha \in \mathbb{R}^{2}$.
Laboratory observation of ion acceleration via reflection off laser-produced magnetized collisionless shocks

Fermi acceleration by collisionless shocks is believed to be the primary mechanism to produce high energy charged particles in the Universe,where charged particles gain energy successively from multiple reflections off the shock front.Here,we present the first direct experimental evidence of ion energization from reflection off a supercritical quasi perpendicular collisionless shock,an essential component of Fermi acceleration in a laser produced magnetized plasma. We observed a quasi monoenergetic ion beam with 2,4 times the shock velocity in the upstream flow using time of flight method. Our related kinetic simulations reproduced the energy gain and showed that these ions were first reflected and then accelerated mainly by the motional electric field associated with the shock. This mechanism can also explain the quasi monoenergetic fast ion component observed in the Earth's bow shock.
Towards learning to explain with concept bottleneck models: mitigating information leakage

Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction. Model trust issues emerge in this paradigm when soft concept labels are used: it has previously been observed that extra information about the data distribution leaks into the concept predictions. In this work we show how Monte-Carlo Dropout can be used to attain soft concept predictions that do not contain leaked information.
Ultracool dwarfs in Gaia DR3

Aims. In this work we use the Gaia DR3 set of ultracool dwarf candidates and complement the Gaia spectrophotometry with additional photometry in order to characterise its global properties. This includes the inference of the distances, their locus in the Gaia colour-absolute magnitude diagram and the (biased through selection) luminosity function in the faint end of the Main Sequence. We study the overall changes in the Gaia RP spectra as a function of spectral type. We study the UCDs in binary systems, attempt to identify low-mass members of nearby young associations, star forming regions and clusters, and analyse their variability properties. Results. We detect 57 young, kinematically homogeneous groups some of which are identified as well known star forming regions, associations and clusters of different ages. We find that the primary members of 880 binary systems with a UCD belong mainly to the thin and thick disk components of the Milky Way. We identify 1109 variable UCDs using the variability tables in the Gaia archive, 728 of which belong to the star forming regions defined by HMAC. We define two groups of variable UCDs with extreme bright or faint outliers. Conclusions. The set of sources identified as UCDs in the Gaia archive contains a wealth of information that will require focused follow-up studies and observations. It will help to advance our understanding of the nature of the faint end of the Main Sequence and the stellar/substellar transition.
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing

Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem. This work presents an end-to-end, learning-based inverse rendering framework incorporating differentiable Monte Carlo raytracing with importance sampling. The framework takes a single image as input to jointly recover the underlying geometry, spatially-varying lighting, and photorealistic materials. Specifically, we introduce a physically-based differentiable rendering layer with screen-space ray tracing, resulting in more realistic specular reflections that match the input photo. In addition, we create a large-scale, photorealistic indoor scene dataset with significantly richer details like complex furniture and dedicated decorations. Further, we design a novel out-of-view lighting network with uncertainty-aware refinement leveraging hypernetwork-based neural radiance fields to predict lighting outside the view of the input photo. Through extensive evaluations on common benchmark datasets, we demonstrate superior inverse rendering quality of our method compared to state-of-the-art baselines, enabling various applications such as complex object insertion and material editing with high fidelity. Code and data will be made available at https://jingsenzhu.github. io/invrend
Epeak ESTIMATOR FOR GAMMA-RAY BURSTS OBSERVED BY THE SWIFT BURST ALERT TELESCOPE

We report a correlation based on a spectral simulation study of the prompt emission spectra of gamma-ray bursts (GRBs) detected by the Swift Burst Alert Telescope (BAT). The correlation is between the Epeak energy, which is the peak energy in the νFν spectrum, and the photon index (Γ) derived from a simple power-law (PL) model. The Epeak–Γ relation, assuming the typical smoothly broken PL spectrum of GRBs, is log Epeak = 3.258 − 0.829 Γ (1.3 ⩽ Γ ⩽ 2.3). We take into account not only a range of Epeak energies and fluences, but also distributions for both the low-energy photon index and the high-energy photon index in the smoothly broken PL model. The distribution of burst durations in the BAT GRB sample is also included in the simulation. Our correlation is consistent with the index observed by BAT and Epeak measured by the BAT, and by other GRB instruments. Since about 85% of GRBs observed by the BAT are acceptably fit with the simple PL model because of the relatively narrow energy range of the BAT, this relationship can be used to estimate Epeak when it is located within the BAT energy range.
An instanton take on some knot detection results

We give new proofs that Khovanov homology detects the figure eight knot and the cinquefoils, and that HOMFLY homology detects $5_2$ and each of the $P(-3,3,2n+1)$ pretzel knots. For all but the figure eight these mostly follow the same lines as in previous work. The key difference is that in honor of Tom Mrowka's 60th birthday, the arguments here use instanton Floer homology rather than knot Floer homology.
Popular Progression Differences in Vector Spaces

Green proved an arithmetic analog of Szemerédi’s celebrated regularity lemma and used it to verify a conjecture of Bergelson, Host, and Kra, which sharpens Roth’s theorem on three-term arithmetic progressions in dense sets. It shows that for every subset of $\mathbb{F}_p^n$ with $n$ sufficiently large, the density of three-term arithmetic progressions with some nonzero common difference is at least the random bound (the cube of the set density) up to an additive $\epsilon $. For a fixed odd prime $p$, we prove that the required dimension grows as an exponential tower of $p$’s of height $\Theta (\log (1/\epsilon ))$. This improves both the lower and upper bound, and is the first example of a result where a tower-type bound coming from applying a regularity lemma is shown to be necessary.
Vortex motion and geometric function theory: the role of connections

We formulate the equations for point vortex dynamics on a closed two-dimensional Riemannian manifold in the language of affine and other kinds of connections. This can be viewed as a relaxation of standard approaches, using the Riemannian metric directly, to an approach based more on local coordinates provided with a minimal amount of extra structure. The speed of a vortex is then expressed in terms of the difference between an affine connection derived from the coordinate Robin function and the Levi–Civita connection associated with the Riemannian metric. A Hamiltonian formulation of the same dynamics is also given. The relevant Hamiltonian function consists of two main terms. One of the terms is the well-known quadratic form based on a matrix whose entries are Green and Robin functions, while the other term describes the energy contribution from those circulating flows which are not implicit in the Green functions. One main issue of the paper is a detailed analysis of the somewhat intricate exchanges of energy between these two terms of the Hamiltonian. This analysis confirms the mentioned dynamical equations formulated in terms of connections. This article is part of the theme issue ‘Topological and geometrical aspects of mass and vortex dynamics’.
Interstellar medium structure and the slope of the radio Σ–D relation of supernova remnants

We analyze the influence of fractal structure of the interstellar matter (ISM) density on the parameter values for the radio surface brightness to diameter ($\Sigma-D$) relation for supernovae remnants (SNRs). We model a dense ISM as a molecular cloud with fractal density structure. SNRs are modelled as spheres of different radius scattered in the modelled ISM. The surface brightness of the SNRs is calculated from the simple relation $\Sigma \propto \rho^{0.5}D^{-3.5}$ and also from the parametrized more general form $\Sigma \propto \rho^{\eta}D^{-\beta_0}$. Our results demonstrate that empirical $\Sigma-D$ slopes that are steeper than the ones derived from theory, might be partly explained with the fractal structure of the ambient medium into which SNRs expand. The slope of the $\Sigma-D$ relation steepens if the density of the regions where SNRs are formed is higher. The simple geometrical effects combined with the fractal structure of the ISM can contribute to a steeper empirical $\Sigma-D$ slopes, especially for older remnants, and this is more pronounced if $\Sigma$ has a stronger dependence on ambient density.
TLP: A Deep Learning-Based Cost Model for Tensor Program Tuning

Tensor program tuning is a non-convex objective optimization problem, to which search-based approaches have proven to be effective. At the core of the search-based approaches lies the design of the cost model. Though deep learning-based cost models perform significantly better than other methods, they still fall short and suffer from the following problems. First, their feature extraction heavily relies on expert-level domain knowledge in hardware architectures. Even so, the extracted features are often unsatisfactory and require separate considerations for CPUs and GPUs. Second, a cost model trained on one hardware platform usually performs poorly on another, a problem we call cross-hardware unavailability. In order to address these problems, we propose TLP and MTL-TLP. TLP is a deep learning-based cost model that facilitates tensor program tuning. Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives. We treat schedule primitives as tensor languages. TLP is thus a Tensor Language Processing task. In this way, the task of predicting the tensor program latency through the cost model is transformed into a natural language processing (NLP) regression task. MTL-TLP combines Multi-Task Learning and TLP to cope with the cross-hardware unavailability problem. We incorporate these techniques into the Ansor framework and conduct detailed experiments. Results show that TLP can speed up the average search time by 9.1× and 3.0× on CPU and GPU workloads, respectively, compared to the state-of-the-art implementation. MTL-TLP can achieve a speed-up of 4.7× and 2.9× on CPU and GPU workloads, respectively, using only 7% of the target hardware data. To the best of our knowledge, TLP is the first tensor program cost model to extract features directly from schedule primitives, and MTL-TLP is the first open-sourced work that effectively addresses the cross-platform unavailability problem. The code is available at https://github.com/zhaiyi000/tlp.
Stateful Detection of Adversarial Reprogramming

Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model trained to recognize images of animals can be reprogrammed to recognize medical images by embedding an adversarial program in the images provided as inputs. This attack can be perpetrated even if the target model is a black box, supposed that the machine-learning model is provided as a service and the attacker can query the model and collect its outputs. So far, no defense has been demonstrated effective in this scenario. We show for the first time that this attack is detectable using stateful defenses, which store the queries made to the classifier and detect the abnormal cases in which they are similar. Once a malicious query is detected, the account of the user who made it can be blocked. Thus, the attacker must create many accounts to perpetrate the attack. To decrease this number, the attacker could create the adversarial program against a surrogate classifier and then fine-tune it by making few queries to the target model. In this scenario, the effectiveness of the stateful defense is reduced, but we show that it is still effective.
Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems

End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called"likelihood trap", resulting in generated responses which are dull, repetitive, and often inconsistent with dialogue history. Comparing ranked lists of multiple generated responses against the"gold response"(from evaluation data) reveals a wide diversity in response quality, with many good responses placed lower in the ranked list. The main challenge, addressed in this work, is then how to reach beyond greedily generated system responses, that is, how to obtain and select such high-quality responses from the list of overgenerated responses at inference without availability of the gold response. To this end, we propose a simple yet effective reranking method which aims to select high-quality items from the lists of responses initially overgenerated by the system. The idea is to use any sequence-level (similarity) scoring function to divide the semantic space of responses into high-scoring versus low-scoring partitions. At training, the high-scoring partition comprises all generated responses whose similarity to the gold response is higher than the similarity of the greedy response to the gold response. At inference, the aim is to estimate the probability that each overgenerated response belongs to the high-scoring partition, given only previous dialogue history. We validate the robustness and versatility of our proposed method on the standard MultiWOZ dataset: our methods improve a state-of-the-art E2E ToD system by 2.0 BLEU, 1.6 ROUGE, and 1.3 METEOR scores, achieving new peak results. Additional experiments on the BiTOD dataset and human evaluation further ascertain the generalisability and effectiveness of the proposed framework.
An improved algorithm to compute the exponential of a matrix

In this work, we present a new way to compute the Taylor polynomial of the matrix exponential which reduces the number of matrix multiplications in comparison with the de-facto standard Patterson-Stockmeyer method. This reduction is sufficient to make the method superior in performance to Pad\'e approximants by 10-30% over a range of values for the matrix norms and thus we propose its replacement in standard software kits. Numerical experiments show the performance of the method and illustrate its stability.
Constraining the Position of the Knee in the Galactic Cosmic-Ray Spectrum with Ultra-high-energy Diffuse γ-Rays

The diffuse γ-ray emission was measured up to 957 TeV by the Tibet-ASγ experiment recently. Assuming that it is produced by the hadronic interaction between cosmic-ray nuclei and the interstellar medium, it requires that the cosmic-ray nuclei should be accelerated well beyond PeV energies. Measurements of the cosmic-ray spectra for different species show diverse results at present. The Tibet experiments showed that the spectrum of proton plus helium has an early knee below PeV. If this is correct, the diffuse γ-ray emission would suggest an additional component of Galactic cosmic rays above PeV energies. This second component may originate from a source population of so-called PeVatrons revealed by recent ultra-high-energy γ-ray observations and could contribute to the cosmic-ray fluxes up to the energy of the second knee. On the other hand, the KASCADE measurement showed that the knee of protons is higher than PeV. In this case, the diffuse γ-rays observed by Tibet-ASγ can be well accounted for by only one cosmic-ray component. These two scenarios (i.e., the Tibet and KASCADE knees) could be distinguished by the spectral structures of diffuse γ-rays and cosmic-ray nuclei. Future measurements of spectra of individual nuclei by HERD and LHAASO experiments and diffuse γ-rays by LHAASO can jointly constrain these two scenarios.