Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.
3D Magic Mirror: Automatic Video to 3D Caricature Translation

Caricature is an abstraction of a real person which distorts or exaggerates certain features, but still retains a likeness. While most existing works focus on 3D caricature reconstruction from 2D caricatures or translating 2D photos to 2D caricatures, this paper presents a real-time and automatic algorithm for creating expressive 3D caricatures with caricature style texture map from 2D photos or videos. To solve this challenging ill-posed reconstruction problem and cross-domain translation problem, we first reconstruct the 3D face shape for each frame, and then translate 3D face shape from normal style to caricature style by a novel identity and expression preserving VAE-CycleGAN. Based on a labeling formulation, the caricature texture map is constructed from a set of multi-view caricature images generated by CariGANs. The effectiveness and efficiency of our method are demonstrated by comparison with baseline implementations. The perceptual study shows that the 3D caricatures generated by our method meet people's expectations of 3D caricature style.
Lieb–Robinson bounds for open quantum systems with long-ranged interactions

We state and prove four types of Lieb–Robinson bounds valid for many-body open quantum systems with power law decaying interactions undergoing out of equilibrium dynamics. We also provide an introductory and self-contained discussion of the setting and tools necessary to prove these results. The results found here apply to physical systems in which both long-ranged interactions and dissipation are present, as commonly encountered in certain quantum simulators, such as Rydberg systems or Coulomb crystals formed by ions.
RF-Net: An End-To-End Image Matching Network Based on Receptive Field

This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable matching framework is desirable and challenging. The very recent approach, LF-Net, successfully embeds the entire feature extraction pipeline into a jointly trainable pipeline, and produces the state-of-the-art matching results. This paper introduces two modifications to the structure of LF-Net. First, we propose to construct receptive feature maps, which lead to more effective keypoint detection. Second, we introduce a general loss function term, neighbor mask, to facilitate training patch selection. This results in improved stability in descriptor training. We trained RF-Net on the open dataset HPatches, and compared it with other methods on multiple benchmark datasets. Experiments show that RF-Net outperforms existing state-of-the-art methods.
Learning the optimally coordinated routes from the statistical mechanics of polymers

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn; the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions.
Topological phases in non-Hermitian Aubry-André-Harper models

Topological phases have recently witnessed a rapid progress in non-Hermitian systems. Here we study a one-dimensional non-Hermitian Aubry-Andre-Harper model with imaginary periodic or quasiperiodic modulations. We demonstrate that the non-Hermitian off-diagonal AAH models can host zero-energy modes at the edges. In contrast to the Hermitian case, the zero-energy mode can be localized only at one edge. Such a topological phase corresponds to the existence of a quarter winding number defined by eigenenergy in momentum space. We further find the coexistence of a zero-energy mode located only at one edge and topological nonzero energy edge modes characterized by a generalized Bott index. In the incommensurate case, a topological non-Hermitian quasicrystal is predicted where all bulk states and two topological edge states are localized at one edge. Such topological edge modes are protected by the generalized Bott index. Finally, we propose an experimental scheme to realize these non-Hermitian models in electric circuits. Our findings add a new direction for exploring topological properties in Aubry-Andre-Harper models.
Deeply-Supervised Knowledge Synergy

Convolutional Neural Networks (CNNs) have become deeper and more complicated compared with the pioneering AlexNet. However, current prevailing training scheme follows the previous way of adding supervision to the last layer of the network only and propagating error information up layer-by-layer. In this paper, we propose Deeply-supervised Knowledge Synergy (DKS), a new method aiming to train CNNs with improved generalization ability for image classification tasks without introducing extra computational cost during inference. Inspired by the deeply-supervised learning scheme, we first append auxiliary supervision branches on top of certain intermediate network layers. While properly using auxiliary supervision can improve model accuracy to some degree, we go one step further to explore the possibility of utilizing the probabilistic knowledge dynamically learnt by the classifiers connected to the backbone network as a new regularization to improve the training. A novel synergy loss, which considers pairwise knowledge matching among all supervision branches, is presented. Intriguingly, it enables dense pairwise knowledge matching operations in both top-down and bottom-up directions at each training iteration, resembling a dynamic synergy process for the same task. We evaluate DKS on image classification datasets using state-of-the-art CNN architectures, and show that the models trained with it are consistently better than the corresponding counterparts. For instance, on the ImageNet classification benchmark, our ResNet-152 model outperforms the baseline model with a 1.47% margin in Top-1 accuracy. Code is available at https://github.com/sundw2014/DKS.
Star formation quenching imprinted on the internal structure of naked red nuggets

The formation and assembly process of massive galaxies is a combination of two phases: an initial in-situ-dominated one followed by an ex-situ-dominated evolution. Separating these two contributions is therefore crucial to understand the baryonic cycle within massive haloes. A recently discovered population of so-called naked red nuggets, galaxies that shortcut the ex-situ stage preserving their pristine properties, presents a unique opportunity to study in detail star formation in massive galaxies without the confounding effect of later accretion. We investigate the spatially resolved star formation histories of a sample of 12 naked red nuggets. We measure how their radial light distributions, star formation rates, and central densities evolved in time. We find that, while forming stars, red nuggets become gradually more concentrated, reaching a maximum concentration at quenching. After being quenched, they kept forming stars in a more discy-like configuration. Our measurements suggest that supermassive black holes and host galaxies grow their mass in a self-regulated way until a characteristic M•/Mhalo is reached. Once black holes are massive enough, red nuggets get quenched and depart from the star formation main sequence. While in the main sequence, red nuggets evolve at roughly constant star formation rate. This can explain up to ∼0.3 dex of the scatter of the star formation main sequence, as well as its higher normalization observed in the early Universe. Hence, our results suggest that the main sequence is composed of populations of galaxies at different evolutionary stages and that the scatter is therefore due to secular processes.
Adversarial Generation and Encoding of Nested Texts

In this paper we propose a new language model called AGENT, which stands for Adversarial Generation and Encoding of Nested Texts. AGENT is designed for encoding, generating and refining documents that consist of a long and coherent text, such as an entire book, provided they are hierarchically annotated (nested). i.e. divided into sentences, paragraphs and chapters. The core idea of our system is learning vector representations for each level of the text hierarchy (sentences, paragraphs, etc.), and train each such representation to perform 3 tasks: The task of reconstructing the sequence of vectors from a lower level that was used to create the representation, and generalized versions of the Masked Language Modeling (MLM) and "Next Sentence Prediction" tasks from BERT Devlin et al. [2018]. Additionally we present a new adversarial model for long text generation and suggest a way to improve the coherence of the generated text by traversing its vector representation tree.
TT¯
 flows and (2, 2) supersymmetry

We construct a solvable deformation of two-dimensional theories with (2, 2) supersymmetry using an irrelevant operator which is a bilinear in the supercurrents. This supercurrent-squared operator is manifestly supersymmetric and equivalent to T (T) over bar after using conservation laws. As illustrative examples, we deform theories involving a single (2, 2) chiral superfield. We show that the deformed free theory is on-shell equivalent to the (2, 2) Nambu-Goto action. At the classical level, models with a superpotential exhibit more surprising behavior: the deformed theory exhibits poles in the physical potential which modify the vacuum structure. This suggests that irrelevant deformations of T (T) over bar type might also affect infrared physics.
Admissibility and nonuniform polynomial dichotomies

For a general one‐sided nonautonomous dynamics defined by a sequence of linear operators, we consider the notion of a polynomial dichotomy with respect to a sequence of norms and we characterize it completely in terms of the admissibility of bounded solutions. As a nontrivial application, we establish the robustness of the notion of a nonuniform polynomial dichotomy.
Learning Perceptually-Aligned Representations via Adversarial Robustness

Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps.
An adaptive multiresolution discontinuous Galerkin method with artificial viscosity for scalar hyperbolic conservation laws in multidimensions

In this paper, we develop an adaptive multiresolution discontinuous Galerkin (DG) scheme for scalar hyperbolic conservation laws in multidimensions. Compared with previous work for linear hyperbolic equations \cite{guo2016transport, guo2017adaptive}, a class of interpolatory multiwavelets are applied to efficiently compute the nonlinear integrals over elements and edges in DG schemes. The resulting algorithm, therefore can achieve similar computational complexity as the sparse grid DG method for smooth solutions. Theoretical and numerical studies are performed taking into consideration of accuracy and stability with regard to the choice of the interpolatory multiwavelets. Artificial viscosity is added to capture the shock and only acts on the leaf elements taking advantages of the multiresolution representation. Adaptivity is realized by auto error thresholding based on hierarchical surplus. Accuracy and robustness are demonstrated by several numerical tests.
Impact of Iron And Vitamin B12 Anaemia At Glycosylated Hemoglobin Level: A Case Control Study

Introduction: Diabetes Mellitus (DM) refers to a group of common metabolic disorder. It is a complex, chronic illness requiring continuous medical care with multifactorial risk reduction strategies beyond glycemic control. HbA1c which was primarily used as a test of glycemic control has now been added as a diagnostic test. One condition that effect erythrocyte turnover is anemia. Iron and Vitamin B12 deficiency inhibit erythropoiesis and increase the mean survival duration of erythrocyte, leading to increase HbA1c. Methods: The study was conducted in tertiary care hospital, Jaipur. The study involved 60patients out of which 30 were of Iron deficiency anemia and remaining 30 had Vitamin B 12 deficiency anemia. Permission was taken from the ethical committee before starting the study. A well informed written consent was taken prior to the study and the results of the same were compared with 30 non anemic, non diabetic controls using ANOVA and T test analysis. Results: Out of 30 participants in each group, 36.67% were male and 63.335% were female in iron deficiency group, 53.33% were male and 46.67% were female in vitamin B12 deficiency group, 60% were male and 40% were female in control group.In Iron deficiency anemia 10 out of 30 participants (33.33%) had Hba1c in the range of impaired glucose homeostasis(5.6 -6.5) 2 and in Vitamin B12 deficiency anemia 12 out of 30 participants (40%) had Hba1c in the range of impaired glucose homeostasis. Interestingly 2 patients from irondeficiency anemia had Hba1c in diabetic range (more then 6.5). Our study reported significant negative trend when Hemoglobin was compared with HbA1c in patients with iron deficiency anemia, that is, patients with lower Hemoglobin had higher HbA1c. Conclusion: Anemia and Diabetes are a common problem in Indian scenario and use of HbA1c to diagnose prediabetes and diabetes in iron-deficient populations may lead to a spuriously exaggerated prevalence of
Assessing algorithmic fairness with unobserved protected class using data combination

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and targeted policymaking, where in some cases legislative or regulatory frameworks for fairness exist and define specific protected classes. In this paper we study a fundamental challenge to assessing disparate impacts, or performance disparities in general, in practice: protected class membership is often not observed in the data. This is particularly a problem in lending and healthcare. We consider the use of an auxiliary dataset, such as the US census, that includes protected class labels but not decisions or outcomes. We show that a variety of common disparity measures are generally unidentifiable aside for some unrealistic cases, providing a new perspective on the documented biases of popular proxy-based methods. We provide exact characterizations of the sharpest-possible partial identification set of disparities either under no assumptions or when we incorporate mild smoothness constraints. We further provide optimization-based algorithms for computing and visualizing these sets of simultaneously achievable pairwise disparties for assessing disparities that arise between multiple groups, which enables reliable and robust assessments - an important tool when disparity assessment can have far-reaching policy implications. We demonstrate this in two case studies with real data: mortgage lending and personalized medicine dosing.
Redshift evolution of the Fundamental Plane relation in the IllustrisTNG simulation

We investigate the Fundamental Plane (FP) evolution of early-type galaxies in the IllustrisTNG-100 simulation (TNG100) from redshift z = 0 to z = 2. We find that a tight plane relation already exists as early as z = 2. Its scatter stays as low as ∼0.08 dex across this redshift range. Both slope parameters b and c (where R ∝ σbIc with R, σ, and I being the typical size, velocity dispersion, and surface brightness) of the plane evolve mildly since z = 2, roughly consistent with observations. The FP residual $\rm Res$ ($\equiv \, a\, +\, b\log \sigma \, +\, c\log I\, -\, \log R$, where a is the zero-point of the FP) is found to strongly correlate with stellar age, indicating that stellar age can be used as a crucial fourth parameter of the FP. However, we find that 4c + b + 2 = δ, where δ ∼ 0.8 for FPs in TNG, rather than zero as is typically inferred from observations. This implies that a tight power-law relation between the dynamical mass-to-light ratio Mdyn/L and the dynamical mass Mdyn (where Mdyn ≡ 5σ2R/G, with G being the gravitational constant) is not present in the TNG100 simulation. Recovering such a relation requires proper mixing between dark matter and baryons, as well as star formation occurring with correct efficiencies at the right mass scales. This represents a powerful constraint on the numerical models, which has to be satisfied in future hydrodynamical simulations.
Dimensionality compression and expansion in Deep Neural Networks

Datasets such as images, text, or movies are embedded in high-dimensional spaces. However, in important cases such as images of objects, the statistical structure in the data constrains samples to a manifold of dramatically lower dimensionality. Learning to identify and extract task-relevant variables from this embedded manifold is crucial when dealing with high-dimensional problems. We find that neural networks are often very effective at solving this task and investigate why. To this end, we apply state-of-the-art techniques for intrinsic dimensionality estimation to show that neural networks learn low-dimensional manifolds in two phases: first, dimensionality expansion driven by feature generation in initial layers, and second, dimensionality compression driven by the selection of task-relevant features in later layers. We model noise generated by Stochastic Gradient Descent and show how this noise balances the dimensionality of neural representations by inducing an effective regularization term in the loss. We highlight the important relationship between low-dimensional compressed representations and generalization properties of the network. Our work contributes by shedding light on the success of deep neural networks in disentangling data in high-dimensional space while achieving good generalization. Furthermore, it invites new learning strategies focused on optimizing measurable geometric properties of learned representations, beginning with their intrinsic dimensionality.
Cohen–Lenstra distributions via random matrices over complete discrete valuation rings with finite residue fields

Let $(R, \mathfrak{m})$ be a complete discrete valuation ring with the finite residue field $R/\mathfrak{m} = \mathbb{F}_{q}$. Given a monic polynomial $P(t) \in R[t]$ whose reduction modulo $\mathfrak{m}$ gives an irreducible polynomial $\bar{P}(t) \in \mathbb{F}_{q}[t]$, we initiate the investigation of the distribution of $\mathrm{coker}(P(A))$, where $A \in \mathrm{Mat}_{n}(R)$ is randomly chosen with respect to the Haar probability measure on the additive group $\mathrm{Mat}_{n}(R)$ of $n \times n$ $R$-matrices. One of our main results generalizes two results of Friedman and Washington. Our other results are related to the distribution of the $\bar{P}$-part of a random matrix $\bar{A} \in \mathrm{Mat}_{n}(\mathbb{F}_{q})$ with respect to the uniform distribution, and one of them generalizes a result of Fulman. We heuristically relate our results to a celebrated conjecture of Cohen and Lenstra, which predicts that given an odd prime $p$, any finite abelian $p$-group (i.e., $\mathbb{Z}_{p}$-module) $H$ occurs as the $p$-part of the class group of a random imaginary quadratic field extension of $\mathbb{Q}$ with a probability inversely proportional to $|\mathrm{Aut}_{\mathbb{Z}}(H)|$. We review three different heuristics for the conjecture of Cohen and Lenstra, and they are all related to special cases of our main conjecture, which we prove as our main theorems. For proofs, we use some concrete combinatorial connections between $\mathrm{Mat}_{n}(R)$ and $\mathrm{Mat}_{n}(\mathbb{F}_{q})$ to translate our problems about a Haar-random matrix in $\mathrm{Mat}_{n}(R)$ into problems about a random matrix in $\mathrm{Mat}_{n}(\mathbb{F}_{q})$ with respect to the uniform distribution.