Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Aesthetic Layouts via Visual Guidance

We explore computational approaches for visual guidance to aid in creating aesthetically pleasing art and graphic design. Our work complements and builds on previous work that developed models for how humans look at images. Our approach comprises three steps. First, we collected a dataset of art masterpieces and labeled the visual fixations with state-of-art vision models. Second, we clustered the visual guidance templates of the art masterpieces with unsupervised learning. Third, we developed a pipeline using generative adversarial networks to learn the principles of visual guidance and that can produce aesthetically pleasing layouts. We show that the aesthetic visual guidance principles can be learned and integrated into a high-dimensional model and can be queried by the features of graphic elements. We evaluate our approach by generating layouts on various drawings and graphic designs. Moreover, our model considers the color and structure of graphic elements when generating layouts. Consequently, we believe our tool, which generates multiple aesthetic layout options in seconds, can help artists create beautiful art and graphic designs.
On the Finite Element Approximation of Fourth-Order Singularly Perturbed Eigenvalue Problems

Abstract We consider fourth-order singularly perturbed eigenvalue problems in one-dimension and the approximation of their solution by the h version of the Finite Element Method (FEM). In particular, we use a C 1 {C^{1}} -conforming FEM with piecewise polynomials of degree p ≥ 3 {p\geq 3} defined on an exponentially graded mesh. We show that the method converges uniformly, with respect to the singular perturbation parameter, at the optimal rate when the error in the eigenvalues is measured in absolute value and the error in the eigenvectors is measured in the energy norm. We also illustrate our theoretical findings through numerical computations for the case p = 3 {p=3} .
Towards exact structural thresholds for parameterized complexity

Parameterized complexity seeks to use input structure to obtain faster algorithms for NP-hard problems. This has been most successful for graphs of low treewidth: Many problems admit fast algorithms relative to treewidth and many of them are optimal under SETH. Fewer such results are known for more general structure such as low clique-width and more restrictive structure such as low deletion distance to a sparse graph class. Despite these successes, such results remain"islands'' within the realm of possible structure. Rather than adding more islands, we seek to determine the transitions between them, that is, we aim for structural thresholds where the complexity increases as input structure becomes more general. Going from deletion distance to treewidth, is a single deletion set to a graph with simple components enough to yield the same lower bound as for treewidth or does it take many disjoint separators? Going from treewidth to clique-width, how much more density entails the same complexity as clique-width? Conversely, what is the most restrictive structure that yields the same lower bound? For treewidth, we obtain both refined and new lower bounds that apply already to graphs with a single separator $X$ such that $G-X$ has treewidth $r=O(1)$, while $G$ has treewidth $|X|+O(1)$. We rule out algorithms running in time $O^*((r+1-\epsilon)^{k})$ for Deletion to $r$-Colorable parameterized by $k=|X|$. For clique-width, we rule out time $O^*((2^r-\epsilon)^k)$ for Deletion to $r$-Colorable, where $X$ is now allowed to consist of $k$ twinclasses. There are further results on Vertex Cover, Dominating Set and Maximum Cut. All lower bounds are matched by existing and newly designed algorithms.
How Much Can CLIP Benefit Vision-and-Language Tasks?

Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks. We release our code at https://github.com/clip-vil/CLIP-ViL.
IID Sampling from Intractable Distributions

We propose a novel methodology for drawing iid realizations from any target distribution on the Euclidean space with arbitrary dimension. No assumption of compact support is necessary for the validity of our theory and method. Our idea is to construct an appropriate infinite sequence of concentric closed ellipsoids, represent the target distribution as an infinite mixture on the central ellipsoid and the ellipsoidal annuli, and to construct efficient perfect samplers for the mixture components. In contrast with most of the existing works on perfect sampling, ours is not only a theoretically valid method, it is practically applicable to all target distributions on any dimensional Euclidean space and very much amenable to parallel computation. We validate the practicality and usefulness of our methodology by generating 10000 iid realizations from the standard distributions such as normal, Student's t with 5 degrees of freedom and Cauchy, for dimensions d = 1, 5, 10, 50, 100, as well as from a 50-dimensional mixture normal distribution. The implementation time in all the cases are very reasonable, and often less than a minute in our parallel implementation. The results turned out to be highly accurate. We also apply our method to draw 10000 iid realizations from the posterior distributions associated with the well-known Challenger data, a Salmonella data and the 160-dimensional challenging spatial example of the radionuclide count data on Rongelap Island. Again, we are able to obtain quite encouraging results with very reasonable computing time.
On λ-backbone coloring of cliques with tree backbones in linear time

A $\lambda$-backbone coloring of a graph $G$ with its subgraph (also called a backbone) $H$ is a function $c \colon V(G) \rightarrow \{1,\dots, k\}$ ensuring that $c$ is a proper coloring of $G$ and for each $\{u,v\} \in E(H)$ it holds that $|c(u) - c(v)| \ge \lambda$. In this paper we propose a way to color cliques with tree and forest backbones in linear time that the largest color does not exceed $\max\{n, 2 \lambda\} + \Delta(H)^2 \lceil\log{n} \rceil$. This result improves on the previously existing approximation algorithms as it is $(\Delta(H)^2 \lceil\log{n} \rceil)$-absolutely approximate, i.e. with an additive error over the optimum. We also present an infinite family of trees $T$ with $\Delta(T) = 3$ for which the coloring of cliques with backbones $T$ require to use at least $\max\{n, 2 \lambda\} + \Omega(\log{n})$ colors for $\lambda$ close to $\frac{n}{2}$.
Kinematic dependence of azimuthal anisotropies in 
p+Au,
 
d+Au,
 and 
He3+Au
 at 
sNN=200GeV

There is strong evidence for the formation of small droplets of quark-gluon plasma in $p/d/^{3}$He+Au collisions at the Relativistic Heavy Ion Collider (RHIC) and in $p$+$p$/Pb collisions at the Large Hadron Collider. In particular, the analysis of data at RHIC for different geometries obtained by varying the projectile size and shape has proven insightful. In the present analysis, we find excellent agreement with the previously published PHENIX at RHIC results on elliptical and triangular flow with an independent analysis via the two-particle correlation method, which has quite different systematic uncertainties and an independent code base. In addition, the results are extended to other detector combinations with different kinematic (pseudorapidity) coverage. These results provide additional constraints on contributions from nonflow and longitudinal decorrelations.
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation

Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain. A common solution is to minimize the Shannon Entropy to push the decision boundary away from the high density area. However, entropy minimization also leads to severe reduction of prediction diversity, and unfortunately brings harm to the domain adaptation. In this paper, we investigate the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. The nuclear-norm is an upperbound of the former, and a convex approximation of the latter. Accordingly, we propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm maximization on the target output matrix to enhance the target prediction ability, and nuclear-norm minimization on the source batch output matrix to increase applicability of the source domain knowledge. We further approximate the nuclear-norm by L_{1,2}-norm, and design multi-batch optimization for stable solution on large number of categories. The fast approximation method achieves O(n^2) computational complexity and better convergence property. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios. The code is available at https://github.com/cuishuhao/BNM.
Financial network games

We study financial systems from a game-theoretic standpoint. A financial system is represented by a network, where nodes correspond to firms, and directed labeled edges correspond to debt contracts between them. The existence of cycles in the network indicates that a payment of a firm to one of its lenders might result to some incoming payment. So, if a firm cannot fully repay its debt, then the exact (partial) payments it makes to each of its creditors can affect the cash inflow back to itself. We naturally assume that the firms are interested in their financial well-being (utility) which is aligned with the amount of incoming payments they receive from the network. This defines a game among the firms, that can be seen as utility-maximizing agents who can strategize over their payments. We are the first to study financial network games that arise under a natural set of payment strategies called priority-proportional payments. We investigate both the existence and the (in)efficiency of equilibrium strategies, under different assumptions on how the firms' utility is defined, on the types of debt contracts allowed between the firms, and on the presence of other financial features that commonly arise in practice. Surprisingly, even if all firms' strategies are fixed, the existence of a unique payment profile is not guaranteed. So, we also investigate the existence and computation of valid payment profiles for fixed payment strategies.
Impact of substrate and thermal boundary resistance on the performance of AlGaN/GaN HEMTs analyzed by means of electro-thermal Monte Carlo simulations

In this paper, we present results from the simulations of a submicrometer AlGaN/GaN high-electron-mobility transistor (HEMT) by using an in-house electro-thermal Monte Carlo simulator. We study the temperature distribution and the influence of heating on the transfer characteristics and the transconductance when the device is grown on different substrates (sapphire, silicon, silicon carbide and diamond). The effect of the inclusion of a thermal boundary resistance (TBR) is also investigated. It is found that, as expected, HEMTs fabricated on substrates with high thermal conductivities (diamond) exhibit lower temperatures, but the difference between hot-spot and average temperatures is higher. In addition, devices fabricated on substrates with higher thermal conductivities are more sensitive to the value of the TBR because the temperature discontinuity is greater in the TBR layer.
Higher Du Bois singularities of hypersurfaces

For a complex algebraic variety X$X$ , we introduce higher p$p$ ‐Du Bois singularity by imposing canonical isomorphisms between the sheaves of Kähler differential forms ΩXq$\Omega _X^q$ and the shifted graded pieces of the Du Bois complex Ω̲Xq$\underline{\Omega }_X^q$ for q⩽p$q\leqslant p$ . If X$X$ is a reduced hypersurface, we show that higher p$p$ ‐Du Bois singularity coincides with higher p$p$ ‐log canonical singularity, generalizing a well‐known theorem for p=0$p=0$ . The assertion that p$p$ ‐log canonicity implies p$p$ ‐Du Bois has been proved by Mustata, Olano, Popa, and Witaszek quite recently as a corollary of two theorems asserting that the sheaves of reflexive differential forms ΩX[q]$\Omega _X^{[q]}$ ( q⩽p$q\leqslant p$ ) coincide with ΩXq$\Omega _X^q$ and Ω̲Xq$\underline{\Omega }_X^q$ , respectively, and these are shown by calculating the depth of the latter two sheaves. We construct explicit isomorphisms between ΩXq$\Omega _X^q$ and Ω̲Xq$\underline{\Omega }_X^q$ applying the acyclicity of a Koszul complex in a certain range. We also improve some non‐vanishing assertion shown by them using mixed Hodge modules and the Tjurina subspectrum in the isolated singularity case. This is useful for instance to estimate the lower bound of the maximal root of the reduced Bernstein–Sato polynomial in the case where a quotient singularity is a hypersurface and its singular locus has codimension at most 4.
Dust Production around Carbon-Rich Stars: The Role of Metallicity

Background: Most of the stars in the Universe will end their evolution by losing their envelope during the thermally pulsing asymptotic giant branch (TP-AGB) phase, enriching the interstellar medium of galaxies with heavy elements, partially condensed into dust grains formed in their extended circumstellar envelopes. Among these stars, carbon-rich TP-AGB stars (C-stars) are particularly relevant for the chemical enrichment of galaxies. We here investigated the role of the metallicity in the dust formation process from a theoretical viewpoint. Methods: We coupled an up-to-date description of dust growth and dust-driven wind, which included the time-averaged effect of shocks, with FRUITY stellar evolutionary tracks. We compared our predictions with observations of C-stars in our Galaxy, in the Magellanic Clouds (LMC and SMC) and in the Galactic Halo, characterised by metallicity between solar and 1/10 of solar. Results: Our models explained the variation of the gas and dust content around C-stars derived from the IRS Spitzer spectra. The wind speed of the C-stars at varying metallicity was well reproduced by our description. We predicted the wind speed at metallicity down to 1/10 of solar in a wide range of mass-loss rates.
Atomic Structure Calculations of Helium with Correlated Exponential Functions

The technique of quantum electrodynamics (QED) calculations of energy levels in the helium atom is reviewed. The calculations start with the solution of the Schrödinger equation and account for relativistic and QED effects by perturbation expansion in the fine structure constant α. The nonrelativistic wave function is represented as a linear combination of basis functions depending on all three interparticle radial distances, r1, r2 and r = |r→1−r→2|. The choice of the exponential basis functions of the form exp(−αr1−βr2−γr) allows us to construct an accurate and compact representation of the nonrelativistic wave function and to efficiently compute matrix elements of numerous singular operators representing relativistic and QED effects. Calculations of the leading QED effects of order α5m (where m is the electron mass) are complemented with the systematic treatment of higher-order α6m and α7m QED effects.
Faster Math Functions, Soundly

Standard library implementations of functions like sin and exp optimize for accuracy, not speed, because they are intended for general-purpose use. But applications tolerate inaccuracy from cancellation, rounding error, and singularities-sometimes even very high error-and many application could tolerate error in function implementations as well. This raises an intriguing possibility: speeding up numerical code by tuning standard function implementations. This paper thus introduces OpTuner, an automatic method for selecting the best implementation of mathematical functions at each use site. OpTuner assembles dozens of implementations for the standard mathematical functions from across the speed-accuracy spectrum. OpTuner then uses error Taylor series and integer linear programming to compute optimal assignments of function implementation to use site and presents the user with a speed-accuracy Pareto curve they can use to speed up their code. In a case study on the POV-Ray ray tracer, OpTuner speeds up a critical computation, leading to a whole program speedup of 9% with no change in the program output (whereas human efforts result in slower code and lower-quality output). On a broader study of 37 standard benchmarks, OpTuner matches 216 implementations to 89 use sites and demonstrates speed-ups of 107% for negligible decreases in accuracy and of up to 438% for error-tolerant applications.
A ug 2 02 1 ASYMPTOTIC PRESSURE ON CAYLEY GRAPHS OF FINITELY GENERATED SEMIGROUPS

The vertices of the Cayley graph of a ﬁnitely generated semigroup form a set of sites which can be labeled by elements of a ﬁnite alphabet in a manner governed by a nonnegative real interaction matrix , respecting nearest neighbor adjacency restrictions. To the set of these conﬁgurations one can associate a pressure , which is deﬁned as the limit, when it exists, of averages of the logarithm of the partition function over certain ﬁnite subgraphs. We prove that for shifts of ﬁnite type on generalized Fibonacci trees and many primitive interaction matrices, the limit exists and is given by an inﬁnite series. We also show that the limit of any cluster points of the pressure on ﬁnite subtrees as the number of generators grows without bound, which we call the asymptotic pressure , equals the logarithm of the maximum row sum of the interaction matrix.
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions

Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neural Networks (DNNs) are robust to increasing the number of parameters in the hidden layers, but it is unclear whether this holds true for the input layer. In this letter, we investigate the impact of unnecessary input dimensions on a central issue of DNNs: their data efficiency, ie. the amount of examples needed to achieve certain generalization performance. Our results show that unnecessary input dimensions that are task-unrelated substantially degrade data efficiency. This highlights the need for mechanisms that remove {task-unrelated} dimensions to enable data efficiency gains.
TSCAN : Dialog Structure discovery using SCAN

Can we learn dialog structure from existing dialogs without ontology or domain assumptions. Understanding dialog structures from existing task oriented human human dialogs can help us automate these dialogues in a better way. Traditionally dialog structures have been created using ontologies that are created by domain experts. However, in our experience getting the ontology right is difficult and time consuming. Like other such tasks an unsupervised approach may do better than hand crafted rules. We propose an unsupervised dialog structure discovery approach that is based on SCAN (Semantic Clustering using Nearest Neighbors). Our approach comprises of two steps, the first being creating clusters of utterances and the second being creation of a structure using inter-cluster transition probabilities. Our main contribution in this paper is the adaptation of SCAN on text data. Unlike the SCAN approach for images, for text we did not train a separate pretext model and were able to use BERT for the same. Similarly for neigbor discovery, instead of augmentation we were able to leverage data variety. Evaluation metrics on dialog structures are a bit subjective, so we have used statistical measures as proxies for structure quality. We have also included our results on an internal human human task oriented 100k dialog dataset. We think SCAN like approaches are very promising for problems that use embedding similarities and should be further explored.
Adaptability and the Pivot Penalty in Science

The ability to confront new questions, opportunities, and challenges is of fundamental importance to human progress and the resilience of human societies, yet the capacity of science to meet new demands remains poorly understood. Here we deploy a new measurement framework to investigate the scientific response to the COVID-19 pandemic and the adaptability of science as a whole. We find that science rapidly shifted to engage COVID-19 following the advent of the virus, with scientists across all fields making large jumps from their prior research streams. However, this adaptive response reveals a pervasive "pivot penalty", where the impact of the new research steeply declines the further the scientists move from their prior work. The pivot penalty is severe amidst COVID-19 research, but it is not unique to COVID-19. Rather it applies nearly universally across the sciences, and has been growing in magnitude over the past five decades. While further features condition pivoting, including a scientist's career stage, prior expertise and impact, collaborative scale, the use of new coauthors, and funding, we find that the pivot penalty persists and remains substantial regardless of these features, suggesting the pivot penalty acts as a fundamental friction that governs science's ability to adapt. The pivot penalty not only holds key implications for the design of the scientific system and human capacity to confront emergent challenges through scientific advance, but may also be relevant to other social and economic systems, where shifting to meet new demands is central to survival and success.