Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Optimize Domain Speciﬁc Normalization for Domain Generalization

As domain generalization aims at working robustly over arbitrary domains, having diverse source domains in the training set is crucial for generalization ability. Based on this intuition, we propose to diversify source domains by domain augmentation. Our key idea is that a mixed set of samples from multiple domains can be interpreted as a new domain with deviated statistics from the original ones. We employ a simple generation strategy; a new domain is constructed by making a union of multiple existing domains. It generates ( S 1 ) + ( S 2 ) +· · ·+ ( S S−1 ) different domains from the original S source domains. For example, suppose that there exist three original domains denoted by U , V, andW and each domain contains the instances in the corresponding domain. We construct a new domain as a union of all the elements in two or more original domains, i.e., U ∪V,V∪W,W∪U . Although these are trivial augmentations, we argue that this strategy is useful since an interpolated domain can be considered indirectly. With the new domains, we construct new datasets, D1 = {U ,V,W}, D2 = {U ∪ V,V ∪ W,W ∪ U}, and D3 = {U ∪ V ∪W}. Then, we run DSON on each of the datasets and make an ensemble of the learned models to obtain the final results. In Table 1, we compare three variations of the proposed network, DSOND1, DSON-D2, and DSON-D3, which are trained using three different training datasets, D1, D2 and D3, respectively. Finally, ensembling the three models (DSON-Ensemble-Dall) further improves the accuracy, and shows much stronger performance as compared to an ensemble of 3 independently trained BN models, which is denoted by BN-Ensemble(3).
Charmed-meson fragmentation functions with finite-mass corrections and their application in various processes

Charmed-meson fragmentation functions with finitemass corrections and their application in various processes We have calculated the single-inclusive production cross section of massive quarks in electron-positron-annihilation with next-to-leading order QCD corrections. With these results we have extracted fragmentation functions for the fragmentation from partons into D, D and D∗ mesons, where we have used experimental data from the B factories Belle and CLEO and from the ALEPH and OPAL experiments at the LEP collider. In our analysis we have included the masses of c and b quarks and of the D mesons and tested the evolution of fragmentation functions with a global fit spanning the B factories’ center-of-mass energy of √ s = 10.5GeV to LEP’s run at the Z boson resonance at MZ . We have applied this fragmentation functions in deep inelastic scattering for comparisons with HERA data using parton cross sections from the literature available in program form. We have then modified this cross section to calculate predictions for deep inelastic two-photon-scattering. By applying the Weizsäcker-Williams spectrum on the real photon we have calculated predictions for LEP1, LEP2 and the future ILC experiments. For ILC we have also included a beamstrahlung spectrum. Finally we have calculated production cross sections for the planned eγ mode of the ILC with the help of a Compton spectrum. Zusammenfassung Charm-Meson Fragmentierungsfunktionen mit endlichen Massenkorrekturen und ihre Anwendung in verschiedenen Prozessen Wir haben den einfach-inklusiven Produktionswirkungsquerschnitt von massiven Quarks in Elektron-Positron-Annihilation mit QCD-Korrekturen der nächstführenden Ordnung berechnet. Mit diesen Ergebnissen haben wir die Fragmentierungsfunktionen für die Fragmentierung von Partonen in D, D und D∗ Mesonen extrahiert, wobei wir experimentelle Daten von den B-Fabriken Belle und CLEO und von den ALEPHund OPAL-Experimenten am LEP-Beschleuniger benutzt haben. In unserer Analyse haben wir die Massen der cund b-Quarks und der D-Mesonen berücksichtigt und die Evolution der Fragmentierungsfunktionen mit einem globalen Fit getestet, welcher von der Schwerpunktsenergie der B-Fabriken von √ s = 10.5GeV bis zu dem LEP-Lauf auf der Z-Resonanz bei MZ reicht. Wir haben diese Fragmentierungsfunktionen in tief-inelastischer Streuung für Vergleiche zu HERA-Daten angewandt mittels Partonwirkungsquerschnitten aus der Literatur, die in Programmform verf̈’ugbar sind. Wir haben diese Wirkungsquerschnitte dann modifiziert, um Vorhersagen für tief-inelastische Zwei-PhotonStreuung zu berechnen. Durch Anwendung des Weizsäcker-Williams-Spektrums auf das reelle Photon haben wir Vorhersagen für LEP1, LEP2 und das zukünftige ILC Experiment berechnet. Für ILC haben wir auch ein Beamstrahlungsspektrum berücksichtigt. Zu guter Letzt haben wir mit Hilfe eines Compton-Spektrums Produktionsquerschnitte für den geplanten eγ-Modus des ILC berechnet.
Mechanisms in the hypersonic laminar near wake of a blunt body

A new theoretical framework, based on the analysis of Navier–Stokes solutions for the hypersonic laminar near wake of two-dimensional and axisymmetric blunt bodies, is presented. A semi-empirical relationship is derived between the free-stream Mach and Reynolds numbers and a characteristic wake Reynolds number. A control volume analysis was performed to assess the validity of some common assumptions used in the literature. Analysis of the momentum and vorticity equations is used to assess the dominant mechanisms of momentum transfer along and across the dividing streamline and centreline which enclose the near wake. An observed stagnation pressure gain along the dividing streamline is explained using the entropy transport equation, demonstrating an unbalance between entropy generation due to viscous dissipation and entropy diffusion. The rear-stagnation point flow is analysed using an analogy to a reversed flow jet which allows for the centreline Mach number to be solved. A new viscous–inviscid interaction theory is presented for the reattachment shock formation process for both planar and axisymmetric wakes. Finally, all of the sub-mechanisms are combined into an overall wake mechanism. The resulting equations constitute the first overall theoretical framework of the laminar near-wake mechanism including separation, reattachment, rear-stagnation point flow and dividing streamline stagnation pressure gain for both planar and axisymmetric near wakes. Scaling arguments are presented throughout the work for each of the key sub-mechanisms. Recommendations are made for how experimental and numerical results for the near wake should be presented. The equations and recommendations presented here are then used to perform a detailed disambiguation of laminar capsule studies in the literature.
Accountability and Motivation

We build a formal model that examines how different policymaking environments shape career-concerned officials' reform decisions and implementation. When career concerns are strong, officials will inefficiently initiate reforms to signal to the central government that they are congruent. To improve the quality of reform policymaking, the central government must hold officials accountable to policy outcomes. We demonstrate that the central government can exercise this accountability by requiring officials to publicize policy outcomes while maintain secrecy on implementation details. In this situation, officials can signal their congruence only through a desirable policy outcome, so they are highly motivated to carry out a reform well. We also demonstrate that the accountability on policy outcomes is infeasible under alternative policymaking environments. We apply the results to China's recent practice in decentralized reform policymaking.
Statistical properties of cosmological correlation functions

Correlation functions are an omnipresent tool in astrophysics, and they are routinely used to study phenomena as diverse as the large-scale structure of the Universe, time-dependent pulsar signals, and the cosmic microwave background. In many cases, measured correlation functions are analyzed in the framework of Bayesian statistics, which requires knowledge about the likelihood of the data. In the case of correlation functions, this probability distribution is usually approximated as a multivariate Gaussian, which is not necessarily good approximation – hence, this work aims at finding a better description. To this end, we exploit fundamental mathematical constraints on correlation functions, which we use to construct a quasi-Gaussian likelihood. We explain how to compute the constraints, in particular for multi-dimensional random fields, where this can only be done numerically, check the quality of the quasi-Gaussian approximation, and compare it to alternative approaches – most importantly, we test the new-found description of the likelihood in a toy-model Bayesian analysis. Finally, we compute correlation functions from the Millennium Simulation and show that they obey the constraints. By studying statistical properties of the measured correlation functions, we present further indications for the validity of the quasi-Gaussian approach.
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems

Since f is `-smooth, f(x,y)+(`/2)‖x‖ is convex in x for any y ∈ Y . Since Y is bounded, Danskin’s theorem (Rockafellar, 2015) implies that Ψ(x) is convex. Putting these pieces together yields that Φ(w) + ` ‖w − x‖ is (`/2)-strongly convex. This implies that Φ1/2`(x) and proxΦ/2`(x) are well-defined. Furthermore, by the definition of proxΦ/2`(x), we have Φ(proxΦ/2`(x)) ≤ Φ1/2`(proxΦ/2`(x)) ≤ Φ(x), ∀x ∈ R. Moreover, Davis & Drusvyatskiy (2019, Lemma 2.2) implies that Φ1/2` is `-smooth with ∇Φ1/2`(x) = 2`(x− proxΦ/2`(x)). Finally, it follows from Nesterov (2013, Theorem 2.1.5) that Φ1/2` satisfies the last inequality.
Learning V 1 Simple Cells with Vector Representations of Local Contents and Matrix Representations of Local Motions

Simple cells in primary visual cortex (V1) can be approximated by Gabor filters, and adjacent simple cells tend to have quadrature phase relationship. This paper entertains the hypothesis that a key purpose of such simple cells is to perceive local motions, i.e., displacements of pixels, caused by the relative motions between the agent and the surrounding environment. Specifically, we propose a representational model that couples the vector representations of local image contents with the matrix representations of local pixel displacements. When the image changes from one time frame to the next due to pixel displacements, the vector at each pixel is rotated by a matrix that represents the displacement of this pixel. We show that by learning from pair of images that are deformed versions of each other, we can learn both vector and matrix representations. The units in the learned vector representations reproduce properties of V1 simple cells. The learned model enables perceptual inference of local motions.
ACOUSTICS BASED INTENT RECOGNITION USING DISCOVERED PHONETIC UNITS FOR LOW RESOURCE LANGUAGES

Intent Recognition and Slot Identiﬁcation are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low-resourced and unwritten languages. We use an acoustic based SLU system that converts speech to its phonetic transcription using a universal phone recognition system. We build a word-free natural language understanding module that does intent recognition and slot identiﬁcation from these phonetic transcription. Our proposed SLU system performs competitively for resource rich scenarios and signiﬁcantly outperforms existing approaches as the amount of available data reduces. We train both recurrent and transformer based neural networks and test our system on ﬁve natural speech datasets in ﬁve different languages. We observe more than 10% improvement for intent classiﬁcation in Tamil and more than 5% improvement for intent classiﬁcation in Sinhala. Additionally, we present a novel approach towards unsupervised slot identiﬁcation us-ing normalized attention scores. This approach can be used for unsupervised slot labelling, data augmentation and to generate data for a new slot in a one-shot way with only one speech recording.
Antimicrobial resistance in Campylobacter jejuni and Campylobacter coli from retail chilled chicken in the UK (Year 3: 2016-17)

This report has been produced by Public Health England (PHE) under a contract placed by the Food Standards Agency (the FSA). The views expressed herein are not necessarily those of the FSA. PHE warrants that all reasonable skill and care has been used in preparing this report. Notwithstanding this warranty, PHE shall not be under any liability for loss of profit, business, revenues or any special indirect or consequential damage of any nature whatsoever or loss of anticipated saving or for any increased costs sustained by the client or his or her servants or agents arising in any way whether directly or indirectly as a result of reliance on this report or of any error or defect in this report.
Saturated flow boiling characteristics in single rectangular minichannels: effect of aspect ratio

ABSTRACT In this study, saturated flow boiling characteristics of deionized water in single rectangular minichannels are investigated experimentally. A special attention is paid to the effect of aspect ratio (channel width to depth, Wch/Hch) on the heat transfer and total pressure drop. Experiments are conducted for various values of the mass flux and the wall heat flux. Flow visualization is used as a complementary technique for a deeper physical understanding of flow phenomena. The results show that the channel aspect ratio has a significant effect on both the local two-phase heat transfer coefficient and the total pressure drop. In general manner, the aspect ratio of 1 presents the highest heat transfer coefficients, while the aspect ratio of 0.25 demonstrates the lowest ones. On the other hand, the lowest values of the pressure drop are obtained at the extreme values of the aspect ratio (0.25 and 4).
Discovering the factivity of "know"

How do children discover which linguistic expressions are associated with presuppositions? Do they take a direct strategy of tracking whether linguistic expressions are associated with particular speaker presuppositions? This strategy may fail children who are trying to learn about the presuppositions of so-called 'soft' presupposition triggers, which can be readily used even when the relevant would-be presupposed content is not part of the common ground. We present a corpus study with the soft trigger "know" and the related, but non-presuppositional "think". We find that a direct learning strategy would indeed run into problems for such a soft trigger given the nature and availability of evidence in children's linguistic input.
THE ZASSENHAUS LEMMA IN STAR-REGULAR CATEGORIES

The Noether Isomorphism Theorems and the Zassenhaus Lemma from group theory have a non-pointed version in a suitable categorical context ﬁrst considered by W. Tholen in his PhD thesis. This article leads to a uniﬁcation of these results with the ones in the pointed categorical context previously considered by O. Wyler, by working in the framework of star-regular categories introduced by M. Gran, Z. Jane-lidze and A. Ursini. Some concrete examples of categories where these results hold are examined in detail.
Active Polymers — Emergent Conformational and Dynamical Properties: A Brief Review

Active matter exhibits a wealth of emerging nonequilibrium behaviours. A paradigmatic example is the interior of cells, where active components, such as the cytoskeleton, are responsible for its structural organization and the dynamics of the various components. Of particular interest are the properties of polymers and filaments. The intimate coupling of thermal and active noise, hydrodynamic interactions, and polymer conformations implies the emergence of novel structural and dynamical features. In this article, we review recent theoretical and simulation developments and results for the structural and dynamical properties of polymers exposed to activity. Two- and three-dimensional filaments are considered propelled by different mechanisms such as active Brownian particles or hydrodynamically-coupled force dipoles.
Visibility Extension via Reflection

This paper studies a variant of the Art-gallery problem in which "walls" can be replaced by \emph{reflecting-edges}, which allows the guard to see further and thereby see a larger portion of the gallery. The art-gallery is a simple closed polygon $P$, a guard is a point $p$ in $P$, and a guard sees another point $q$ in $P$ if the segment $\overline{pq}$ is contained in the interior of $P$. The visibility region of $p$ is the set of points $q$ in $P$ that are visible from $p$. If we let an edge of the polygon allow reflections, then the visibility region should be changed accordingly. We study visibility with specular and diffuse reflections. Moreover, the number of times a ray can be reflected can be taken as a parameter. For \emph{vertex} guarding polygons with $k$ diffuse reflections, we establish an upper bound on the optimum solution. For this problem, we generalize the $O(logn)$-approximation ratio algorithm of the Art Gallery Problem. For a bounded $k$, the generalization gives a polynomial-time algorithm with $O(log n)$-approximation ratio for both cases diffuse and specular reflections. Furthermore, We show that several cases of the generalized problem are NP-hard. We also illustrate that if $P$ is a funnel or a weak visibility polygon, then the problem becomes more straightforward and can be solved in polynomial time.
A modelling approach to infer the solar wind dynamic pressure from magnetic field observations inside Mercury’s magnetosphere

Aims. The lack of an upstream solar wind plasma monitor when a spacecraft is inside the highly dynamic magnetosphere of Mercury limits interpretations of observed magnetospheric phenomena and their correlations with upstream solar wind variations. Methods. We used AMITIS, a three-dimensional GPU-based hybrid model of plasma (particle ions and fluid electrons) to infer the solar wind dynamic pressure and Alfvén Mach number upstream of Mercury by comparing our simulation results with MESSENGER magnetic field observations inside the magnetosphere of Mercury. We selected a few orbits of MESSENGER that have been analysed and compared with hybrid simulations before. Then we ran a number of simulations for each orbit (~30–50 runs) and examined the effects of the upstream solar wind plasma variations on the magnetic fields observed along the trajectory of MESSENGER to find the best agreement between our simulations and observations. Results. We show that, on average, the solar wind dynamic pressure for the selected orbits is slightly lower than the typical estimated dynamic pressure near the orbit of Mercury. However, we show that there is a good agreement between our hybrid simulation results and MESSENGER observations for our estimated solar wind parameters. We also compare the solar wind dynamic pressure inferred from our model with those predicted previously by the WSA-ENLIL model upstream of Mercury, and discuss the agreements and disagreements between the two model predictions. We show that the magnetosphere of Mercury is highly dynamic and controlled by the solar wind plasma and interplanetary magnetic field. In addition, in agreement with previous observations, our simulations show that there are quasi-trapped particles and a partial ring current-like structure in the nightside magnetosphere of Mercury, more evident during a northward interplanetary magnetic field (IMF). We also use our simulations to examine the correlation between the solar wind dynamic pressure and stand-off distance of the magnetopause and compare it with MESSENGER observations. We show that our model results are in good agreement with the response of the magnetopause to the solar wind dynamic pressure, even during extreme solar events. We also show that our model can be used as a virtual solar wind monitor near the orbit of Mercury and this has important implications for interpretation of observations by MESSENGER and the future ESA/JAXA mission to Mercury, BepiColombo.
The many graded cellular bases of Hecke algebras

We settle a long-standing problem in the theory of Hecke algebras of complex reflection groups by constructing many (graded) integral cellular bases of these algebras. As applications, we explicitly construct the simple modules of Ariki's categorification theorem and prove unitriangularity of decomposition matrices over arbitrary fields, we also prove Martin-Woodcock's conjecture.
High-dimensional Central Limit Theorems by Stein's Method in the Degenerate Case

In the literature of high-dimensional central limit theorems, there is a gap between results for general limiting correlation matrix $\Sigma$ and the strongly non-degenerate case. For the general case where $\Sigma$ may be degenerate, under certain light-tail conditions, when approximating a normalized sum of $n$ independent random vectors by the Gaussian distribution $N(0,\Sigma)$ in multivariate Kolmogorov distance, the best-known error rate has been $O(n^{-1/4})$, subject to logarithmic factors of the dimension. For the strongly non-degenerate case, that is, when the minimum eigenvalue of $\Sigma$ is bounded away from 0, the error rate can be improved to $O(n^{-1/2})$ up to a $\log n$ factor. In this paper, we show that the $O(n^{-1/2})$ rate up to a $\log n$ factor can still be achieved in the degenerate case, provided that the minimum eigenvalue of the limiting correlation matrix of any three components is bounded away from 0. We prove our main results using Stein's method in conjunction with previously unexplored inequalities for the integral of the first three derivatives of the standard Gaussian density over convex polytopes. These inequalities were previously known only for hyperrectangles. Our proof demonstrates the connection between the three-components condition and the third moment Berry--Esseen bound.
Implicit Generation and Generalization in Energy-Based Models

Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. Finally, we show that EBMs are useful models across a wide variety of tasks, achieving state-of-the-art out-of-distribution classification, adversarially robust classification, state-of-the-art continual online class learning, and coherent long term predicted trajectory rollouts.
Deep Learning in Characteristics-Sorted Factor Models

This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long-short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors -- hidden layers. Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.