Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Resolution Measurements of the Halos of Four Dark Matter-Dominated Galaxies: Deviations from a Universal Density Profile

We derive rotation curves for four nearby, low-mass spiral galaxies and use them to constrain the shapes of their dark matter density profiles. This analysis is based on high-resolution two-dimensional Hα velocity fields of NGC 4605, NGC 5949, NGC 5963, and NGC 6689 and CO velocity fields of NGC 4605 and NGC 5963. In combination with our previous study of NGC 2976, the full sample of five galaxies contains density profiles that span the range from αDM = 0 to αDM = 1.20, where αDM is the power-law index describing the central density profile. The scatter in αDM from galaxy to galaxy is 0.44, 3 times as large as in cold dark matter (CDM) simulations, and the mean density profile slope is αDM = 0.73, shallower than that predicted by the simulations. These results call into question the hypothesis that all galaxies share a universal dark matter density profile. We show that one of the galaxies in our sample, NGC 5963, has a cuspy density profile that closely resembles those seen in CDM simulations, demonstrating that while galaxies with the steep central density cusps predicted by CDM do exist, they are in the minority. In spite of these differences between observations and simulations, the relatively cuspy density profiles we find do not suggest that this problem represents a crisis for CDM. Improving the resolution of the simulations and incorporating additional physics may resolve the remaining discrepancies. We also find that four of the galaxies contain detectable radial motions in the plane of the galaxy. We investigate the hypothesis that these motions are caused by a triaxial dark matter halo and place lower limits on the ellipticity of the orbits in the plane of the disk of 0.043-0.175.
A DIY Ultrasonic Signal Generator for Sound Experiments

Many physics departments around the world have electronic and mechanical workshops attached to them that can help build experimental setups and instruments for research and the training of undergraduate students. The workshops are usually run by experienced technicians and equipped with expensive lathing, computer numerical control (CNC) machines, electric measuring instruments, and several other essential tools. However, in developing countries such as Sudan, the lack of qualified technicians and adequately equipped workshops hampers efforts by these departments to supplement their laboratories with the equipment they need. The only other option is to buy the needed equipment from specialized manufacturers. The latter option is not feasible for the departments in developing countries where funding for education and research is scarce and very limited and as equipment from these manufacturers is typically too expensive. These departments struggle significantly in equipping undergraduate teaching laboratories, and here we propose one way to address this.
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization

Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena renders them an ideal vehicle for communication. To comprehend the subtle message conveyed within a meme, one must understand the relevant background that facilitates its holistic assimilation. Besides digital archiving of memes and their metadata by a few websites like knowyourmeme.com, currently, there is no efficient way to deduce a meme’s context dynamically. In this work, we propose a novel task, MEMEX - given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme. At first, we develop MCC (Meme Context Corpus), a novel dataset for MEMEX. Further, to benchmark MCC, we propose MIME (MultImodal Meme Explainer), a multimodal neural framework that uses external knowledge-enriched meme representation and a multi-level approach to capture the cross-modal semantic dependencies between the meme and the context. MIME surpasses several unimodal and multimodal systems and yields an absolute improvement of 4% F1-score over the best baseline. Lastly, we conduct detailed analyses of MIME’s performance, highlighting the aspects that could lead to optimal modeling of cross-modal contextual associations.
SELF-CONSISTENT CASTAING DISTRIBUTION OF SOLAR WIND TURBULENT FLUCTUATIONS

The intermittent behavior of solar wind turbulent fluctuations has often been investigated through the modeling of their probability distribution functions (PDFs). Among others, the Castaing model has successfully been used in the past. In this paper, the energy dissipation field of solar wind turbulence has been studied for fast, slow, and polar wind samples recorded by Helios 2 and Ulysses spacecraft. The statistical description of the dissipation rate has then been used to remove intermittency through conditioning of the PDFs. Based on such observation, a self-consistent, parameter-free Castaing model is presented. The self-consistent model is tested against experimental PDFs, showing good agreement and supporting the picture of a multifractal energy cascade at the origin of solar wind intermittency.
A Response to Prelec Luc Bovens Outcome and diagnostic utility

The outcome utility of the single sinful action, smoking the single cigarette and making a different investment is positive, but the diagnostic utility is negative. The diagnostic utility of the single sinful action is the utility that is associated with not being among God’s chosen; the diagnostic utility of smoking the single cigarette is the utility associated with being a weakwilled person who won’t be able to stick to her resolution of quitting smoking; and the diagnostic utility of choosing the different investment is the utility associated with being the kind of society that is heartless and does not care about the needy. A neo-Calvinist decisionmaker will refrain from the actions in (i), (ii) and (iii) on the grounds of their negative diagnostic utility, although their outcome utility is positive.
Group Equivariant Convolutional Neural Networks for Color Fundus Images Super-Resolution

High-resolution (HR) color fundus images can provide finer details and help with a more accurate diagnosis, while deep learning-based super-resolution methods usually require a large amount of data. This paper integrated Group Equivariant Convolution Neural Networks (G-CNNs) in MSRResNet for fundus images super-resolution. Experiments showed that it leads to some improvement compared with regular CNNs.
ReSPack: A Large-Scale Rectilinear Steiner Tree Packing Data Generator and Benchmark

Combinatorial optimization (CO) has been studied as a useful tool for modeling industrial problems, but it still remains a challenge in complex domains because of the NP-hardness. With recent advances in machine learning, the ﬁeld of CO is shifting to the study of neural combinatorial optimization using a large amount of data, showing promising results in some CO problems. Rectilinear Steiner tree packing problem (RSTPP) is a well-known CO problem and is widely used in modeling wiring problem among components in a printed circuit board and an integrated circuit design. Despite the importance of its application, the lack of available data has restricted to fully leverage machine learning approaches. In this paper, we present ReSPack, a large-scale synthetic RSTPP data generator and a benchmark. ReSPack includes a source code for generating RSTPP instances of various types with different sizes, test instances generated for the benchmark evaluation, and implementations of several baseline algorithms.
A Framework for Generating 3D Shape Counterfactuals

Many important problems in medical imaging require analysing the causal effect of genetic, environmental, or lifestyle factors on the normal and pathological variation of anatomical phenotypes. There is, however, a lack of computational tooling to enable causal reasoning about morphological variations of 3D surface meshes. To tackle this problem, we present the framework of deep structural causal shape models (CSMs) using a database of subcortical brain meshes. CSMs enable subject-specific prognoses through counterfactual mesh generation, by utilising high-quality mesh generation techniques, from geometric deep learning, within the expressive framework of deep structural causal models (DSCM).
Medical Scientific Table-to-Text Generation with Synthetic Data under Data Sparsity Constraint

An efficient table-to-text summarization system can drastically reduce manual efforts to understand and summarise tabular data into textual reports. However, in practice, the problem is heavily impeded by data sparsity and the inability of the state-of-the-art natural language generation models (such as T5, PEGASUS, and GPT-Neo) to produce coherent and accurate outputs. This is particularly true in pre-clinical and clinical domains. In this paper, we propose a novel table-to-text approach and tackle these problems with the help of synthetic data generation as well as copy mechanism. Experiments show that the proposed method can boost the performance of copying concise and relevant information from tabular data to generate assay validation and toxicology reports.
Undertaking complex but effective instructional supports for students: A systematic review of research on high-impact tutoring planning and implementation

This study synthesizes existing research on the implementation of tutoring programs which we define as one-to-one or small-group instruction in which a human tutor supports students grades K-12 in an academic subject area. Tutoring has emerged as an especially promising strategy for supporting students’ academic success with strong causal evidence finding large, positive effects on students' math and reading test scores across grade levels. Prior studies have reviewed this causal evidence of effects, but none have summarized the evidence on implementation. We iteratively developed search and selection criteria to identify studies addressing key research questions and synthesized these 40 studies which employ a range of research methodologies to describe how tutoring is implemented and experienced. We find that existing research provides rich descriptions of tutoring implementation within specific programs of focus, with most studies describing after-school tutoring and small-scale programs run by university professors. While few elements of implementation are studied in depth across multiple studies, common patterns emerge. Tutoring program launch is often facilitated by strategic relationships between schools and external tutoring providers and strengthened by transparent assessments of program quality and effectiveness. Successful tutoring implementation often hinges on the support of key school leaders with the power to direct the use of school funding, space, and time. Tutoring setting and schedule, tutor recruitment and training, and curriculum identification influence whether students are able to access tutoring services and the quality of the instruction provided. Ultimately, the evidence points to strong tutoring being driven by positive student-tutor relationships through which tutors provide instruction strategically targeted for students’ strengths and needs driving towards a long-term academic goal.
Attend, Select and Eliminate: Accelerating Multi-turn Response Selection with Dual-attention-based Content Elimination

Although the incorporation of pre-trained lan- 001 guage models (PLMs) significantly pushes the 002 research frontier of multi-turn response se- 003 lection, it brings a new issue of heavy com- 004 putation costs. To alleviate this problem 005 and make the PLM-based response selection 006 model both effective and efficient, we pro- 007 pose an inference framework together with 008 a post-training strategy that builds upon any 009 pre-trained transformer-based response selec- 010 tion models to accelerate inference by progres- 011 sively selecting and eliminating unimportant 012 content under the guidance of context-response 013 dual-attention. Specifically, at each trans- 014 former layer, we first identify the importance 015 of each word based on context-to-response 016 and response-to-context attention, then select 017 a number of unimportant words to be elimi- 018 nated following a retention configuration de- 019 rived from evolutionary search while passing 020 the rest of the representations into deeper layers. 021 To mitigate the training-inference gap posed 022 by content elimination, we introduce a post- 023 training strategy where we use knowledge dis- 024 tillation to force the model with progressively 025 eliminated content to mimic the predictions of 026 the original model with no content elimination. 027 Experiments on three benchmarks indicate that 028 our method can effectively speeds-up SOTA 029 models without much performance degradation 030 and shows a better trade-off between speed and 031 performance than previous methods. 032
Direct estimation of evolutionary power spectral density of nonstationary winds: A unified formulation

Estimating the evolutionary power spectral density (EPSD) of non-stationary winds (e.g., tropical storms and downbursts) is necessary to predict the response of structures under such extreme winds. Following the review of the existing direct estimation methods of EPSD, this paper offers a two-step unified formulation, i.e., raw estimation and associated error reduction. The raw estimation is expressed in terms of a time-frequency transform with a general kernel function. It is shown that if the kernel function is described by a time-frequency analysis tool such as the short-time Fourier transform, the wavelet transform, and the S-transform, the generalized raw EPSD estimation becomes a particular case of the existing methods. The unified estimation method presented here can be viewed as a filter bank with adjustable time and frequency resolution. The analysis of error in the raw estimation is carried out on the bias and random errors accounting for the approximation in both the time and frequency domains. Various techniques for reducing such errors are then summarized and recast in the unified formulation, including series expansion, short-time window smoothing, and multi-tapering. Based on the unified perspective, a discussion and some prospects of EPSD estimating are provided.
Finite Element Procedures for the Numerical Simulation of CRack Propagation and Bilateral Contact

This thesis concerns finite element methods for the numerical simulation of crack propagation including contact formulations for the contact between deformable bodies. The classical integral formulations for the energy release rate at crack growth are considered and adaptive algorithms are developed. In addition, we derive three different strategies for computing the material forces at a cracktip and the different strategies are compared. A new stabilized Lagrange multiplier method is presented where the multipliers are discretized with global polynomials. The method allows for non-matching meshes at the contact interface and the delicate problem of integrating products of basis functions between the master and slave body is alleviated. A discontinuous Galerkin method for cutting elements independent of the mesh structure is extended to the problem of a propagating crack whereby different strategies for computing the crack driving force are considered.
NGC 1817: the richest population of δ Scuti stars

We combined two sets of data to get high-precision time-series for stars in the open cluster NGC 1817. We verified most of the variable stars detected earlier and found some new ones. To the 12 δ Scuti variables detected prior to this article 4 new δ Scuti stars and one candidate have been added. In addition 3 γ Doradus candidates have been detected. The total number of variables and possible variables in NGC 1817, or in the field of view of the cluster is now up to 26. This high number of pulsating stars and especially δ Scuti variables makes this cluster an interesting asteroseismic target for future study. Using asteroseismology on δ Scuti stars will hopefully provide us with information that eventually will constrain the theoretical models of stars. This will in the end give us a much greater understanding of the evolution of stellar objects. Accepted: 2009, July 8 Individual Objects: NGC 1817
Revealing Anti-Ferromagnetic Domains with Linear Non-Reciprocal Optical Effects

I show that domains in anti-ferromagnetic crystals that break inversion symmetry as well as time-reversal symmetry can be characterized using linear non-reciprocal optical effects detected with Sagnac-interferometry-based microscopy. In addition, a simultaneous detection of these effects from oppositely aligned domains yields a background-free measurement of non-reciprocal optical susceptibilities.
Interpersonal Behaviors Questionnaire in Sport: Psychometric Analysis With Romanian Professional Athletes

Building upon self-determination theory, our objective in this research was to adapt and analyze psychometrically the Interpersonal Behaviors Questionnaire (IBQ) in sport. Our participants were 642 professional athletes (55.14% men, M age = 22.81) who completed an online survey measuring their perception of coaching behaviors, need satisfaction, need frustration, and motivation. The results showed a good fit for the 24-item IBQ with a six-factor correlated model (χ2/df = 4.178; CFI = .925; TLI = .912; SRMR = .048; RMSEA = .070), and we obtained evidence supporting its convergent and discriminant validity. The analyses also underpinned measurement invariance across gender, age, and sport, and confirmed good reliability. Criterion validity was met by positive associations of autonomy-supportive, competence-supportive, and relatedness-supportive behaviors with need satisfaction and autonomous motivation; and of autonomy-thwarting, competence-thwarting and relatedness-thwarting behaviors with need frustration, controlled motivation and amotivation. The adapted IBQ can be applied to the assessment of professional Romanian athletes’ perceptions of need-supportive and need-thwarting coaching behaviors.
Deformation of Rapidly Rotating Compact Stars

We have developed a numerical code to study the deformation ($\varepsilon = (I_{zz}-I_{xx})/I_{zz}$, where $I_{ii}$ are the moments of inertia) of neutron stars in rapidly rotation in a fully general relativistic calculation. We have found that the deformation is larger, depending on the angular velocity, than is generally assumed for gravitational wave estimations. Calculations were performed by employing the Bethe-Johnson I EOS (equation of state) and a new set of models by the Frankfurt group including $\Lambda$ hyperons for several choices of their coupling constants to ordinary nucleons. Possible implications for gravitational wave searches are briefly discussed.
Helicobacter pylori Infection Elicits Type I Interferon Response in Human Monocytes via Toll-Like Receptor 8 Signaling

Helicobacter pylori colonization and persistence could precede gastric adenocarcinoma. Elucidating immune recognition strategies of H. pylori is therefore imperative to curb chronic persistence in the human host. Toll-like receptor 7 (TLR7) and TLR8 are widely known as viral single-stranded RNA (ssRNA) sensors yet less studied in the bacteria context. Here, we investigated the involvement of these receptors in the immunity to H. pylori. Human THP-1 monocytic cells were infected with H. pylori, and the expression levels of human Toll-like receptors (TLRs) were examined. The roles of TLR7 and TLR8 in response to H. pylori infection were further investigated using receptor antagonists. Among all TLR transcripts examined, TLR8 exhibited the most prominent upregulation, followed by TLR7 in the THP-1 cells infected with H. pylori J99 or SS1 strains. H. pylori infection-mediated IFN-α and IFN-β transactivation was significantly abrogated by the TLR7/8 (but not TLR7) antagonist. Additionally, TLR7/8 antagonist treatment reduced H. pylori infection-mediated phosphorylation of interferon regulatory factor 7 (IRF7). Our study suggests a novel role of TLR8 signaling in host immunity against H. pylori through sensing live bacteria to elicit the production of type I interferon.
Landau functions for noninteracting bosons

We discuss the statistics of Bose-Einstein condensation (BEC) in a canonical ensemble of N noninteracting bosons in terms of a Landau function L{sub N}{sup BEC}(q) defined by the logarithm of the probability distribution of the order parameter q for BEC. We also discuss the corresponding Landau function for spontaneous symmetry breaking (SSB), which for finite N should be distinguished from L{sub N}{sup BEC}(q). Only for N{yields}{infinity} BEC and SSB can be described by the same Landau function which depends on the dimensionality and on the form of the external potential in a surprisingly complex manner. For bosons confined by a three-dimensional harmonic trap the Landau function exhibits the usual behavior expected for continuous phase transitions.
Constraining astrophysical neutrino flavor composition from leptonic unitarity

The recent IceCube observation of ultra-high-energy astrophysical neutrinos has begun the era of neutrino astronomy. In this work, using the unitarity of leptonic mixing matrix, we derive nontrivial unitarity constraints on the flavor composition of astrophysical neutrinos detected by IceCube. Applying leptonic unitarity triangles, we deduce these unitarity bounds from geometrical conditions, such as triangular inequalities. These new bounds generally hold for three flavor neutrinos, and are independent of any experimental input or the pattern of lepton mixing. We apply our unitarity bounds to derive general constraints on the flavor compositions for three types of astrophysical neutrino sources (and their general mixture), and compare them with the IceCube measurements. Furthermore, we prove that for any sources without ντ neutrinos, a detected νμ flux ratio < 1/4 will require the initial flavor composition with more νe neutrinos than νμ neutrinos.
Scaling limits of random graph models at criticality: Universality and the basin of attraction of the Erdős-Rényi random graph

Over the last few years a wide array of random graph models have been postulated to understand properties of empirically observed networks. Most of these models come with a parameter t (usually related to edge density) and a (model dependent) critical time tc which specifies when a giant component emerges. There is evidence to support that for a wide class of models, under moment conditions, the nature of this emergence is universal and looks like the classical Erd\H{o}s-R\'enyi random graph, in the sense of the critical scaling window and (a) the sizes of the components in this window (all maximal component sizes scaling like n2/3) and (b) the structure of components (rescaled by n-1/3) converge to random fractals related to the continuum random tree. Till date, (a) has been proven for a number of models using different techniques while (b) has been proven for only two models, the classical Erd\H{o}s-R\'enyi random graph and the rank-1 inhomogeneous random graph. The aim of this paper is to develop a general program for proving such results. The program requires three main ingredients: (i) in the critical scaling window, components merge approximately like the multiplicative coalescent (ii) scaling exponents of susceptibility functions are the same as the Erd\H{o}s-R\'enyi random graph and (iii) macroscopic averaging of expected distances between random points in the same component in the barely subcritical regime. We show that these apply to a number of fundamental random graph models including the configuration model, inhomogeneous random graphs modulated via a finite kernel and bounded size rules. Thus these models all belong to the domain of attraction of the classical Erd\H{o}s-R\'enyi random graph. As a by product we also get results for component sizes at criticality for a general class of inhomogeneous random graphs.