Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamics-based density profile for dark haloes -- II. Fitting function

The density proﬁles of dark matter haloes are commonly described by ﬁtting functions such as the NFW or Einasto models, but these approximations break down in the transition region where halos become dominated by newly accreting matter. In Paper I we dynamically split simulation particles into orbiting and infalling components and analysed their separate proﬁles. Here we propose simple, accurate ﬁtting functions designed to capture the asymptotic shapes of the two terms at large and small radii. The orbiting term is described as a truncated Einasto proﬁle, ρ orb ∝ exp (cid:104) − 2 /α ( r / r s ) α − 1 /β ( r / r t ) β (cid:105) , with a ﬁve-parameter space of normalization, physically distinct scale and truncation radii, and α and β , which control how rapidly the proﬁles steepen. The infalling proﬁle is modelled as a power law in overdensity that smoothly transitions to a constant at the halo centre. We show that these formulae ﬁt the averaged, total proﬁles in simulations to about 5% accuracy across almost all of an expansive parameter space in halo mass, redshift, cosmology, and accretion rate. When ﬁxing α = 0 . 18 and β = 3, the formula becomes a three-parameter model for the orbiting term that ﬁts individual halos better than the Einasto proﬁle on average.
Identifying synthetic voices qualities for conversational agents

The present study aims to explore user acceptance and perceptions toward different quality levels of synthetical voices. To achieve this, four voices have been exploited considering two main factors: the quality of the voices (low vs high) and their gender (male and female). 186 volunteers were recruited and subsequently allocated into four groups of different ages respec-tively, adolescents, young adults, middle-aged and seniors. After having randomly listened to each voice, participants were asked to fill the Virtual Agent Voice Acceptance Questionnaire (VAVAQ). Outcomes show that the two higher quality voices of Antonio and Giulia were more appreciated than the low-quality voices of Edoardo and Clara by the whole sample in terms of pragmatic, hedonic and attractiveness qualities attributed to the voices. Concerning preferences towards differently aged voices, it clearly appeared that they varied according to participants age' ranges examined. Furthermore, in terms of suitability to perform different tasks, participants considered Antonio and Giulia equally adapt for healthcare and front office jobs. Antonio was also judged to be significantly more qualified to accomplish protection and security tasks, while Edoardo was classified as the absolute least skilled in conducting household chores.
A Structured Span Selector

Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstream processing. This approach, however, does not incorporate any inductive bias about what sort of spans ought to be selected, e.g., that selected spans tend to be syntactic constituents. In this paper, we propose a novel grammar-based structured span selection model which learns to make use of the partial span-level annotation provided for such problems. Compared to previous approaches, our approach gets rid of the heuristic greedy span selection scheme, allowing us to model the downstream task on an optimal set of spans. We evaluate our model on two popular span prediction tasks: coreference resolution and semantic role labeling; and show improvements on both.
Better Retrieval May Not Lead to Better Question Answering

Considerable progress has been made recently in open-domain question answering (QA) problems, which require Information Retrieval (IR) and Reading Comprehension (RC). A popular approach to improve the system's performance is to improve the quality of the retrieved context from the IR stage. In this work we show that for StrategyQA, a challenging open-domain QA dataset that requires multi-hop reasoning, this common approach is surprisingly ineffective -- improving the quality of the retrieved context hardly improves the system's performance. We further analyze the system's behavior to identify potential reasons.
Long-term Multiband Near-infrared Variability of the Blazar OJ 287 during 2007–2021

We present the most extensive and well-sampled long-term multiband near-infrared (NIR) temporal and spectral variability study of OJ 287, considered to be the best candidate binary supermassive black hole blazar. These observations were made between 2007 December and 2021 November. The source underwent ∼2–2.5 mag variations in the J, H, and Ks NIR bands. Over these long-term timescales there were no systematic trends in either flux or spectral evolution with time or with the source’s flux states. However, on shorter timescales, there are significant variations in flux and spectra indicative of strong changes during different activity states. The NIR spectral energy distributions show diverse facets at each flux state, from the lowest to the highest. The spectra are, in general, consistent with a power-law spectral profile (within 10%) and many of them indicate minor changes (observationally insignificant) in the shift of the peak. The NIR spectra generally steepen during bright phases. We briefly discuss these behaviors in the context of blazar emission scenarios/mechanisms, OJ 287's well-known traditional behavior, and implications for models of the source central engine invoked for its long-term optical semiperiodic variations.
Exploring the Delivery of Swimming and Water Safety Teacher Training to Culturally and Linguistically Diverse Communities

The dearth of information on the delivery of specific culturally and linguistically diverse (CALD) programs presents a major limitation in the effort to prevent drowning, particularly when drowning rates of people from countries other than Australia are increasing. This study describes programs delivered by the aquatic industry for CALD communities in New South Wales (NSW), explores what CALD communities believe their water safety education needs are, and evaluates AUSTSWIM’s current training methods to ascertain which is most effective when training candidates from CALD communities. We found an absence of specialized aquatic programs being run at aquatic facilities for CALD communities. Barriers to participation included a lack of understanding of the cultural perceptions toward water safety and the different emphasis on the need for swimming skills and water safety education. The likelihood of AUSTSWIM Teacher of Swimming and Water Safety candidates’ having successful aquatic training outcomes increases with a combined learning approach that pairs meaningful practical applications with ongoing mentor support. Engagement of CALD communities’ using these kind of water safety programs will be essential if Australia wants to reduce the drowning burden.
Hausdorff dimension in quasiregular dynamics

It is shown that the Hausdorﬀ dimension of the fast escaping set of a quasiregular self-map of R 3 can take any value in the interval [1 , 3]. The Hausdorﬀ dimension of the Julia set of such a map is estimated under some growth condition.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result, RE still suffers from unintended entity bias, i.e., the spurious correlation between entity mentions (names) and relations. Entity bias can mislead the RE models to extract the relations that do not exist in the text. To combat this issue, some previous work masks the entity mentions to prevent the RE models from over-fitting entity mentions. However, this strategy degrades the RE performance because it loses the semantic information of entities. In this paper, we propose the CoRE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information. We first construct a causal graph for RE, which models the dependencies between variables in RE models. Then, we propose to conduct counterfactual analysis on our causal graph to distill and mitigate the entity bias, that captures the causal effects of specific entity mentions in each instance. Note that our CoRE method is model-agnostic to debias existing RE systems during inference without changing their training processes. Extensive experimental results demonstrate that our CoRE yields significant gains on both effectiveness and generalization for RE. The source code is provided at: https://github.com/vanoracai/CoRE.
Monopole Floer homology and invariant theta characteristics

We describe a relationship between the monopole Floer homology of three-manifolds and the geometry of Riemann surfaces. Consider an automorphism ϕ of a compact Riemann surface Σ with quotient P 1 . There is a natural correspondence between theta characteristics L on Σ which are invariant under ϕ and self-conjugate spin c structures s L on the mapping torus M ϕ of ϕ . We show that the monopole Floer homology groups of p M ϕ , s L q are explicitly determined by the eigenvalues of the (lift of the) action of ϕ on H 0 p L q , the space of holomorphic sections of L . Decategorifying our computation, we also obtain that the dimension of H 0 p L q equals the Reidemeister-Turaev torsion of p M ϕ , s L q . Finally, we combine our description with the Atiyah-Bott G -spin theorem to provide explicit computations of the Floer homology groups for all automorphisms ϕ of prime order in terms of ramiﬁcation data.
Robustness of Kardar-Parisi-Zhang scaling in a classical integrable spin chain with broken integrability

Recent investigations have observed superdiffusion in integrable classical and quantum spin chains. An intriguing connection between these spin chains and Kardar-Parisi-Zhang (KPZ) universality class has emerged. Theoretical developments (e.g. generalized hydrodynamics) have highlighted the role of integrability as well as spin-symmetry in KPZ behaviour. However understanding their precise role on superdiffusive transport still remains a challenging task. The widely used quantum spin chain platform comes with severe numerical limitations. To circumvent this barrier, we focus on a classical integrable spin chain which was shown to have deep analogy with the quantum spin-$\frac{1}{2}$ Heisenberg chain. Remarkably, we find that KPZ behaviour prevails even when one considers integrability-breaking but spin-symmetry preserving terms, strongly indicating that spin-symmetry plays a central role even in the non-perturbative regime. On the other hand, in the non-perturbative regime, we find that energy correlations exhibit clear diffusive behaviour. We also study the classical analog of out-of-time-ordered correlator (OTOC) and Lyapunov exponents. We find significant presence of chaos for the integrability-broken cases even though KPZ behaviour remains robust. The robustness of KPZ behaviour is demonstrated for a wide class of spin-symmetry preserving integrability-breaking terms.
Structure Learning in Graphical Models from Indirect Observations

This paper considers learning of the graphical structure of a p -dimensional random vector X ∈ R p using both parametric and non-parametric methods. Unlike the previous works which observe x directly, we consider the indirect observation scenario in which samples y are collected via a sensing matrix A ∈ R d × p , and corrupted with some additive noise w , i.e, Y = AX + W . For the parametric method, we assume X to be Gaussian, i.e., x ∈ R p ∼ N ( µ, Σ ) , µ ∈ R p , and Σ ∈ R p × p . For the ﬁrst time, we show that the correct graphical structure can be correctly recovered under the indeﬁnite sensing system ( d < p ) using insufﬁcient samples ( n < p ). In particular, we show that for the exact recovery, we require dimension d = Ω (cid:0) p 0 . 8 (cid:1) and sample number n = Ω (cid:0) p 0 . 8 log 3 p (cid:1) . For the nonparametric method, we assume a nonparanormal distribution for X rather than Gaussian. Under mild conditions, we show that our graph-structure estimator can obtain the correct structure. We derive the minimum sample number n and dimension d as n & ( deg ) 4 log 4 n and d & p + ( deg · log( d − p )) β/ 4 , respectively, where deg is the maximum Markov blanket in the graphical model and β > 0 is some ﬁxed positive constant. Additionally, we obtain a non-asymptotic uniform bound on the estimation error of the CDF of X from indirect observations with inexact knowledge of the noise distribution.
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing-over-time properties, making its analysis a complex task. Hence, improvement of understanding and development of more informative, generalisable market representations are essential for the successful operation in financial markets, including risk assessment, diversification, trading, and order execution. In this study, we propose a volume-price-based market representation for making financial time series more suitable for machine learning pipelines. We use a statistical approach for evaluating the representation. Through the research questions, we investigate, i) whether the proposed representation allows the more efficient design of machine learning models; ii) whether the proposed representation leads to increased performance over the price levels market pattern; iii) whether the proposed representation performs better on the liquid markets, and iv) whether SHAP feature interactions are reliable to be used in the considered setting. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than the price levelsbased method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models and compare the outcomes to those of the SHAP method. This results in the significant similarity between the two methods, hence we claim that SHAP feature interactions are reliable to be used in the setting of financial markets.
Fractional quantum Hall effect in semiconductor systems

The fractional quantum Hall (FQH) effect refers to the strongly-correlated phenomena and the associated quantum phases of matter realized in a two-dimensional gas of electrons placed in a large perpendicular magnetic field. In such systems, topology and quantum mechanics conspire to give rise to exotic physics that manifests via robust quantization of the Hall resistance. In this chapter, we provide an overview of the experimental phenomenology of the FQH effect in GaAs-based semiconductor materials and present its theoretical interpretations in terms of trial wave functions, composite fermion quasiparticles, and enigmatic non-Abelian states. We also highlight some recent developments, including the parton theory and the Dirac composite fermion field theory of FQH states, the role of anisotropy and geometrical degrees of freedom, and quantum entanglement in FQH fluids.
NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations

Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital humans. However, existing monocular hair modeling methods are either tricky to deploy in digital systems (e.g., due to their dependence on complex user interactions or large databases) or can produce only a coarse geometry. In this paper, we introduce NeuralHDHair, a flexible, fully automatic system for modeling high-fidelity hair from a single image. The key enablers of our system are two carefully designed neural networks: an IRHairNet (Im-plicit representation for hair using neural network) for inferring high-fidelity 3D hair geometric features (3D orientation field and 3D occupancy field) hierarchically and a GrowingNet (Growing hair strands using neural network) to efficiently generate 3D hair strands in parallel. Specifically, we perform a coarse-to-fine manner and propose a novel voxel-aligned implicit function (VIFu) to represent the global hair feature, which is further enhanced by the local details extracted from a hair luminance map. To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features. Extensive ex-periments show that our method is capable of constructing a high-fidelity 3D hair model from a single image, both efficiently and effectively, and achieves the-state-of-the-art performance.
Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach

The robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, the shape control of DLOs is challenging, especially for large deformation control that requires global and more accurate models. In this article, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model and achieve the large deformation control of untrained DLOs in 2-D and 3-D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning.
A coherent differential PCF

The categorical models of the differential lambda-calculus are additive categories because of the Leibniz rule which requires the summation of two expressions. This means that, as far as the differential lambda-calculus and differential linear logic are concerned, these models feature finite non-determinism and indeed these languages are essentially non-deterministic. In a previous paper we introduced a categorical framework for differentiation which does not require additivity and is compatible with deterministic models such as coherence spaces and probabilistic models such as probabilistic coherence spaces. Based on this semantics we develop a syntax of a deterministic version of the differential lambda-calculus. One nice feature of this new approach to differentiation is that it is compatible with general fixpoints of terms, so our language is actually a differential extension of PCF for which we provide a fully deterministic operational semantics.
EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers

Self-attention based models such as vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer vision. Despite increasingly stronger variants with ever-higher recognition accuracies, due to the quadratic complexity of self-attention, existing ViTs are typically demanding in computation and model size. Although several successful design choices (e.g., the convolutions and hierarchical multi-stage structure) of prior CNNs have been reintroduced into recent ViTs, they are still not sufficient to meet the limited resource requirements of mobile devices. This motivates a very recent attempt to develop light ViTs based on the state-of-the-art MobileNet-v2, but still leaves a performance gap behind. In this work, pushing further along this under-studied direction we introduce EdgeViTs, a new family of light-weight ViTs that, for the first time, enable attention-based vision models to compete with the best light-weight CNNs in the tradeoff between accuracy and on-device efficiency. This is realized by introducing a highly cost-effective local-global-local (LGL) information exchange bottleneck based on optimal integration of self-attention and convolutions. For device-dedicated evaluation, rather than relying on inaccurate proxies like the number of FLOPs or parameters, we adopt a practical approach of focusing directly on on-device latency and, for the first time, energy efficiency. Specifically, we show that our models are Pareto-optimal when both accuracy-latency and accuracy-energy trade-offs are considered, achieving strict dominance over other ViTs in almost all cases and competing with the most efficient CNNs. Code is available at https://github.com/saic-fi/edgevit.
Spin interaction and magnetism in cobaltate Kitaev candidate materials: An 
ab initio
 and model Hamiltonian approach

In the quest for materials hosting Kitaev spin liquids, much of the eﬀorts have been focused on the fourth- and ﬁfth-row transition metal compounds, which are spin-orbit coupling assisted Mott insulators. Here, we study the structural and magnetic properties of 3 d transition metal oxides, Na 2 Co 2 TeO 6 and Na 3 Co 2 SbO 6 . The partial occupancy of sodium in former compound is addressed using a cluster expansion, and a honeycomb lattice of sodiums is found to be energetically favored. Starting from the ab initio band structures, a many-body second order perturbation theory leads to a pseudospin- 12 Hamiltonian with estimated magnetic interactions. We show that the experimentally observed zigzag magnetic state is stabilised only when the ﬁrst neighbour Kitaev coupling dominates over the Heisenberg term, both of which are highly suppressed due to presence of e g orbitals. A third neighbour Heisenberg interaction is found dominant in both these compounds. We also present a phase diagram for Na 2 Co 2 TeO 6 by varying Hubbard- U and spin-orbit coupling- λ . Spin excitation spectra are found to capture essential features of recent experimental magnon spetrum, lending support to our results.
Moving from continuous to discrete symmetry in the 2D XY model

We study the effects of discretization on the U(1) symmetric XY model in two dimensions using the Higher Order Tensor Renormalization Group (HOTRG) approach. Regarding the $Z_N$ symmetric clock models as specific discretizations of the XY model, we compare those discretizations to ones from truncations of the tensor network formulation of the XY model based on a character expansion, and focus on the differences in their phase structure at low temperatures. We also divide the tensor network formulations into core and interaction tensors and show that the core tensor has the dominant influence on the phase structure. Lastly, we examine a perturbed form of the XY model that continuously interpolates between the XY and clock models. We examine the behavior of the additional phase transition caused by the perturbation as the magnitude of perturbation is taken to zero. We find that this additional transition has a non-zero critical temperature as the perturbation vanishes, suggesting that even small perturbations can have a significant effect on the phase structure of the theory.