Miracle-dz/UniFAR-FTUs huggingface dataset:
Tidal evolution of cored and cuspy dark matter halos

The internal structure and abundance of dark matter halos and subhalos are powerful probes of the nature of dark matter. In order to compare observations with dark matter models, accurate theoretical predictions of these quantities are needed. We present a fast and accurate method to describe the tidal evolution of subhalos within their parent halo, based on a semi-analytic approach. We first consider idealized N-body simulations of subhalos within their host halo, using a generalized mass density profile that describes their properties in a variety of dark matter models at infall, including popular warm, cold, and self-interacting ones. Using these simulations we construct tidal"tracks"for the evolution of subhalos based on their conditions at infall. Second, we use the results of these simulations to build semi-analytic models (SAMs) for tidal effects, including stripping and heating and implement them within the code GALACTICUS. Our SAMs can accurately predict the tidal evolution of both cored and cuspy subhalos, including the bound mass and density profiles, providing a powerful and efficient tool for studying the post-infall properties of subhalos in different dark matter models.
GiT: Towards Generalist Vision Transformer through Universal Language Interface

This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large language models (LLMs), we seek to broaden its scope to serve as a powerful vision foundation model (VFM). However, unlike language modeling, visual tasks typically require specific modules, such as bounding box heads for detection and pixel decoders for segmentation, greatly hindering the application of powerful multi-layer transformers in the vision domain. To solve this, we design a universal language interface that empowers the successful auto-regressive decoding to adeptly unify various visual tasks, from image-level understanding (e.g., captioning), over sparse perception (e.g., detection), to dense prediction (e.g., segmentation). Based on the above designs, the entire model is composed solely of a ViT, without any specific additions, offering a remarkable architectural simplification. GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning. Interestingly, our GiT builds a new benchmark in generalist performance, and fosters mutual enhancement across tasks, leading to significant improvements compared to isolated training. This reflects a similar impact observed in LLMs. Further enriching training with 27 datasets, GiT achieves strong zero-shot results over various tasks. Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language. Code and models will be available at \url{https://github.com/Haiyang-W/GiT}.
Complexity Classification of Complex-Weighted Counting Acyclic Constraint Satisfaction Problems

We study the computational complexity of counting constraint satisfaction problems (#CSPs) whose constraints assign complex numbers to Boolean inputs when the corresponding constraint hypergraphs are acyclic. These problems are called acyclic #CSPs or succinctly, #ACSPs. We wish to determine the computational complexity of all such #ACSPs when arbitrary unary constraints are freely available. Depending on whether we further allow or disallow the free use of the specific constraint XOR (binary disequality), we present two complexity classifications of the #ACSPs according to the types of constraints used for the problems. When XOR is freely available, we first obtain a complete dichotomy classification. On the contrary, when XOR is not available for free, we then obtain a trichotomy classification. To deal with an acyclic nature of constraints in those classifications, we develop a new technical tool called acyclic-T-constructibility or AT-constructibility, and we exploit it to analyze a complexity upper bound of each #ACSPs.
Self-adaptive, Requirements-driven Autoscaling of Microservices

Microservices architecture offers various benefits, including granularity, flexibility, and scalability. A crucial feature of this architecture is the ability to autoscale microservices, i.e., adjust the number of replicas and/or manage resources. Several autoscaling solutions already exist. Nonetheless, when employed for diverse microservices compositions, current solutions may exhibit suboptimal resource allocations, either exceeding the actual requirements or falling short. This can in turn lead to unbalanced environments, downtime, and undesirable infrastructure costs. We propose MS-RA, a self-adaptive, requirements-driven solution for microservices autoscaling. MS-RA utilizes service-level objectives (SLOs) for real-time decision making. Our solution, which is customizable to specific needs and costs, facilitates a more efficient allocation of resources by precisely using the right amount to meet the defined requirements. We have developed MS-RA based on the MAPE-K self-adaptive loop, and have evaluated it using an open-source microservice-based application. Our results indicate that MS-RA considerably outperforms the horizontal pod autoscaler (HPA), the industry-standard Kubernetes autoscaling mechanism. It achieves this by using fewer resources while still ensuring the satisfaction of the SLOs of interest. Specifically, MS-RA meets the SLO requirements of our case-study system, requiring at least 50% less CPU time, 87% less memory, and 90% fewer replicas compared to the HPA.
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation

Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language. However, it remains challenging for existing 3D generation methods to create subject-driven 3D content across diverse prompts. In this paper, we introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5 minutes. Our key insight is to harmonize the distributions of a multi-view diffusion model and an identity-specific 2D generative model, aligning them with the distribution of the desired 3D subject. Specifically, we design a co-evolution framework to reduce the variance of distributions, where each model undergoes a process of learning from the other through identity-aware optimization and subject-prior optimization, respectively. Extensive experiments demonstrate that our method can produce high-quality, consistent, and subject-specific 3D content with text-driven modifications that are unseen in subject image.
Unveiling the Generalization Power of Fine-Tuned Large Language Models

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning. However, the comprehensive effects of fine-tuning on the LLMs' generalization ability are not fully understood. This paper delves into the differences between original, unmodified LLMs and their fine-tuned variants. Our primary investigation centers on whether fine-tuning affects the generalization ability intrinsic to LLMs. To elaborate on this, we conduct extensive experiments across five distinct language tasks on various datasets. Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. Intriguingly, we observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability. Through this systematic investigation, we aim to contribute valuable insights into the evolving landscape of fine-tuning practices for LLMs.
de Leeuw representations of functionals on Lipschitz spaces

Let $\mathrm{Lip}_0(M)$ be the space of Lipschitz functions on a complete metric space $(M,d)$ that vanish at a point $0\in M$. We investigate its dual $\mathrm{Lip}_0(M)^*$ using the de Leeuw transform, which allows representing each functional on $\mathrm{Lip}_0(M)$ as a (non-unique) measure on $\beta\widetilde{M}$, where $\widetilde{M}$ is the space of pairs $(x,y)\in M\times M$, $x\neq y$. We distinguish a set of points of $\beta\widetilde{M}$ that are"away from infinity", which can be assigned coordinates belonging to the Lipschitz realcompactification $M^{\mathcal{R}}$ of $M$. We define a natural metric $\bar{d}$ on $M^{\mathcal{R}}$ extending $d$ and we show that optimal (i.e. positive and norm-minimal) de Leeuw representations of well-behaved functionals are characterised by $\bar{d}$-cyclical monotonicity of their support, extending known results for functionals in $\mathcal{F}(M)$, the predual of $\mathrm{Lip}_0(M)$. We also extend the Kantorovich-Rubinstein theorem to normal Hausdorff spaces, in particular to $M^{\mathcal{R}}$, and use this to characterise measure-induced and majorisable functionals in $\mathrm{Lip}_0(M)^*$ as those admitting optimal representations with additional finiteness properties. Finally, we use de Leeuw representations to define a natural L-projection of $\mathrm{Lip}_0(M)^*$ onto $\mathcal{F}(M)$ under some conditions on $M$.
Perspectives on physics-based one-dimensional modeling of lung physiology

The need to understand how infection spreads to the deep lung was acutely realized during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic. The challenge of modeling virus laden aerosol transport and deposition in the airways, coupled with mucus clearance, and infection kinetics, became evident. This perspective provides a consolidated view of coupled one-dimensional physics-based mathematical models to probe multifaceted aspects of lung physiology. Successes of 1D trumpet models in providing mechanistic insights into lung function and optimalities are reviewed while identifying limitations and future directions. Key non-dimensional numbers defining lung function are reported. The need to quantitatively map various pathologies on a physics-based parameter space of non-dimensional numbers (a virtual disease landscape) is noted with an eye on translating modeling to clinical practice. This could aid in disease diagnosis, get mechanistic insights into pathologies, and determine patient specific treatment plan. 1D modeling could be an important tool in developing novel measurement and analysis platforms that could be deployed at point-of-care.
Sequential optimal experimental design for vapor-liquid equilibrium modeling

We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in particular, in vapor-liquid equilibrium modeling. As a sequential design method, our method iteratively alternates between performing experiments, updating parameter estimates, and computing new experiments. Specifically, our sequential design method computes a whole batch of new experiments in each iteration and this batch of new experiments is designed in a two-stage locally optimal manner. In essence, this means that in every iteration the combined information content of the newly proposed experiments and of the already performed experiments is maximized. In order to solve these two-stage locally optimal design problems, a recent and efficient adaptive discretization algorithm is used. We demonstrate the benefits of the proposed methodology on the example of of the parameter estimation for the non-random two-liquid model for narrow azeotropic vapor-liquid equilibria. As it turns out, our sequential optimal design method requires substantially fewer experiments than traditional factorial design to achieve the same model precision and prediction quality. Consequently, our method can contribute to a substantially reduced experimental effort in vapor-liquid equilibrium modeling and beyond.
Working Paper Series Consumers' payment preferences and banking digitalisation in the euro area

This paper contributes to understanding consumers' retail payment preferences and digitalisation in personal finances. We focus on the acceptance of cashless payments in everyday situations and the use of mobile banking apps in the euro area, where the payment services market has changed significantly in recent years. In particular, we study app-based tools for day-to-day (offline) purchases that involve small amounts of money as well as digital tools for managing personal finances. By looking at factors associated with using non-cash payment methods, and app-based financial services solutions, we shed light on the topic of financial inclusion in payment services that concern consumers’ everyday choices. Using granular microdata from the European Central Bank's Consumer Expectations Survey, we find that most people prefer to use only one payment instrument. After the COVID-19 pandemic, it has mostly been cash and contactless cards. The use of cash is partly due to limited perceived acceptance of non-cash payments by merchants. We also find substantial cross-country heterogeneity and highlight the prominent role of demographic factors in choosing non-cash payment options and app-based tools when managing personal finances. While mobile banking is already popular amongst euro area consumers, the use of smart payment methods remains very limited. Our findings suggest that financial service providers should recognize the growing preference of the younger generations for alternative payment methods. Creating awareness among consumers might also lead to positive feedback effects by reducing consumers’ reliance on cash through higher perceived availability of non-cash payment options.
Up and Down Quark Structure of the Proton

We measure proton structure parameters sensitive primarily to valence quarks using 8.6 fb$^{-1}$ of data collected by the D0 detector in $\sqrt{s}=1.96$ TeV $p\bar{p}$ collisions at the Fermilab Tevatron. We exploit the property of the forward-backward asymmetry in dilepton events to be factorized in to distinct structure parameters and electroweak quark-level asymmetries. Contributions to the asymmetry from $s$, $c$ and $b$ quarks, as well as from $u$ and $d$ quarks, are suppressed allowing valence $u$ and $d$ quarks to be separately determined. We find and $u$ to $d$ quark ratio near the peak values in the quark density distributions that is smaller than predictions from modern parton distribution functions.
Quantum analog of Landau-Lifshitz-Gilbert dynamics

The Landau-Lifshitz-Gilbert (LLG) and Landau-Lifshitz (LL) equations play an essential role for describing the dynamics of magnetization in solids. While a quantum analog of the LL dynamics has been proposed in [Phys. Rev. Lett. 110, 147201 (2013)], the corresponding quantum version of LLG remains unknown. Here, we propose such a quantum LLG equation that inherently conserves purity of the quantum state. We examine the quantum LLG dynamics of a dimer consisting of two interacting spin-1/2 particles. Our analysis reveals that, in the case of ferromagnetic coupling, the evolution of initially uncorrelated spins mirrors the classical LLG dynamics. However, in the antiferromagnetic scenario, we observe pronounced deviations from classical behavior, underscoring the unique dynamics of becoming a spinless state, which is non-locally correlated. Moreover, when considering spins that are initially correlated, our study uncovers an unusual form of transient quantum correlation dynamics, which differ significantly from what is typically seen in open quantum systems.
Rh alloimmunization awareness among pregnant women in the north of Jordan: a cross sectional study

Objective. Rh alloimmunization is a condition where antibodies develop against fetal red blood cell antigens; this can lead to severe complications. However, studies on the awareness among pregnant women about this condition remain limited. This cross-sectional study aims to assess the awareness of Rh alloimmunization among pregnant women in the north of Jordan as a preliminary step toward enhancing the quality of healthcare services provided. Materials and Methods. In this study, a total of 403 pregnant women were enrolled. Data were collected from pregnant women attending antenatal clinics at King Abdullah University Hospital and Princess Badea’a Hospital using a validated questionnaire. The questionnaire covered various aspects including demographics, awareness of blood type, current pregnancy history, anti-D immunoglobulin administration, and knowledge of Rh alloimmunization. Data analysis was conducted using Statistical Package for Social Sciences
Use of epilithic diatoms as bioindicators from lotic systems in southern Brazil, with special emphasis on eutrophication.

: Use of epilithic diatoms as bioindicators from lotic systems in southern brazil, with special emphasis on eutrophication. Epilithic diatom biocenoses have been recommended by researchers from several countries as particularly suitable for water quality evaluation. However, saprobic systems using diatoms have been developed to assess water organic pollution, not taking into account the effects of eutrophication on the biological composition of the communities. Thus, the main aim of this study was to determine the tolerance of diatom species to eutrophication in three streams (Sampaio, Grande and Bonito) of the Mato Leitão countryside area, RS , Brazil, using multivariate analyses techniques. Results of physical, chemical and biological analyses obtained in studies carried on in the area between the years of 1993 to 1998 were used to develop the present study. Multivariate analyses of species composition data were applied in two different approaches. Firstly, species and sampling units were grouped using TWINSPAN (Two-way Indicator Species Analysis). Secondly, Canonical Correspondence Analysis (CCA) was applied in order to uncover the main gradients of changes in species composition, relating these changes to the eutrophication process. From the levels of tolerance to eutrophication determined for each diatom species, they were given an indicative value from 1 to 5, which corresponded, respectively, to very low, low, medium, high and very high tolerance levels, thus allowing the calculation of the Biological Water Quality Index (BWQI). These results are complementary to the saprobic system already proposed for use in Southern Brazilian rivers.
OF A

† Background and Aims Attempts to answer the old question of whether high diversity causes high invasion resistance have resulted in an invasion paradox: while large-scale studies often ﬁnd a positive relationship between diversity and invasibility, small-scale experimental studies often ﬁnd a negative relationship. Many of the small-scale studies are conducted in artiﬁcial communities of even-aged plants. Species in natural communities, however, do not represent one simultaneous cohort and occur at various levels of spatial aggregation at different scales. This study used natural patterns of diversity to assess the relationship between diversity and invasibility within a uniformly managed, semi-natural community. † Methods In species-rich grassland, one seed of each of ten species was added to each of 50 contiguous 16 cm 2 quadrats within seven plots (8 × 100 cm). The emergence of these species was recorded in seven control plots, and establishment success was measured in relation to the species diversity of the resident vegetation at two spatial scales, quadrat (64 cm 2 ) within plots (800 cm 2 ) and between plots within the site (approx. 400 m 2 ) over 46 months. † Key Results Invader success was positively related to resident species diversity and richness over a range of 28–37 species per plot. This relationship emerged 7 months after seed addition and remained over time despite continuous mortality of invaders. † Conclusions Biotic resistance to plant invasion may play only a sub-ordinate role in species-rich, semi-natural grassland. As possible alternative explanations for the positive diversity–invasibility relationship are not clear, it is recommended that future studies elaborate ﬁne-scale environmental heterogeneity in resource supplies or potential resource ﬂows from resident species to seedlings by means of soil biological networks established by arbuscular mycorrhizal fungi.
Age of Great Grey Owls Strix nebulosa observed in Scandinavia in 2012 as revealed by digital photos in the national species report archives.

dead owls brought to the Natural History Museum in Stockholm, the percentages of owls hatched in 2011 were similar (78% and 88%). The high percent- age of young owls could be caused by young birds hunting closer to human settlement than older birds, but more likely it was caused by a higher total production of young in south-central Scandinavia in 2011 than in 2010. This study shows that photos in the national species archives reveal the age structure of the Great Grey Owl popula- tion, fundamental data to understand the current distribu-tional expansion of this species. This method may also be applied to other species.
Consistency of global carbon budget between concentration-and emission-driven historical experiments simulated by CMIP6 Earth system models and suggestion for improved simulation of CO 2 concentration

Anthropogenically emitted CO 2 from fossil fuel use and land use change is partly absorbed by terrestrial ecosystems and the ocean, while the remainder retained in the atmosphere adds to the ongoing increase in atmospheric CO 2 concentration. Earth system models (ESMs) can simulate such dynamics of the global carbon cycle and consider its 25 interaction with the physical climate system. The ESMs that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6) performed historical simulations to reproduce past climate–carbon cycle dynamics. This study investigated the cause of CO 2 concentration biases in ESMs and identified how they might be reduced. First, we compared simulated historical carbon budgets in two types of experiments: one with prescribed CO 2 emissions (the emission-driven experiment, “E-HIST”) and the other with prescribed CO 2 concentration (the concentration-driven
Séminaire Laurent Schwartz EDP et applications

We review a series of works that address homogenization for partial differential equations with highly oscillatory coefficients. A prototypical setting is that of periodic coefficients that are locally, or more globally perturbed. We investigate the homogenization limits obtained, first for linear elliptic equations, both in conservative and non conservative forms, and next for nonlinear equations such as Hamilton-Jacobi type equations.
ÉQUATIONS AUX DÉRIVÉES PARTIELLES

We review recent work of the authors on the non-relativistic Schrödinger equation with a honeycomb lattice potential, V . In particular, we summarize results on (i) the existence of Dirac points, conical singularities in dispersion surfaces of H V = − ∆+ V and (ii) the two-dimensional Dirac equations, as the large (but ﬁnite) time eﬀective system of equations governing the evolution e − iH V t ψ 0 , for data ψ 0 , which is spectrally localized near a Dirac point. We conclude with a formal derivation and discussion of the eﬀective large time evolution for the nonlinear Schrödinger - Gross Pitaevskii equation for small amplitude initial conditions, ψ 0 . The eﬀective dynamics are governed by a nonlinear Dirac system