Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher-order topological insulator in a modified Haldane-Hubbard model

We investigate the ground-state phase diagram of a modified spinless Haldane-Hubbard model with broken threefold rotational symmetry, employing exact diagonalization calculations. The interplay of asymmetry, interactions, and topology gives rise to a rich phase diagram. The non-interacting limit of the Hamiltonian exhibits a higher-order topological insulator characterized by the existence of corner modes, in contrast to known chiral edge metallic states of the standard Haldane model. Our investigation demonstrates that these symmetry-protected states are robust to the presence of finite interactions. Furthermore, in certain regimes of parameters, we show that a topological Mott insulator exists in this model, where a non-trivial topological bulk coexists with an interaction-driven charge-density-wave, whose emergence is characterized by a $Z_2$-symmetry breaking within the 3$d$-Ising universality class.
On the Role of Constraints and Degrees of Freedom in the Hamiltonian Formalism

Unfortunately, the Hamiltonian mechanics of degenerate Lagrangian systems is usually presented as a mere recipe of Dirac, with no explanation as to how it works. It then comes to discussing conjectures of whether all primary constraints correspond to gauge symmetries, and it goes all the way to absolutely wrong claims such as the statement that electrodynamics or gravity only have two physical components each, with others being spurious. One has to be very careful because non-dynamical, or constrained, does not mean unphysical. In this article, I give a pedagogical introduction to the degenerate Hamiltonian systems, showing both very simple mechanical examples and general arguments about how it works. For the familiar field theory models, I explain why the gauge freedom there “hits twice” in the sense of producing twice as many first-class constraints as gauge symmetries, and why primary, and only primary, constraints should be put into the total Hamiltonian.
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection

Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.
A combinatorial proof of a tantalizing symmetry on Catalan objects

We investigate a tantalizing symmetry on Catalan objects. In terms of Dyck paths, this symmetry is interpreted in the following way: if w n,k,m is the number of Dyck paths of semilength n with k occurrences of UD and m occurrences of UUD , then w 2 k +1 ,k,m = w 2 k +1 ,k,k +1 − m . We give two proofs of this symmetry: an algebraic proof using generating functions, and a combinatorial proof which makes heavy use of the cycle lemma and an alternate interpretation of the numbers w n,k,m using plane trees. In particular, our combinatorial proof expresses the numbers w 2 k +1 ,k,m in terms of Narayana numbers, and we generalize this to a relationship between the numbers w n,k,m and a family of generalized Narayana numbers due to Callan. Some further generalizations and applications of our combinatorial proofs are explored. Finally, we investigate properties of the polynomials W n,k ( t ) = P km =0 w n,k,m t m , including real-rootedness, γ -positivity, and a symmetric decomposition.
CHAIRS: Towards Full-Body Articulated Human-Object Interaction

Fine-grained capturing of 3D Human-Object Interactions (HOIs) boosts human activity understanding and facilitates downstream visual tasks, including action recognition, holistic scene reconstruction, and human motion synthesis. Despite its signiﬁcance, existing works mostly assume that humans interact with rigid objects using only a few body parts, limiting their scope. In this paper, we address the challenging problem of Full-Body Articulated Human-Object Interaction (f-AHOI), wherein the whole human bodies interact with articulated objects, whose parts are connected by movable joints. We present Capturing Human and Articulated-object InteRactionS ( CHAIRS ) , a large-scale motion-captured f-AHOI dataset, consisting of 17.3 hours of versatile interactions between 46 participants and 81 articulated and rigid sittable objects. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process, as well as realistic and physically plausible full-body interactions. We show the value of CHAIRS with object pose estimation. By learning the geometrical relationships in HOI, we devise the very ﬁrst model that leverage human pose estimation to tackle the estimation of articulated object poses and shapes during whole-body interactions. Given an image and an estimated human pose, our model ﬁrst reconstructs the pose and shape of the object, then optimizes the reconstruction according to a learned interaction prior. Under both evaluation settings ( i
Magnetism of the 
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The spin-$1/2$ Heisenberg antiferromagnet on the square-kagome (SK) lattice has attracted growing attention as a model system of highly frustrated quantum magnetism. A further motivation for theoretical studies comes from the recent discovery of SK spin-liquid compounds. The SK antiferromagnet exhibits two non-equivalent nearest-neighbor bonds $J_1$ and $J_2$. One may expect that in SK compounds $J_1$ and $J_2$ are of different strength. We present a numerical study of finite systems by means of the finite-temperature Lanczos method. We discuss the temperature dependence of the specific heat $C(T)$, the entropy $S(T)$, and of the susceptibility $X(T)$ of the $J_1$-$J_2$ SK Heisenberg antiferromagnet varying $J_2/J_1$ in the range $0 \le J_2/J_1 \le 4$. We also discuss the zero-field ground state of the model. We find indications for a magnetically disordered singlet ground state for $0 \le J_2/J_1 \lesssim 1.65$. Beyond $J_2/J_1 \sim 1.65$ the singlet ground state gives way for a ferrimagnetic ground state. In the region $0.77 \lesssim J_2/J_1 \lesssim 1.65$ the low-temperature thermodynamics is dominated by a finite singlet-triplet gap filled with low-lying singlet excitations leading to an exponentially activated low-temperature behavior of $X(T)$. On the other hand, the low-lying singlets yield an extra maximum or a shoulder-like profile below the main maximum in the $C(T)$ curve. For $J_2/J_1 \lesssim 0.7$ the low-temperature thermodynamics is characterized by a large fraction of $N/3$ weakly coupled spins leading to a sizable amount of entropy at very low temperatures. In an applied magnetic field the magnetization process features plateaus and jumps in a wide range of $J_2/J_1$.
Abstract approach to evolution equations of phase-field type and applications

A general system of abstract nonlinear parabolic equations deriving from phase-field models of heat transfer in anisotropic fluids with phase transitions is studied through a backward finite differences approximation scheme and existence and uniqueness theorems are proved. Then, applications are presented to two physical situations: first, a standard heat diffusion problem is briefly discussed mainly as a justification of the choices of the abstract setting. Second, a more interesting transmission problem between two different adjoining fluids is studied under very general compatibility hypotheses and the abstract results are adapted to provide an existence and uniqueness theorem also in this case. Moreover, through convex analysis techniques, it is performed a mathematical study of the subdifferential-type relations linking the phase fields and the temperatures to the internal energies of the fluids. EMAIL:: schimper@dragon.ian. pv.cnr. it KEYWORDS:: Phase-field models, subdifferential operators, abstract evolution equations
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Given the success with in-context learning of large pre-trained language models, we introduce in-context learning distillation to transfer in-context few-shot learning ability from large models to smaller models. We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models. We perform in-context learning distillation under two different few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask In-context Tuning (Multitask-ICT). Multitask-ICT performs better on multitask few-shot learning but also requires more computation than Meta-ICT. Our method shows consistent improvements for both Meta-ICT and Multitask-ICT on two benchmarks: LAMA and CrossFit. Our extensive experiments and analysis reveal that in-context learning objectives and language modeling objectives are complementary under the Multitask-ICT paradigm. In-context learning objectives achieve the best performance when combined with language modeling objectives.
PHANGS–JWST First Results: Mid-infrared Emission Traces Both Gas Column Density and Heating at 100 pc Scales

We compare mid-infrared (mid-IR), extinction-corrected Hα, and CO (2–1) emission at 70–160 pc resolution in the first four PHANGS–JWST targets. We report correlation strengths, intensity ratios, and power-law fits relating emission in JWST’s F770W, F1000W, F1130W, and F2100W bands to CO and Hα. At these scales, CO and Hα each correlate strongly with mid-IR emission, and these correlations are each stronger than the one relating CO to Hα emission. This reflects that mid-IR emission simultaneously acts as a dust column density tracer, leading to a good match with the molecular-gas-tracing CO, and as a heating tracer, leading to a good match with the Hα. By combining mid-IR, CO, and Hα at scales where the overall correlation between cold gas and star formation begins to break down, we are able to separate these two effects. We model the mid-IR above I ν = 0.5 MJy sr−1 at F770W, a cut designed to select regions where the molecular gas dominates the interstellar medium (ISM) mass. This bright emission can be described to first order by a model that combines a CO-tracing component and an Hα-tracing component. The best-fitting models imply that ∼50% of the mid-IR flux arises from molecular gas heated by the diffuse interstellar radiation field, with the remaining ∼50% associated with bright, dusty star-forming regions. We discuss differences between the F770W, F1000W, and F1130W bands and the continuum-dominated F2100W band and suggest next steps for using the mid-IR as an ISM tracer.
Electrons and positrons in dense gases

The appearance of electrons or positrons in dense gaseous media produces a whole series of striking phenomena governed by the properties of the states of the light particles in the dense medium. The present review considers these phenomena systematically. The results of measurements of electron mobility and positron annihilation rates in moderately dense gases are discussed. Effects that arise as the density of the gas increases are considered. Self-trapped states become possible at high densities: these are bound states of the light particles with the medium, "clusters," "bubbles," or mixed-type formations. The fluctuon theory of these states is set forth, along with the results of experiments in which the light particles make transitions to new states. The possible existence of "orientation clusters" in gases consisting of polar molecules is investigated. Certain applications of the results to the theory of discharges in dense gases and liquids and to the theory of the weakly ionized nonideal plasma are discussed.
Anharmonic lattice dynamics via the special displacement method

On the basis of the self-consistent phonon theory and the special displacement method, we develop an approach for the treatment of anharmonicity in solids. We show that this approach enables the efficient calculation of temperature-dependent anharmonic phonon dispersions, requiring very few steps to achieve minimization of the system's free energy. We demonstrate this methodology in the regime of strongly anharmonic materials which exhibit a multi-well potential energy surface, like cubic SrTiO$_3$, CsPbBr$_3$, CsPbI$_3$, CsSnI$_3$, and Zr. Our results are in good agreement with experiments and previous first-principles studies relying on stochastic nonperturbative and molecular dynamics simulations. We achieve a very robust workflow by using harmonic phonons of the polymorphous ground state as the starting point and an iterative mixing scheme of the dynamical matrix. We also suggest that the phonons of the polymorphous ground state might provide an excellent starting approximation to explore anharmonicity. Given the simplicity, efficiency, and stability of the present treatment to anharmonicity, it is especially suitable for use with any electronic structure code and for investigating electron-phonon couplings in strongly anharmonic systems.
Orbital- and Spin-phase Variability in the X-Ray Emission from the Accreting Pulsar Centaurus X-3

We present a time-resolved analysis using 39 ks NuSTAR observation data of the X-ray pulsar Centaurus X-3, covering an orbital-phase interval of Φ = 0.199–0.414. The orbital- and spin-phase variabilities are investigated through time-resolved spectra, light curves, and pulse profiles. The orbital-phase variability was due to the mixture of two comparable effects: intrinsic flux variability of ∼10% and obscuration by the clumpy stellar wind. The typical size and number density of the clumps are ∼9 × 1010 cm and ∼3 × 1012 cm−3, respectively. In the spin-phase-resolved analysis, we detected variations in the spectral features of the continuum, the Fe line, and the cyclotron resonance scattering feature (CRSF). The photon index ranged from 0.72 to 1.06, corresponding to the difference in the Comptonization optical depth by a factor of ∼1.6. The equivalent width and intensity of the Fe line had negative correlations with the continuum flux. The central energy and the strength of the CRSF increased at the pulse maximum. The former ranged from 26.0 to 28.7 keV, while the latter varied by a factor of ∼1.9. The pulse profile was double-peaked in the low-energy band, and it gradually shifted to being single-peaked with energy, indicating the existence of two distinct emission patterns, corresponding to the pencil and fan beams. Finally, we found that the pulse profiles were highly stable along the orbital phase, within a variation degree of ∼20%, which provides evidence of the highly stable accretion stream of the binary system.
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?

Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages. In this work, we investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages. We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms. Such difference learnt via self-supervision plays a crucial role in the zero-shot transfer performance and can be predicted by variation in morphosyntactic properties between languages. These results suggest that mBERT properly encodes languages in a way consistent with linguistic diversity and provide insights into the mechanism of cross-lingual transfer.
Hodge theory of twisted derived categories and the period-index problem

We study the Hodge theory of twisted derived categories and its relation to the period-index problem. Our main contribution is the development of a theory of twisted Mukai structures for topologically trivial Brauer classes on arbitrary smooth proper varieties and in families. As applications, we construct Hodge classes whose algebraicity would imply period-index bounds; construct new counterexamples to the integral Hodge conjecture on Severi-Brauer varieties; and prove the integral Hodge conjecture for derived categories of Deligne-Mumford surfaces.
Characteristic of Organic Thin Film Transistor with a High-k Insulator of Nano-TiO2 and Polyimide Blend

The preparation of nanocomposite as gate dielectric film was carried out by blending polyimide and nano-TiO2 particle to enhance the capacitance of gate dielectric. When the concentration of the nano-TiO2 particle was less than 2 vol % and was well dispersed in polyimide slurry, a nanocomposite film with a homogeneous distribution of nano-TiO2 particles in polyimide and low roughness was obtained after curing at 200 °C, resulting in low leakage current density of the nanocomposite film and high on/off ratio of the organic thin film transistor (OTFT) device. A strong correlation between the morphology and electrical properties of the nanocomposite gate dielectric film is reflected in the OTFT device performance. Because capacitance of the gate dielectric film and proportion of thin film phase of the pentacene both increased when the pentacene was deposited on a nanocomposite gate dielectric film, the mobility and the threshold voltage of OTFT noticeably improved.
Patterns in Spatio-Temporal Extremes

In environmental science applications, extreme events frequently exhibit a complex spatio-temporal structure, which is difficult to describe flexibly and estimate in a computationally efficient way using state-of-art parametric extreme-value models. In this paper, we propose a computationally-cheap non-parametric approach to investigate the probability distribution of temporal clusters of spatial extremes, and study within-cluster patterns with respect to various characteristics. These include risk functionals describing the overall event magnitude, spatial risk measures such as the size of the affected area, and measures representing the location of the extreme event. Under the framework of functional regular variation, we verify the existence of the corresponding limit distributions as the considered events become increasingly extreme. Furthermore, we develop non-parametric estimators for the limiting expressions of interest and show their asymptotic normality under appropriate mixing conditions. Uncertainty is assessed using a multiplier block bootstrap. The finite-sample behavior of our estimators and the bootstrap scheme is demonstrated in a spatio-temporal simulated example. Our methodology is then applied to study the spatio-temporal dependence structure of high-dimensional sea surface temperature data for the southern Red Sea. Our analysis reveals new insights into the temporal persistence, and the complex hydrodynamic patterns of extreme sea temperature events in this region.
Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias

Warning: The content of this paper may be upsetting or triggering. Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a person’s human characteristics, such as emotions, are disregarded and the person is treated as a body or a collection of body parts. We replicate three experiments in the psychology literature quantifying sexual objectification and show that the phenomena persist in trained AI models. A first experiment uses standardized images of women from the Sexual OBjectification and EMotion Database, and finds that human characteristics are disassociated from images of objectified women: the model’s recognition of emotional state is mediated by whether the subject is fully or partially clothed. Embedding association tests (EATs) return significant effect sizes for both anger (d > 0.80) and sadness (d > 0.50), associating images of fully clothed subjects with emotions. GRAD-CAM saliency maps highlight that CLIP gets distracted from emotional expressions in objectified images where subjects are partially clothed. A second experiment measures the effect in a representative application: an automatic image captioner (Antarctic Captions) includes words denoting emotion less than 50% as often for images of partially clothed women than for images of fully clothed women. A third experiment finds that images of female professionals (scientists, doctors, executives) are likely to be associated with sexual descriptions relative to images of male professionals. A fourth experiment shows that a prompt of "a [age] year old girl" generates sexualized images (as determined by an NSFW classifier) up to 73% of the time for VQGAN-CLIP (age 17), and up to 42% of the time for Stable Diffusion (ages 14 and 18); the corresponding rate for boys never surpasses 9%. The evidence indicates that language-vision AI models trained on automatically collected web scrapes learn biases of sexual objectification, which propagate to downstream applications.
Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms

The deployment flexibility and maneuverability of unmanned aerial vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this article introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the state-of-the-art studies demonstrating that our model outperforms the competing models.