Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspherical theory of the quantum Hall effect: The role of exceptional degeneracy

By separating the Schr\"odinger equation for $N$ noninteracting spin-polarized fermions in two-dimensional hyperspherical coordinates, we demonstrate that fractional quantum Hall (FQH) states emerge naturally from degeneracy patterns of the antisymmetric free-particle eigenfunctions. In the presence of Coulomb interactions, the FQH states split off from a degenerate manifold and become observable as distinct quantized energy eigenstates with an energy gap. This alternative classification scheme is based on an approximate separability of the interacting $N$-fermion Schr\"odinger equation in the hyperradial coordinate, which sheds light on the emergence of Laughlin states as well as other FQH states. An approximate good collective quantum number, the grand angular momentum $K$ from $K$-harmonic few-body theory, is shown to correlate with known FQH states at many filling factors observed experimentally.
Prompt-Based Metric Learning for Few-Shot NER

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. Our code is available at https://github.com/AChen-qaq/ProML.
Unsupervised multi-branch capsule for hyperspectral and LiDAR classification

With the convenient availability of remote sensing data, how to make models to interpret complex remote sensing data attracts wide attention. In remote sensing data, hyperspectral images contain spectral information and LiDAR contains elevation information. Hence, more explorations are warranted to better fuse the features of different source data. In this paper, we introduce semantic understanding to dynamically fuse data from two different sources, extract features of HSI and LiDAR through different capsule network branches and improve self-supervised loss and random rigid rotation in canonical capsule to a high-dimensional situation. Canonical capsule computes the capsule decomposition of objects by permutation-equivariant attention and the process is self-supervised by training pairs of randomly rotated objects. After fusing the features of HSI and LiDAR with semantic understanding, the unsupervised extraction of spectral-spatial-elevation fusion features is achieved. With two real-world examples of HSI and LiDAR fused, the experimental results show that the proposed multi-branch high-dimensional canonical capsule algorithm can be effective for semantic understanding of HSI and LiDAR. It indicates that the model can extract HSI and LiDAR data features effectively as opposed to existing models for unsupervised extraction of multi-source RS data.
Malicious Selling Strategies in E-Commerce Livestream: A Case Study of Alibaba's Taobao and ByteDance's TikTok

Due to the limitations imposed by the COVID-19 pandemic, many users have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, many streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao and TikTok (or “Douyin” in Chinese). We identified 16 malicious selling strategies and found that platform designs enhanced these malicious selling strategies. Second, through an interview study with 13 viewers, we provide a rich description of viewers’ awareness of malicious selling strategies and the challenges they encountered while trying to overcome malicious selling. We conclude by discussing the policy and design implications of countering malicious selling.
Role of parasitic interactions and microwave crosstalk in dispersive control of two superconducting artificial atoms

In this work we study the role of parasitic interactions and microwave crosstalk in a system of two superconducting artificial atoms interacting via a single-mode coplanar waveguide. Through a general description of the effective dynamics of the atoms, beyond the two-level approximation, we show that the atom selectivity (ability to individually address an atom) is only dependent on the resultant phasor associated to the drives used to control the system. We then exploit the benefits of such a drive-dependent selectivity to describe how the coherent population inversion occurs in the atoms simultaneously, with no interference of residual atom-atom interaction. In this scenario the parasitic interaction works as a resource to fast and high fidelity control, as it gives rise to a new regime of frequencies for the atoms able to suppress effective atom-atom coupling (idling point). To end, we show how an entangling $i$SWAP gate is implemented with fidelity higher than $99\%$, even in presence of parasitic interactions. More than that, we argue that the existence of this interaction can be helpful to speed up the gate performance. Our results open prospects to a new outlook on the real role of such ``undesired"effects in a system of superconducting artificial atoms.
Ab initio study of proton-exchanged LiNbO3(I): Structural, thermodynamic, dielectric, and optical properties

Using first principles calculations, we study the ground-state structure of bulk proton-exchanged lithium niobate, which is also called hydrogen niobate and is widely used in waveguides. Thermodynamics helps to establish the most favorable nonpolar surface as well as the water-deficient and water-rich phases under different ambient conditions, which we refer to as"dehydrated"and"rehydrated"phases, respectively. We compute the low-frequency dielectric response and the optical refractive indices of hydrogen niobate in different phases. The dielectric constant is greatly enhanced compared to lithium niobate. At shorter wavelengths, the refractive indices vary between each phase and have a sharp contrast to lithium niobate. Our study characterizes the structures and thermal instabilities of this compound and reveals its excellent dielectric and optical properties, which can be important in the future application in waveguides.
EVIDENCE FOR THE RAPID FORMATION OF LOW-MASS EARLY-TYPE GALAXIES IN DENSE ENVIRONMENTS

We explore the environmental dependence of star formation timescales in low-mass galaxies using the [α/Fe] abundance ratio as an evolutionary clock. We present integrated [α/Fe] measurements for 11 low-mass ( M ⋆ ∼ 10 9 M ⊙ ? > ) early-type galaxies (ETGs) with a large range of cluster-centric distance in the Virgo Cluster. We find a gradient in [α/Fe], where the galaxies closest to the cluster center (the cD galaxy, M87) have the highest values. This trend is driven by galaxies within a projected radius of 0.4 Mpc (0.26 times the virial radius of Virgo A), all of which have super-solar [α/Fe]. Galaxies in this mass range exhibit a large scatter in the [α/Fe]–σ diagram, and do not obviously lie on an extension of the relation defined by massive ETGs. In addition, we find a correlation between [α/Fe] and globular cluster specific frequency (SN), suggesting that low-mass ETGs that formed their stars over a short period of time were also efficient at forming massive star clusters. The innermost low-mass ETGs in our sample have [α/Fe] values comparable to that of M87, implying that environment is the controlling factor for star formation timescales in dense regions. These low-mass galaxies could be the surviving counterparts of the objects that have already been accreted into the halo of M87, and may be the link between present-day low-mass galaxies and the old, metal-poor, high-[α/Fe], high-SN stellar populations seen in the outer halos of massive ETGs.
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic

The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants. It remains an open problem on whether the limited dialectical data can be used to improve the models trained in Arabic on its dialectal variants. First, we show that multilingual-BERT (mBERT) incrementally pretrained on Arabic monolingual data takes less training time and yields comparable accuracy when compared to our custom monolingual Arabic model and beat existing models (by an avg metric of +$6.41$). We then explore two continual pre-training methods-- (1) using small amounts of dialectical data for continual finetuning and (2) parallel Arabic to English data and a Translation Language Modeling loss function. We show that both approaches help improve performance on dialectal classification tasks ($+4.64$ avg. gain) when used on monolingual models.
Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

Function signature recovery is important for many binary analysis tasks such as control-flow integrity enforcement, clone detection, and bug finding. Existing works try to substitute learning-based methods with rule-based methods to reduce human effort.They made considerable efforts to enhance the system’s performance, which also bring the side effect of higher resource consumption. However, recovering the function signature is more about providing information for subsequent tasks, and both efficiency and performance are significant.In this paper, we first propose a method called Nimbus for efficient function signature recovery that furthest reduces the whole-process resource consumption without performance loss. Thanks to information bias and task relation (i.e., the relation between parameter count and parameter type recovery), we utilize selective inputs and introduce multi-task learning (MTL) structure for function signature recovery to reduce computational resource consumption, and fully leverage mutual information. Our experimental results show that, with only about the one-eighth processing time of the state-of-the-art method, we even achieve about 1% more prediction accuracy over all function signature recovery tasks.
HOT ELECTROMAGNETIC OUTFLOWS. III. DISPLACED FIREBALL IN A STRONG MAGNETIC FIELD

The evolution of a dilute electron–positron fireball is calculated in the regime of strong magnetization and high compactness (ℓ ∼ 103–108). Heating is applied at a low effective temperature (<25 keV), appropriate to breakout from a confining medium, so that relaxation to a blackbody is inhibited by pair annihilation. The diffusion equation for Compton scattering by thermal pairs is coupled to a trans-relativistic cyclo-synchrotron source. We find that the photon spectrum develops a quasi-thermal peak at energy ∼0.1 mec2 in the comoving frame, with a power-law slope below it that is characteristic of gamma-ray bursts (GRBs; Fω ∼ const). The formation of a thermal high-energy spectrum is checked using the full kinetic equations. Calculations for a baryon-dominated photosphere reveal a lower spectral peak energy, and a harder low-energy spectrum, unless ion rest mass carries ≲ 10−5 of the energy flux. We infer that (1) the GRB spectrum is inconsistent with the neutron-rich wind emitted by a young magnetar or neutron torus, and points to an event horizon in the engine; (2) neutrons play a negligible role in prompt gamma-ray emission; (3) the relation between observed peak frequency and burst energy is bounded below by the observed Amati relation if the Lorentz factor ∼(opening angle)−1 at breakout, and the jet is surrounded by a broader sheath that interacts with a collapsing stellar core; (4) X-ray flashes are consistent with magnetized jets with ion-dominated photospheres; (5) high-frequency Alfvén waves may become charge starved in the dilute pair gas; (6) limitations on magnetic reconnection from plasma collisionality have been overestimated.
Swift follow-up observations of 13 INTEGRAL sources

The various IBIS/ISGRI catalogues contain a large population of hard X-ray sources whose nature is still unknown. Even if the> 20 keV positional uncertainty provided by ISGRI is unprecedented, it is still too large to pinpoint the counterpart at othe r wavelengths, which is the only secure way of obtaining a source identificat ion. We continue the work of trying to reveal the nature of these hard X-ray sources, starting with analysis of X-ray data collect ed via focusing X-ray telescopes, in order to obtain arcsec accurate X-ray positions. We can then identify counterparts at infrared and optical wavelengths and try to unveil the nature of the sources. We analysed data from observations of 13 INTEGRALsources made with the Swift satellite. The X-ray images obtained by the X-Ray Telescope instrument allowed us to find possible counterparts to the IG R sources with a positional accuracy of a few arcsec. We then browsed the online catalogues (e.g., NED, SIMBAD, 2MASS, 2MASX, USNO B1.0) to search for counterparts at other wavelengths. We also made use of the X-ray spectral parameters in trying to identify the nature of those objects. For the 13 objects, we found possible counterparts at X-ray energies and identified the IR /optical and/or UV counterparts as seen with Swift/UVOT. We also discuss the likelihood of association of the X-ray and INTEGRAL source in each case. We confirm the previously proposed class ification of IGR J02524−0829 (Sey 2 AGN), J08023−6954 (RS CVn star), and J11457−1827 (Sey 1 AGN). For 7 of these sources we give the first identification of their nature: IGR J02086 −1742, J12060+3818, J12070+2535, J13042−1020, and J13412+3022 are AGN, and J14488−5942 is a probable X-ray binary. For J03184−0014, although we question the association of the IGR and Swift sources, we classify the latter as an AGN. We suggest that IGR J15283−4443 is a Galactic source, but we cannot classify the source further. Finally, we question the association of IGR J11457−1827 and J23130+8608 with the X-ray sources we found, and go on to question the genuineness of the former IGR source.
Infant Hip Screening Using Multi-Class Ultrasound Scan Segmentation

Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.
eROSITA on SRG

eROSITA (extended ROentgen Survey with an Imaging Telescope Array) is the core instrument on the Russian Spektrum-Roentgen-Gamma (SRG) mission which is scheduled for launch in late 2012. eROSITA is fully approved and funded by the German Space Agency DLR and the Max-Planck-Society. The instrument development is in phase C/D since fall 2009. The design driving science is the detection 100.000 Clusters of Galaxies up to redshift z ~1.3 in order to study the large scale structure in the Universe and test cosmological models, especially Dark Energy. This will be accomplished by an all-sky survey lasting for four years plus a phase of pointed observations. eROSITA consists of seven Wolter-I telescope modules, each equipped with 54 Wolter-I shells having an outer diameter of 360 mm. This would provide an effective area of ~1500 cm2 at 1.5 keV and an on axis PSF HEW of 15 arcsec resulting in an effective angular resolution of 28 - 30 arcsec, averaged over the field of view. In the focus of each mirror module, a fast frame-store pn-CCD provides a field of view of 1° in diameter.
An Energy-Based Lengthscale for Reduced Order Models of Turbulent Flows

In this paper, we propose a novel reduced order model (ROM) lengthscale that is constructed by using energy distribution arguments. The new energy-based ROM lengthscale is fundamentally different from the current ROM lengthscales, which are built by using dimensional arguments. To assess the novel, energy-based ROM lengthscale, we compare it with a standard, dimensionality-based ROM lengthscale in two fundamentally different types of models: (i) the mixing-length ROM (ML-ROM), which is a ROM closure model; and (ii) the evolve-filter-relax ROM (EFR-ROM), which is a regularized ROM. We test the four combinations (i.e., ML-ROM and EFR-ROM equipped with the energy-based and dimensionality-based lengthscales) in the numerical simulation of the turbulent channel flow at $Re_{\tau} = 395$. The numerical investigation yields the following conclusions: (i) The new energy-based ROM lengthscale is significantly (almost two orders of magnitude) larger than the standard dimensionality-based ROM lengthscale. As a result, the energy-based lengthscale yields more stable ML-ROMs and EFR-ROMs than the dimensionality-based lengthscale. (ii) The energy-based lengthscale displays the correct asymptotic behavior with respect to the ROM dimension, whereas the dimensionality-based lengthscale does not. (iii) The energy-based lengthscale yields ML-ROMs and (when significant filtering is effected) EFR-ROMs whose parameters are less sensitive (i.e., more robust) than the parameters of the ML-ROMs and EFR-ROMs based on the dimensionality-based lengthscale. The novel energy-based lengthscale could enable the development of better scale-aware ROM strategies for flow-specific applications and is expected to have long term applications in nuclear reactor thermal-hydraulics.
User Engagement and the Toxicity of Tweets

Twitter is one of the most popular online micro-blogging and social networking platforms. This platform allows individuals to freely express opinions and interact with others regardless of geographic barriers. However, with the good that online platforms offer, also comes the bad. Twitter and other social networking platforms have created new spaces for incivility. With the growing interest on the consequences of uncivil behavior online, understanding how a toxic comment impacts online interactions is imperative. We analyze a random sample of more than 85,300 Twitter conversations to examine differences between toxic and non-toxic conversations and the relationship between toxicity and user engagement. We find that toxic conversations, those with at least one toxic tweet, are longer but have fewer individual users contributing to the dialogue compared to the non-toxic conversations. However, within toxic conversations, toxicity is positively associated with more individual Twitter users participating in conversations. This suggests that overall, more visible conversations are more likely to include toxic replies. Additionally, we examine the sequencing of toxic tweets and its impact on conversations. Toxic tweets often occur as the main tweet or as the first reply, and lead to greater overall conversation toxicity. We also find a relationship between the toxicity of the first reply to a toxic tweet and the toxicity of the conversation, such that whether the first reply is toxic or non-toxic sets the stage for the overall toxicity of the conversation, following the idea that hate can beget hate.
Prophet Inequality: Order selection beats random order

In the prophet inequality problem, a gambler faces a sequence of items arriving online with values drawn independently from known distributions. On seeing an item, the gambler must choose whether to accept its value as her reward and quit the game, or reject it and continue. The gambler's aim is to maximize her expected reward relative to the expected maximum of the values of all items. Since the seventies, a tight bound of 1/2 has been known for this competitive ratio in the setting where the items arrive in an adversarial order [Krengel and Sucheston, 1977, 1978]. However, the optimum ratio still remains unknown in the order selection setting, where the gambler selects the arrival order, as well as in prophet secretary, where the items arrive in a random order. Moreover, it is not even known whether a separation exists between the two settings. In this paper, we show that the power of order selection allows the gambler to guarantee a strictly better competitive ratio than if the items arrive randomly. For the order selection setting, we identify an instance for which Peng and Tang's [Peng and Tang, 2022] state-of-the-art algorithm performs no better than their claimed competitive ratio of (approximately) 0.7251, thus illustrating the need for an improved approach. We therefore extend their design and provide a more general algorithm design framework, using which we show that their ratio can be beaten, by designing a 0.7258-competitive algorithm. For the random order setting, we improve upon Correa, Saona and Ziliotto's [Correa et al., 2019b] [EQUATION]-hardness result to show a hardness of 0.7254 for general algorithms - even in the setting where the gambler knows the arrival order beforehand, thus establishing a separation between the order selection and random order settings.
Time-Varying Correlation Networks for Interpretable Change Point Detection

Change point detection (CPD) methods aim to detect abrupt changes in time-series data. Recent CPD methods have demonstrated their potential in identifying changes in underlying statistical distributions but often fail to capture complex changes in the correlation structure in time-series data. These methods also fail to generalize effectively, as even within the same time-series, different kinds of change points (CPs) may arise that are best characterized by different types of time-series perturbations. To address this issue, we propose TiVaCPD, a CPD methodology that uses a time-varying graphical lasso based method to identify changes in correlation patterns between features over time, and combines that with an aggregate Kernel Maximum Mean Discrepancy (MMD) test to identify subtle changes in the underlying statistical distributions of dynamically established time windows. We evaluate the performance of TiVaCPD in identifying and characterizing various types of CPs in time-series and show that our method outperforms current state-of-the-art CPD methods for all categories of CPs.
The higher spin/vector model duality

This paper is mainly a review of the dualities between Vasiliev’s higher spin gauge theories in AdS4 and three dimensional large N vector models, with focus on the holographic calculation of correlation functions of higher spin currents. We also present some new results in the computation of parity odd structures in the three point functions in parity violating Vasiliev theories. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Higher spin theories and holography’.