Miracle-dz/UniFAR-FTUs huggingface dataset:
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields

We present Pose-NDF, a continuous model for plausible human poses based on neural distance fields (NDFs). Pose or motion priors are important for generating realistic new poses and for reconstructing accurate poses from noisy or partial observations. Pose-NDF learns a manifold of plausible poses as the zero level set of a neural implicit function, extending the idea of modeling implicit surfaces in 3D to the high-dimensional domain SO(3)^K, where a human pose is defined by a single data point, represented by K quaternions. The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres. In contrast to previous VAE-based human pose priors, which transform the pose space into a Gaussian distribution, we model the actual pose manifold, preserving the distances between poses. We demonstrate that PoseNDF outperforms existing state-of-the-art methods as a prior in various downstream tasks, ranging from denoising real-world human mocap data, pose recovery from occluded data to 3D pose reconstruction from images. Furthermore, we show that it can be used to generate more diverse poses by random sampling and projection than VAE-based methods.
Explicit congruences for the class equations $H_{-7p}(X)$ and $H_{-28p}(X)$

In this paper I will give a computational proof of the following theorem, which gives the factorization of K7p(x) modulo p for primes p > 1217. This is the analogue of theorems in [7, Thms. 1.1, 1.2] and [8, Thm. 2.1] for the primes l = 2, 3 and 5 concerning the factorization of the analogous polynomials Klp(x) (mod p). This theorem is needed for the proofs of Conjectures 2 and 3 in [9], in the same way that the results of [7] were needed for [5] and [6].
Dynamical chiral magnetic current and instability in Weyl semimetals

Weyl semimetals realize massless relativistic fermions with two Weyl nodes separated in energy and momentum space, whose low-energy physics is described by Dirac fermions with an axial gauge constant. Here, we study their electromagnetic linear responses based on the effective field theory and on the chiral kinetic theory. Although the static chiral magnetic effect is canceled by the Chern-Simons current under the Pauli-Villars regularization, a dynamical magnetic field is found capable of driving an electric current along its direction, with the total transported charge being independent of temperature and chemical potential for a uniform field. We also incorporate dissipation in the relaxation-time approximation and study collective excitations coupled with Maxwell electromagnetic fields when Weyl node populations deviate from equilibrium. Their dispersion relations at low frequency and long wavelength are determined only by electric, chiral magnetic, and anomalous Hall conductivities, which predict unstable modes leading to anisotropic generation of electromagnetic waves oriented to the direction of Weyl node separation.
White Dwarf Binaries across the H-R Diagram

We created the APOGEE-GALEX-Gaia catalog to study white dwarf (WD) binaries. This database aims to create a minimally biased sample of WD binary systems identified from a combination of GALEX, Gaia, and APOGEE data to increase the number of WD binaries with orbital parameters and chemical compositions. We identify 3414 sources as WD binary candidates, with nondegenerate companions of spectral types between F and M, including main-sequence stars, main-sequence binaries, subgiants, sub-subgiants, red giants, and red clump stars. Among our findings are (a) a total of 1806 systems having inferred WD radii R < 25 R ⊕, which constitute a more reliable group of WD binary candidates within the main sample; (b) a difference in the metallicity distribution function between WD binary candidates and the control sample of most luminous giants (M H < −3.0); (c) the existence of a population of sub-subgiants with WD companions; (d) evidence for shorter periods in binaries that contain WDs compared to those that do not, as shown by the cumulative distributions of APOGEE radial velocity shifts; (e) evidence for systemic orbital evolution in a sample of 252 WD binaries with orbital periods, based on differences in the period distribution between systems with red clump, main-sequence binary, and sub-subgiant companions and systems with main-sequence or red giant companions; and (f) evidence for chemical enrichment during common envelope (CE) evolution, shown by lower metallicities in wide WD binary candidates (P > 100 days) compared to post-CE (P < 100 days) WD binary candidates.
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables

In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, in practice human decision-makers often lack understanding of what information an AI model has access to, in relation to themselves. There are few available guidelines regarding how to effectively communicate aboutunobservables: features that may influence the outcome, but which are unavailable to the model. In this work, we conducted an online experiment to understand whether and how explicitly communicating potentially relevant unobservables influences how people integrate model outputs and unobservables when making predictions. Our findings indicate that presenting prompts about unobservables can change how humans integrate model outputs and unobservables, but do not necessarily lead to improved performance. Furthermore, the impacts of these prompts can vary depending on decision-makers' prior domain expertise. We conclude by discussing implications for future research and design of AI-based decision support tools.
Remote Medication Status Prediction for Individuals with Parkinson’s Disease using Time-series Data from Smartphones

Medication for neurological diseases such as the Parkinson’s disease usually happens remotely away from hospitals. Such out-of-lab environments pose challenges in collecting timely and accurate health status data. Individual differences in behavioral signals collected from wearable sensors also lead to difficulties in adopting current general machine learning analysis pipelines. To address these challenges, we present a method for predicting the medication status of Parkinson’s disease patients using the public mPower dataset, which contains 62,182 remote multi-modal test records collected on smartphones from 487 patients. The proposed method shows promising results in predicting three medication statuses objectively: Before Medication (AUC=0.95), After Medication (AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical records with the attention weights learned through a Transformer model. Our method provides an innovative way for personalized remote health sensing in a timely and objective fashion which could benefit a broad range of similar applications.
Certified Everlasting Functional Encryption

Computational security in cryptography has a risk that computational assumptions underlying the security are broken in the future. One solution is to construct information-theoretically-secure protocols, but many cryptographic primitives are known to be impossible (or unlikely) to have information-theoretical security even in the quantum world. A nice compromise (intrinsic to quantum) is certified everlasting security, which roughly means the following. A receiver with possession of quantum encrypted data can issue a certificate that shows that the receiver has deleted the encrypted data. If the certificate is valid, the security is guaranteed even if the receiver becomes computationally unbounded. Although several cryptographic primitives, such as commitments and zero-knowledge, have been made certified everlasting secure, there are many other important primitives that are not known to be certified everlasting secure. In this paper, we introduce certified everlasting FE. In this primitive, the receiver with the ciphertext of a message m and the functional decryption key of a function f can obtain f(m) and nothing else. The security holds even if the adversary becomes computationally unbounded after issuing a valid certificate. We, first, construct certified everlasting FE for P/poly circuits where only a single key query is allowed for the adversary. We, then, extend it to q-bounded one for NC circuits where q-bounded means that q key queries are allowed for the adversary with an a priori bounded polynomial q. For the construction of certified everlasting FE, we introduce and construct certified everlasting versions of secret-key encryption, public-key encryption, receiver non-committing encryption, and a garbling scheme, which are of independent interest.
Sequence-to-sequence pretraining for a less-resourced Slovenian language

Introduction Large pretrained language models have recently conquered the area of natural language processing. As an alternative to predominant masked language modeling introduced in BERT, the T5 model has introduced a more general training objective, namely sequence to sequence transformation, which more naturally fits text generation tasks. The monolingual variants of T5 models have been limited to well-resourced languages, while the massively multilingual T5 model supports 101 languages. Methods We trained two different-sized T5-type sequence-to-sequence models for morphologically rich Slovene language with much fewer resources. We analyzed the behavior of new models on 11 tasks, eight classification ones (named entity recognition, sentiment classification, lemmatization, two question answering tasks, two natural language inference tasks, and a coreference resolution task), and three text generation tasks (text simplification and two summarization tasks on different datasets). We compared the new SloT5 models with the multilingual mT5 model, multilingual mBART-50 model, and with four encoder BERT-like models: multilingual BERT, multilingual XLM-RoBERTa, trilingual Croatian-Slovene-English BERT, and monolingual Slovene RoBERTa model. Results Concerning the classification tasks, the SloT5 models mostly lag behind the monolingual Slovene SloBERTa model. However, these models are helpful for generative tasks and provide several useful results. In general, the size of models matters, and currently, there is not enough training data for Slovene for successful pretraining of large models. Discussion While the results are obtained on Slovene, we believe that they may generalize to other less-resourced languages, where such models will be built. We make the training and evaluation code, as well as the trained models, publicly available.
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion

The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection. However, DETR suffers from slow training convergence, which hinders its applicability to various detection tasks. We observe that DETR's slow convergence is largely attributed to the difficulty in matching object queries to relevant regions due to the unaligned semantics between object queries and encoded image features. With this observation, we design Semantic-Aligned-Matching DETR++ (SAM-DETR++) to accelerate DETR's convergence and improve detection performance. The core of SAM-DETR++ is a plug-and-play module that projects object queries and encoded image features into the same feature embedding space, where each object query can be easily matched to relevant regions with similar semantics. Besides, SAM-DETR++ searches for multiple representative keypoints and exploits their features for semantic-aligned matching with enhanced representation capacity. Furthermore, SAM-DETR++ can effectively fuse multi-scale features in a coarse-to-fine manner on the basis of the designed semantic-aligned matching. Extensive experiments show that the proposed SAM-DETR++ achieves superior convergence speed and competitive detection accuracy. Additionally, as a plug-and-play method, SAM-DETR++ can complement existing DETR convergence solutions with even better performance, achieving 44.8% AP with merely 12 training epochs and 49.1% AP with 50 training epochs on COCO val2017 with ResNet-50. Codes are available at https://github.com/ZhangGongjie/SAM-DETR .
Weak cosmic censorship conjecture in Myers-Perry black hole with separability

We investigate the weak cosmic censorship conjecture in Myers-Perry black holes with arbitrary rotations in general dimensions based on the scattering of a massless scalar field. From the fluxes of the scalar field flowing into the black hole, the changes in mass and angular momenta of the black hole are obtained. However, the extremal and near-extremal black holes with the aforementioned changes are still black holes in the final state. Hence, the conjecture is valid for our investigation. Furthermore, we analyze the changes in the black hole from a thermodynamic perspective to highlight that the laws of thermodynamics support the conjecture.
Role of Ambient Pressure on Colliding Jets

In this letter, the merging-vs-bouncing response of obliquely-oriented colliding jets under elevated and reduced gaseous environment pressures was experimentally examined. Experiments with water and n-tetradecane confirmed that the collision outcome transitions from merging to bouncing, and then to merging again, when the impact velocity was increased. This behavior which was previously reported for atmospheric pressure, has now also been observed at elevated and reduced pressures. New results also show that there exists a critical pressure (0.9 bar for tetradecane and 5 bar for water) below which increasing pressure promotes bouncing (expands the bouncing regime), while beyond this, merging is promoted (reduces the bouncing regime) instead. This leads to a non-monotonic influence of pressure on the non-coalescence outcomes of collisional jets, which was not previously reported. The study provides evidence of new behaviors in colliding jets at reduced and elevated pressures, which differs from well-studied droplet-droplet collisions.
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning

Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present iVoro, a holistic framework for CIL, derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner -- the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Further, in order to find a better set of centers for VD construction, we colligate DNN with VD using Power Diagram and show that the VD structure can be optimized by integrating local DNN models using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multi-centered VD (CIVD) that efficiently captures multi-grained features from DNN. Importantly, iVoro is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ~0.9). Putting everything together, iVoro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, iVoro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. Our code is available at https://machunwei.github. io/ivoro.
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-resolution segmentation results for analysis, which leads to super-computational load by most existing methods. If based on low-resolution input, they easily ignore tiny vessels or cause discontinuity of segmented vessels. To solve these problems, the paper proposes an algorithm named SuperVessel, which gives out high-resolution and accurate vessel segmentation using low-resolution images as input. We first take super-resolution as our auxiliary branch to provide potential high-resolution detail features, which can be deleted in the test phase. Secondly, we propose two modules to enhance the features of the interested segmentation region, including an upsampling with feature decomposition (UFD) module and a feature interaction module (FIM) with a constraining loss to focus on the interested features. Extensive experiments on three publicly available datasets demonstrate that our proposed SuperVessel can segment more tiny vessels with higher segmentation accuracy IoU over 6%, compared with other state-of-the-art algorithms. Besides, the stability of SuperVessel is also stronger than other algorithms. We will release the code after the paper is published.
Directional Josephson traveling-wave parametric amplifier via non-Hermitian topology

Low-noise microwave amplifiers are crucial for detecting weak signals in quantum technologies and radio astronomy. Building an ideal device is challenging as it must amplify a broad range of frequencies while adding minimal noise. The amplification must also be directional so that it favors the observer's direction while protecting the source from its environment. Here, we introduce a fundamentally new type of superconducting Josephson traveling-wave parametric amplifier that fulfills all these requirements by exploiting non-Hermitian topological effects and time-reversal symmetry breaking via the phase of the four-wave-mixing pump. In the topological amplification phase, the device acquires an unprecedented performance such as a gain growing exponentially with system size and largely surpassing 20 dB, exponential suppression of back-wards noise below -30 dB, a bandwidth of GHz, and topological protection against disorder. This opens the door for integrating near-ideal and compact pre-amplifiers on the same chip as quantum processors.
Probing the Earliest Phases in the Formation of Massive Galaxies with Simulated HST+JWST Imaging Data from Illustris

We use the Illustris-1 simulation to explore the capabilities of the Hubble Space Telescope (HST) and James Webb Space Telescope (JWST) data to analyze the stellar populations in high-redshift galaxies, taking advantage of the combined depth, spatial resolution, and wavelength coverage. For that purpose, we use simulated broadband ACS, WFC3, and NIRCam data and two-dimensional stellar population synthesis (2D-SPS) to derive the integrated star formation history (SFH) of massive (M * > 1010 M ⊙) simulated galaxies at 1 < z < 4 that evolve into a local M * > 1011 M ⊙ galaxy. In particular, we explore the potential of HST and JWST data sets reaching a depth similar to those of the CANDELS and ongoing CEERS observations, respectively, and concentrate on determining the capabilities of this data set for characterizing the first episodes in the SFH of local M * > 1011 M ⊙ galaxies by studying their progenitors at z > 1. The 2D-SPS method presented in this paper has been calibrated to robustly recover the cosmic times when the first star formation episodes occurred in massive galaxies, i.e., the first stages in their integrated SFHs. In particular, we discuss the times when the first 1%–50% of their total stellar mass formed in the simulation. We demonstrate that we can recover these ages with typical median systematic offset of less than 5% and scatter around 20%–30%. According to our measurements on Illustris data, we are able to recover that local M * > 1011 M ⊙ galaxies would have started their formation by z = 16, forming the first 5% of their stellar mass present at z ∼ 1 by z = 4.5, 10% by z = 3.7, and 25% by z = 2.7.
Contact interaction analysis of octet baryon axial-vector and pseudoscalar form factors

Octet baryon axial, induced pseudoscalar, and pseudoscalar form factors are computed using a symmetry-preserving treatment of a vector$\,\times\,$vector contact interaction (SCI), thereby unifying them with an array of other baryon properties and analogous treatments of semileptonic decays of pseudoscalar mesons. The baryons are treated as quark--plus--interacting-diquark bound states, whose structure is obtained by solving a Poincar\'e-covariant Faddeev equation. The approach is marked by algebraic simplicity, involves no free parameters, and since it is symmetry preserving, all consequences of partial conservation of the axial current are manifest. It is found that SCI results are consistent with only small violations of SU$(3)$-flavour symmetry, an outcome which may be understood as a dynamical consequence of emergent hadron mass. The spin-flavour structure of the Poincar\'e-covariant baryon wave functions is expressed in the presence of both flavour-antitriplet scalar diquarks and flavour-sextet axialvector diquarks and plays a key role in determining all form factors. Considering neutral axial currents, SCI predictions for the flavour separation of octet baryon axial charges and, therefrom, values for the associated SU$(3)$ singlet, triplet, and octet axial charges are obtained. The results indicate that at the hadron scale, $\zeta_{\cal H}$, valence degrees-of-freedom carry roughly 50% of an octet baryon's total spin. Since there are no other degrees-of-freedom at $\zeta_{\cal H}$, the remainder may be associated with quark+diquark orbital angular momentum.
Exploration of the origin of the 2020 X-ray outburst in OJ 287

Research into OJ 287 has been ongoing for many years. In 2020 April–June, this source underwent the second highest X-ray outburst (second only to the 2016–2017 outburst) and the mechanism of this outburst is still under debate. In this paper, we discuss two scenarios to explore the origin of the outburst: an after-effect of a black hole–disc impact and a tidal disruption event (TDE). We present the weak correlations of the spectral index versus X-ray flux and the hardness ratio (HR) versus the soft X-ray flux during the outburst, and these features are different from the case in the quiescent state. The correlations are compared with those of the 2016–2017 outburst with the highest X-ray flux in monitoring history. Analysis of the outbursts in 2016–2017 and 2020 shows that the expected time of the X-ray outburst, based on the theory of the after-effect of the black hole–disc impact and the estimation of available data, is inconsistent with historical observations. The soft X-ray spectra, the barely temporal evolution of colour, and the evolution of the HR mean that the 2020 outburst shares similar features with the 2016–2017 outburst, which was considered as a possible candidate for a TDE. Additionally, we find that the predictions of full TDEs (t−5/3) and partial TDEs (t−9/4) for the soft X-ray decay light curve are well fitted. Our analysis suggests that the 2020 outburst in OJ 287 is probably related to the TDE candidate.