Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.
Mid-IR Colloidal Nanocrystals

Colloidal quantum dots presenting optoelectronic properties into the mid-infrared are reviewed with an emphasis on HgTe. Interband transitions with narrow band gap material and intraband transitions with wide band gap semiconductors can both address the infrared range of wavelengths. Semimetals are particularly promising since, by controlling the particle size, a gap can in principle be opened and tuned through the full infrared spectrum. HgTe has been quite successful recently, and colloidal synthesis has allowed the tuning of the absorption edge from 1 and 5 μm at room temperature. The cutoff wavelength and absorption coefficient of these materials have been discussed. Electrochemistry experiments have demonstrated that both n and p nanoparticles are stable. Films of the HgTe colloidal quantum dots have shown room temperature photoconduction when excited in the mid-IR. With novel processing leading to improved detectivity, these materials are becoming of significant interest as an alternative to the muc.
Slowly evolving dopaminergic activity modulates the moment-to-moment probability of movement initiation

Clues from human movement disorders have long suggested that the neurotransmitter dopamine plays a key role in motor control, but how the endogenous dopaminergic system regulates movement is unknown. Here we show dynamic dopaminergic signaling over seconds-long timescales controls movement timing in mice. Animals were trained to initiate licking after a self-timed interval following a start-timing cue. The movement time was variable from trial-to-trial, as expected from previous studies. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement time on single trials. Steeply rising signals preceded early lick-initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movements. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of movement initiation, whereas inhibition caused late-shifting, as if modulating the probability of movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation on single trials. These results reveal a causal role for dynamic dopaminergic signaling unfolding over seconds in modulating the decision of when to move.
Naa12 compensates for Naa10 in mice in the amino-terminal acetylation pathway

Amino-terminal acetylation is catalyzed by a set of N-terminal acetyltransferases (NATs). The NatA complex (including X-linked Naa10 and Naa15) is the major acetyltransferase, with 40-50% of all mammalian proteins being potential substrates. However, the overall role of amino-terminal acetylation on a whole-organism level is poorly understood, particularly in mammals. Male mice lacking Naa10 show no globally apparent in vivo amino-terminal acetylation impairment and do not exhibit complete embryonic lethality. Rather Naa10 nulls display increased neonatal lethality, and the majority of surviving undersized mutants exhibit a combination of hydrocephaly, cardiac defects, homeotic anterior transformation, piebaldism and urogenital anomalies. Naa12 is a previously unannotated Naa10-like paralogue with NAT activity that genetically compensates for Naa10. Mice deficient for Naa12 have no apparent phenotype, whereas mice deficient for Naa10 and Naa12 display embryonic lethality. The discovery of Naa12 adds to the currently known machinery involved in amino-terminal acetylation in mice.
Learning to Efficiently Sample from Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a powerful family of generative models that can yield high-fidelity samples and competitive log-likelihoods across a range of domains, including image and speech synthesis. Key advantages of DDPMs include ease of training, in contrast to generative adversarial networks, and speed of generation, in contrast to autoregressive models. However, DDPMs typically require hundreds-to-thousands of steps to generate a high fidelity sample, making them prohibitively expensive for high dimensional problems. Fortunately, DDPMs allow trading generation speed for sample quality through adjusting the number of refinement steps as a post process. Prior work has been successful in improving generation speed through handcrafting the time schedule by trial and error. We instead view the selection of the inference time schedules as an optimization problem, and introduce an exact dynamic programming algorithm that finds the optimal discrete time schedules for any pre-trained DDPM. Our method exploits the fact that ELBO can be decomposed into separate KL terms, and given any computation budget, discovers the time schedule that maximizes the training ELBO exactly. Our method is efficient, has no hyper-parameters of its own, and can be applied to any pre-trained DDPM with no retraining. We discover inference time schedules requiring as few as 32 refinement steps, while sacrificing less than 0.1 bits per dimension compared to the default 4,000 steps used on ImageNet 64x64 [Ho et al., 2020; Nichol and Dhariwal, 2021].
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Rendering

We introduce DoubleField, a novel representation combining the merits of both surface ﬁeld and radiance ﬁeld for high-ﬁdelity human rendering. Within Double-Field, the surface ﬁeld and radiance ﬁeld are associated together by a shared feature embedding and a surface-guided sampling strategy. In this way, DoubleField has a continuous but disentangled learning space for geometry and appearance modeling, which supports fast training, inference, and ﬁnetuning. To achieve high-ﬁdelity free-viewpoint rendering, DoubleField is further augmented to leverage ultra-high-resolution inputs, where a view-to-view transformer and a transfer learning scheme are introduced for more efﬁcient learning and ﬁnetuning from sparse-view inputs at original resolutions. The efﬁcacy of DoubleField is validated by the quantitative evaluations on several datasets and the qualitative results in a real-world sparse multi-view system, showing its superior capability for photo-realistic free-viewpoint human rendering. For code and demo video, please refer to our project
Cayley-Klein Lie Bialgebras: Noncommutative Spaces, Drinfel'd Doubles and Kinematical Applications

The Cayley–Klein (CK) formalism is applied to the real algebra so(5) by making use of four graded contraction parameters describing, in a unified setting, 81 Lie algebras, which cover the (anti-)de Sitter, Poincaré, Newtonian and Carrollian algebras. Starting with the Drinfel’d–Jimbo real Lie bialgebra for so(5) together with its Drinfel’d double structure, we obtain the corresponding CK bialgebra and the CK r-matrix coming from a Drinfel’d double. As a novelty, we construct the (first-order) noncommutative CK spaces of points, lines, 2-planes and 3-hyperplanes, studying their structural properties. By requiring dealing with real structures, we found that there exist 63 specific real Lie bialgebras together with their sets of four noncommutative spaces. Furthermore, we found 14 classical r-matrices coming from Drinfel’d doubles, obtaining new results for the de Sitter so(4,1) and anti-de Sitter so(3,2) as well as for some of their contractions. These geometric results were exhaustively applied onto the (3 + 1)D kinematical algebras, considering not only the usual (3 + 1)D spacetime but also the 6D space of lines. We established different assignations between the geometrical CK generators and the kinematical ones, which convey physical identifications for the CK contraction parameters in terms of the cosmological constant/curvature Λ and the speed of light c. We, finally, obtained four classes of kinematical r-matrices together with their noncommutative spacetimes and spaces of lines, comprising all κ-deformations as particular cases.
Analysis of Magnetohydrodynamic Perturbations in the Radial-field Solar Wind from Parker Solar Probe Observations

We report analysis of sub-Alfvénic magnetohydrodynamic (MHD) perturbations in the low-β radial-field solar wind employing the Parker Solar Probe spacecraft data from 2018 October 31 to November 12. We calculate wavevectors using the singular value decomposition method and separate MHD perturbations into three eigenmodes (Alfvén, fast, and slow modes) to explore the properties of sub-Alfvénic perturbations and the role of compressible perturbations in solar wind heating. The MHD perturbations show a high degree of Alfvénicity in the radial-field solar wind, with the energy fraction of Alfvén modes dominating (∼45%–83%) over those of fast modes (∼16%–43%) and slow modes (∼1%–19%). We present a detailed analysis of a representative event on 2018 November 10. Observations show that fast modes dominate magnetic compressibility, whereas slow modes dominate density compressibility. The energy damping rate of compressible modes is comparable to the heating rate, suggesting the collisionless damping of compressible modes could be significant for solar wind heating. These results are valuable for further studies of the imbalanced turbulence near the Sun and possible heating effects of compressible modes at MHD scales in low-β plasma.
Supplementary information for Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions

We start with some definitions that commonly appear in the approximate message-passing literature, see e.g. [33, 36, 37]. The main regularity class of functions we will use is that of pseudo-Lipschitz functions, which roughly amounts to functions with polynomially bounded first derivatives. We include the required scaling w.r. t. the dimensions in the definition for convenience. Definition 1 (Pseudo-Lipschitz function). For k,K ∈ N∗ and any n,m ∈ N∗, a function φ : Rn×K → Rm×K is called a pseudo-Lipschitz of order k if there exists a constant L(k,K) such that for any x,y ∈ Rn×K , ‖φ(x)− φ(y)‖F √ m 6 L(k,K) (
Estimating the size of a closed population by modeling latent and observed heterogeneity.

The paper extends the empirical likelihood (EL) approach of Liu et al. to a new and very flexible family of latent class models for capture-recapture data also allowing for serial dependence on previous capture history, conditionally on latent type and covariates. The EL approach allows to estimate the overall population size directly rather than by adding estimates conditional to covariate configurations. A Fisher-scoring algorithm for maximum likelihood estimation is proposed and a more efficient alternative to the traditional EL approach for estimating the non-parametric component is introduced; this allows us to show that the mapping between the non-parametric distribution of the covariates and the probabilities of being never captured is one-to-one and strictly increasing. Asymptotic results are outlined, and a procedure for constructing profile likelihood confidence intervals for the population size is presented. Two examples based on real data are used to illustrate the proposed approach and a simulation study indicates that, when estimating the overall undercount, the method proposed here is substantially more efficient than the one based on conditional maximum likelihood estimation, especially when the sample size is not sufficiently large.
Deep Medial Fields

Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form. In this work, we introduce medial fields: a field function derived from the medial axis transform (MAT) that makes available information about the underlying 3D geometry that is immediately useful for a number of downstream tasks. In particular, the medial field encodes the local thickness of a 3D shape, and enables O(1) projection of a query point onto the medial axis. To construct the medial field we require nothing but the SDF of the shape itself, thus allowing its straightforward incorporation in any application that relies on signed distance fields. Working in unison with the O(1) surface projection supported by the SDF, the medial field opens the door for an entirely new set of efficient, shape-aware operations on implicit representations. We present three such applications, including a modification to sphere tracing that renders implicit representations with better convergence properties, a fast construction method for memory-efficient rigid-body collision proxies, and an efficient approximation of ambient occlusion that remains stable with respect to viewpoint variations.
Equivariant Graph Neural Networks for 3D Macromolecular Structure

Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptrons and apply equivariant graph neural networks to a wide range of tasks from structural biology. Our method outperforms all reference architectures on three out of eight tasks in the ATOM3D benchmark, is tied for first on two others, and is competitive with equivariant networks using higher-order representations and spherical harmonic convolutions. In addition, we demonstrate that transfer learning can further improve performance on certain downstream tasks. Code is available at https://github.com/drorlab/gvp-pytorch.
Scalable Computation of Monge Maps with General Costs

Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another. In spite of the rapid developments of the numerical methods for optimal transport problems, computing the Monge maps remains challenging, especially for high dimensional problems. In this paper, we present a scalable algorithm for computing the Monge map between two probability distributions. Our algorithm is based on a weak form of the optimal transport problem, thus it only requires samples from the marginals instead of their analytic expressions, and can accommodate optimal transport between two distributions with different dimensions. Our algorithm is suitable for general cost functions, compared with other existing methods for estimating Monge maps using samples, which are usually for quadratic costs. The performance of our algorithms is demonstrated through a series of experiments with both synthetic and realistic data.
From the traditional way of pyrolysis to tunable photoluminescent water soluble carbon nano-onions for cell imaging and selective sensing of glucose

The traditional pyrolysis of vegetable ghee leads to the fabrication of graphitic photoluminescent, water soluble carbon nano-onions (wsCNO) with tunable photoluminescence without using any metal catalyst. Simple oxidative treatment by nitric acid fabricated a high density “self-passivated” water soluble version. As-synthesized wsCNO possessed tunable photoluminescence behavior from the visible-to-near infrared region. Further small sized wsCNO separated from the bulk as-synthesized wsCNO via gel filtration achieved a highly fluorescent colored fraction, used for cell imaging (Escherichia coli and Pseudomonas putida) and selective, immediate sensing of glucose molecules based upon a simple fluorescence “turn-off”/“turn-on” technique.
Heteroepitaxy growth of GaAsBi on Ge(100) substrate by gas source molecular beam epitaxy

We have investigated the growth of GaAsBi single-crystal film on Ge substrate by gas source molecular beam epitaxy. A high-quality GaAsBi epilayer has been obtained. It has been found that the surfactant effect of Bi suppresses the interdiffusion of Ge at the GaAsBi/Ge interface and reduces the misfit dislocation density. The Bi atoms occupy the As sites, as indicated by the appearance of GaBi-like TO(Γ) and LO(Γ) phonon modes in Raman spectra. In addition, the redshift of the GaAs-like LO(Γ) phonon frequency has been observed in the Raman spectra, owing to the Bi-induced biaxial strain and the alloying effect as well.
Initiation behaviour in hydrogenation of pyrolysis gasoline over presulphided Ni-Mo-Zn/Al2O3 catalyst

A presulphided treatment was applied to the oxidic Ni-Mo-Zn/Al2O3 catalyst (nickel catalyst) in order to avoid thermal run-away during initiation of the hydrogenation of pyrolysis gasoline. The physico-chemical properties of the prepared oxidic nickel catalyst, the reduced and passivated (RP) nickel catalyst and the sulphided (RPS) nickel catalyst were characterised using N2 adsorption-desorption, X-ray diffraction, temperature-programmed reduction (TPR) and X-ray photoelectron spectroscopy (XPS). The TPR results showed that the reducibility of the RP Ni-Mo-Zn/Al2O3 catalyst was improved over the oxidic nickel catalyst. The XPS spectra confirmed the binding energy of the RPS nickel catalyst to be higher than that of the oxidic nickel catalyst. The catalytic performance was evaluated on a fixed-bed reactor (reaction temperature between 30 °C and 70°C, at 2.8 MPa of total pressure and weight hourly space velocity of 2.0 h−1, the volume of H2/pyrogasoline = 200: 1). The rising temperature of the RPS nickel catalyst was almost 20°C lower than that of the oxidic nickel catalyst during the initial stage of the hydrogenation reaction. The results indicated that the RPS nickel catalyst exhibited better stability than the oxidic nickel catalyst during the start-up period, thereby providing a better selectivity in long-term operation.
Extracorporeal Shockwave Therapy in the Management of Sports Medicine Injuries

Extracorporeal shockwave therapy can safely be used to treat musculoskeletal injuries, including tendinopathy and fasciopathy, in athletes. Abstract Treatment of musculoskeletal conditions in athletes with extracorporeal shockwave therapy (ESWT) is gaining popularity as greater evidence supports its use. ESWT protocols (describing energy flux density, number of impulses, type of shockwave (focused or radial), number/frequency/duration of treatment session, area of application, and postprocedural therapy protocols) can be adjusted in the clinical setting. Protocols vary across studies, and optimal protocols for most indications are yet to be determined. ESWT can safely be used to treat various musculoskeletal conditions in athletes, including rotator cuff tendinopathy, lateral elbow epicondlyopathy, greater trochanteric pain syndrome, hamstring tendinopathy, patellar tendinopathy, Achilles tendinopathy, other tendinopathies, plantar fasciopathy, bone stress injuries, and medial tibial stress syndrome. ESWT can be used to treat in-season athletes, as it often requires no/minimal time away from sport and may result in rapid benefits. ESWT should be used in conjunction with physical therapy to facilitate longer-term gains in function and to optimize healing.
Postcranial Osteology of the Basally Branching Hadrosauroid Dinosaur Tanius sinensis from the Upper Cretaceous Wangshi Group of Shandong, China

ABSTRACT Tanius sinensis was one of the first dinosaur species to be named from China. It was established on a partial skeleton recovered by a joint Sino-Swedish expedition in 1923. The fossils were excavated from Upper Cretaceous strata of the Jiangjunding Formation (Wangshi Group) in Shandong Province, and although their discovery dates back almost 100 years, they have not been reassessed in detail since their initial description in 1929. This omission is critical because T. sinensis is now recognized as one of the stratigraphically youngest non-hadrosaurid hadrosauroid taxa. Here, we reevaluate the postcranial osteology of T. sinensis as a prelude to an anatomical and phylogenetic revision of the species. We examined the holotype and all currently referred specimens of T. sinensis first-hand, and identified a unique postcranial character state combination incorporating tall dorsal neural spines, a reduced postacetabular ridge on the ilium, a fully enclosed flexor tunnel formed by the distal condyles of the femur, and a lunate proximal end on metatarsal III. Comparisons with other species of Tanius confirm that: (1) T. chingkankouensis is a nomen dubium erected on non-diagnostic composite material; (2) T. laiyangensis was established on indeterminate hadrosaurid remains that are not attributable to Tanius; and (3) the anecdotal assignments of Bactrosaurus prynadai and Tsintaosaurus spinorhinus to Tanius cannot be substantiated. Close inspection of the holotype caudal vertebra further reveals a possible healed bite trace consistent with a prey–predator interaction. Lastly, our calculated average body mass estimate for T. sinensis of between 2091–3533 kg suggests that it was one of the largest non-hadrosaurid hadrosauroids.
Judicial Corruption, Financial Development, and Economic Growth: Fresh Evidence from Nigeria

This paper attempts to juxtapose the relationship among judicial corruption, financial development and economic growth in Nigeria over the period of 1980 to 2016 using Autoregressive Distributed Lag (ARDL) Model and Granger-Causality Test. The former takes care of both long-run and short-run dynamic effects of judicial corruption and financial development as it affects economic growth while the latter captures the flow of causality among the variables. Therefore, the ARDL Bound test result reveals cointegration implying a long-run relationship among judicial corruption, financial development and economic growth. The short-run result shows that judicial corruption is negatively and significantly related to economic growth while ni the long-run, a positive but insignificant relationship exists. It also reveals that rule of law is positively and significantly related to economic growth in the short-run. However, the results of the Granger-causality test show that there is unidirectional causality running from judicial corruption to economic growth. In all, the study concludes that justice delayed is growth denied. The study, therefore, recommends that the institution of justice be reformed, civil litigations are discharged on time and judicial independence be restored accordingly.