Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-view Image-based Hand Geometry Refinement using Differentiable Monte Carlo Ray Tracing

The amount and quality of datasets and tools available in the research field of hand pose and shape estimation act as evidence to the significant progress that has been made.However, even the datasets of the highest quality, reported to date, have shortcomings in annotation. We propose a refinement approach, based on differentiable ray tracing,and demonstrate how a high-quality publicly available, multi-camera dataset of hands(InterHand2.6M) can become an even better dataset, with respect to annotation quality. Differentiable ray tracing has not been employed so far to relevant problems and is hereby shown to be superior to the approximative alternatives that have been employed in the past. To tackle the lack of reliable ground truth, as far as quantitative evaluation is concerned, we resort to realistic synthetic data, to show that the improvement we induce is indeed significant. The same becomes evident in real data through visual evaluation.
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering

Video question answering is a challenging task, which requires agents to be able to understand rich video contents and perform spatial-temporal reasoning. However, existing graph-based methods fail to perform multi-step reasoning well, neglecting two properties of VideoQA: (1) Even for the same video, different questions may require different amount of video clips or objects to infer the answer with relational reasoning; (2) During reasoning, appearance and motion features have complicated interdependence which are correlated and complementary to each other. Based on these observations, we propose a Dual-Visual Graph Reasoning Unit (DualVGR) which reasons over videos in an end-to-end fashion. The first contribution of our DualVGR is the design of an explainable Query Punishment Module, which can filter out irrelevant visual features through multiple cycles of reasoning. The second contribution is the proposed Video-based Multi-view Graph Attention Network, which captures the relations between appearance and motion features. Our DualVGR network achieves state-of-the-art performance on the benchmark MSVD-QA and SVQA datasets, and demonstrates competitive results on benchmark MSRVTT-QA datasets. Our code is available at https://github.com/MM-IR/DualVGR-VideoQA.
Sharp subcritical Sobolev inequalities and uniqueness of nonnegative solutions to high-order Lane-Emden equations on $ \mathbb{S}^n $

<p style='text-indent:20px;'>In this paper, we are concerned with the uniqueness result for non-negative solutions of the higher-order Lane-Emden equations involving the GJMS operators on <inline-formula><tex-math id="M2">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula>. Since the classical moving-plane method based on the Kelvin transform and maximum principle fails in dealing with the high-order elliptic equations in <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula>, we first employ the Mobius transform between <inline-formula><tex-math id="M4">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M5">\begin{document}$ \mathbb{R}^n $\end{document}</tex-math></inline-formula>, poly-harmonic average and iteration arguments to show that the higher-order Lane-Emden equation on <inline-formula><tex-math id="M6">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula> is equivalent to some integral equation in <inline-formula><tex-math id="M7">\begin{document}$ \mathbb{R}^n $\end{document}</tex-math></inline-formula>. Then we apply the method of moving plane in integral forms and the symmetry of sphere to obtain the uniqueness of nonnegative solutions to the higher-order Lane-Emden equations with subcritical polynomial growth on <inline-formula><tex-math id="M8">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula>. As an application, we also identify the best constants and classify the extremals of the sharp subcritical high-order Sobolev inequalities involving the GJMS operators on <inline-formula><tex-math id="M9">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula>. Our results do not seem to be in the literature even for the Lane-Emden equation and sharp subcritical Sobolev inequalities for first order derivatives on <inline-formula><tex-math id="M10">\begin{document}$ \mathbb{S}^n $\end{document}</tex-math></inline-formula>. </p>
Accurate quasinormal modes of the five-dimensional Schwarzschild-Tangherlini black holes

The objective of this paper is to construct the accurate (say, to 11 decimal places) frequencies of the quasinormal modes of the 5-dimensional Schwarzschild-Tangherlini black hole using three major techniques: the Hill determinant method, the continued fractions method and the WKB-Padé method and to discuss the limitations of each. It is shown that for the massless scalar, gravitational tensor, gravitational vector and electromagnetic vector perturbations considered in this paper, the Hill determinant method and the method of continued fractions (both with the convergence acceleration) always give identical results, whereas the WKB-Padé method gives the results that are amazingly accurate in most cases. Notable exception are the gravitational vector perturbations (j = 2 and ` = 2), for which the WKB-Padé approach apparently does not work. Here we have interesting situation in which the WKB-based methods (WKB-Padé and WKB-Borel-Le Roy) give the complex frequency that differs from the from the result obtained within the framework of the continued fraction method and the Hill determinant method. For the fundamental mode, deviation of the real part of frequency from the exact value is 0.5% whereas the deviation of the imaginary part is 2.7%. For ` ≥ 3 the accuracy of the WKB results is similar again to the accuracy obtained for other perturbations. The case of the gravitational scalar perturbations is briefly discussed.
T-s3ra: traffic-aware scheduling for secure slicing and resource allocation in sdn/nfv enabled 5g networks

Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements including massive machine-type communication, ultrareliable low-latency communication, and enhanced mobile broadband.
Bloch theorem in the presence of an additional conserved charge

The Bloch theorem is a general theorem restricting the persistent current associated with a conserved U(1) charge in a ground state or in a thermal equilibrium. It gives an upper bound of the magnitude of the current density, which is inversely proportional to the system size. In a recent paper, Else and Senthil applied the argument for the Bloch theorem to a generalized Gibbs ensemble, assuming the presence of an additional conserved charge, and predicted a nonzero current density in the nonthermal steady state [D. V. Else and T. Senthil, Phys. Rev. B 104, 205132 (2021)]. In this work, we provide a complementary derivation based on the canonical ensemble, given that the additional charge is strictly conserved within the system by itself. Furthermore, using the example where the additional conserved charge is the momentum operator, we discuss that the persistent current tends to vanish when the system is in contact with an external momentum reservoir in the co-moving frame of the reservoir.
Semi-Supervised Learning with Multi-Head Co-Training

Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algorithm should be delicately designed to prevent individual classifiers from collapsing into each other. To remove these obstacles which deter the adoption of single-view co-training, we present a simple and efficient algorithm Multi-Head Co-Training. By integrating base learners into a multi-head structure, the model is in a minimal amount of extra parameters. Every classification head in the unified model interacts with its peers through a “Weak and Strong Augmentation” strategy, in which the diversity is naturally brought by the strong data augmentation. Therefore, the proposed method facilitates single-view co-training by 1). promoting diversity implicitly and 2). only requiring a small extra computational overhead. The effectiveness of Multi-Head Co-Training is demonstrated in an empirical study on standard semi-supervised learning benchmarks.
Dark Energy Survey Year 3 Results: A 2.7% measurement of Baryon Acoustic Oscillation distance scale at redshift 0.835

We present angular diameter measurements obtained by measuring the position of Baryon Acoustic Oscillations (BAO) in an optimised sample of galaxies from the first three years of Dark Energy Survey data (DES Y3). The sample consists of 7 million galaxies distributed over a footprint of 4100 deg$^2$ with $0.6<z_{\rm photo}<1.1$ and a typical redshift uncertainty of $0.03(1+z)$. The sample selection is the same as in the BAO measurement with the first year of DES data, but the analysis presented here uses three times the area, extends to higher redshift and makes a number of improvements, including a fully analytical BAO template, the use of covariances from both theory and simulations, and an extensive pre-unblinding protocol. We used two different statistics: angular correlation function and power spectrum, and validate our pipeline with an ensemble of over 1500 realistic simulations. Both statistics yield compatible results. We combine the likelihoods derived from angular correlations and spherical harmonics to constrain the ratio of comoving angular diameter distance $D_M$ at the effective redshift of our sample to the sound horizon scale at the drag epoch. We obtain $D_M(z_{\rm eff}=0.835)/r_{\rm d} = 18.92 \pm 0.51$, which is consistent with, but smaller than, the Planck prediction assuming flat \lcdm, at the level of $2.3 \sigma$. The analysis was performed blind and is robust to changes in a number of analysis choices. It represents the most precise BAO distance measurement from imaging data to date, and is competitive with the latest transverse ones from spectroscopic samples at $z>0.75$. When combined with DES 3x2pt + SNIa, they lead to improvements in $H_0$ and $\Omega_m$ constraints by $\sim 20\%$
Viscous interacting and stability on dark matter Bose-Einstein condensation with modified Chaplygin gas

In this paper, the viscous cosmological dynamics are studied in the presence of dark matter Bose-Einstein Condensation (BEC) by curved-FRW background. For this purpose, we use the BEC regime rather than the normal dark matter (the cold dark matter or the barotropic dark matter) with the dark matter Equation of State (EoS) as pdm ∝ p2dm, which arises from the gravitational form. Therefore, we obtain the corresponding continuity equations with the existence of the universe components by considering an interacting model with modified Chaplygin gas. Afterward, we derive the energy density and the pressure of dark energy in terms of the redshift parameter. And then, by introducing a parametrization function and fitting it with 51 supernova data with the likelihood analysis, we find the cosmological parameters versus redshift parameter. In what follows, we plot the corresponding dynamic graphs proportional to redshift, and then we represent the universe is currently undergoing an accelerated expansion phase. Finally, we explore the stability and the instability of the present model with the sound speed parameter.
Improving forecasting by subsampling seasonal time series

ABSTRACT Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance of model selection, we propose a simple and reliable algorithm to improve the forecasting performance. Specifically, we construct multiple time series with different sub-seasons from the original time series. These derived series highlight different sub-seasonal patterns of the original series, making it possible for the forecasting methods to capture diverse patterns and components of the data. Subsequently, we produce forecasts for these multiple series separately with classical statistical models (ETS or ARIMA). Finally, the forecasts are combined. We evaluate our approach on widely used forecasting competition data sets (M1, M3, and M4) in terms of both point forecasts and prediction intervals. We observe performance improvements compared with the benchmarks. Our approach is particularly suitable and robust for the data with higher frequency. To demonstrate the practical value of our proposition, we showcase the performance improvements from our approach on hourly load data that exhibit multiple seasonal patterns.
Construction of 
A−B
 heterolayer intermetallic crystals: Case studies of the 1144-phase TM-phosphides 
AB
(TM)
4P4
 (TM=Fe, Ru, Co, Ni)

B. Q. Song, 2 Mingyu Xu, 2 Vladislav Borisov, 4 Olena Palasyuk, C. Z. Wang, 2 Roser Valent́ı, Paul C. Canfield, 2 and K. M. Ho 2 Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA Institute of Theoretical Physics, Goethe University Frankfurt am Main, D-60438 Frankfurt am Main, Germany Department of Physics and Astronomy, Uppsala University, 752 36 Uppsala, Sweden (Dated: October 5, 2021)
Entropy Regularized Motion Planning via Stein Variational Inference

Many Imitation and Reinforcement Learning approaches rely on the availability of expert-generated demonstrations for learning policies or value functions from data. Obtaining a reliable distribution of trajectories from motion planners is non-trivial, since it must broadly cover the space of states likely to be encountered during execution while also satisfying task-based constraints. We propose a sampling strategy based on variational inference to generate distributions of feasible, low-cost trajectories for high-dof motion planning tasks. This includes a distributed, particle-based motion planning algorithm which leverages a structured graphical representations for inference over multi-modal posterior distributions. We also make explicit connections to both approximate inference for trajectory optimization and entropy-regularized reinforcement learning.
Holonomic quantum manipulation in the Weyl disk

It has been shown that a Weyl point in a superconducting nanostructure may give rise to a Weyl disk where two quantum states are almost degenerate in a 2D manifold in the parametric space. This opens up the possibility of a holonomic quantum manipulation: a transformation of the wave function upon adiabatic change of the parameters within the degenerate manifold. In this paper, we investigate in detail the opportunities for holonomic manipulation in Weyl disks. We compute the connection at the manifold in quasiclassical approximation to show it is Abelian and can be used for a phase gate. To provide a closed example of quantum manipulation that includes a state preparation and read-out, we augment the holonomic gate with a change of parameters that brings the system out of the degenerate subspace. For numerical illustrations, we use a finite value of quasiclassical parameter and exact quantum dynamics. We investigate the fidelity of an example gate for different execution times.
Convexification with Bounded Gap for Randomly Projected Quadratic Optimization

Random projection techniques based on Johnson-Lindenstrauss lemma are used for randomly aggregating the constraints or variables of optimization problems while approximately preserving their optimal values, that leads to smaller-scale optimization problems. D'Ambrosio et al. have applied random projection to a quadratic optimization problem so as to decrease the number of decision variables. Although the problem size becomes smaller, the projected problem will also almost surely be non-convex if the original problem is non-convex, and hence will be hard to solve. In this paper, by focusing on the fact that the level of the non-convexity of a non-convex quadratic optimization problem can be alleviated by random projection, we find an approximate global optimal value of the problem by attributing it to a convex problem with smaller size. To the best of our knowledge, our paper is the first to use random projection for convexification of non-convex optimization problems. We evaluate the approximation error between optimum values of a non-convex optimization problem and its convexified randomly projected problem.
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning

We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner's public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment.
Population density and spreading of COVID-19 in England and Wales

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening coincided with increased variance indicative of conditions with local impact and country scale heterogeneity. University reopening and December holidays reduced variance indicative of country scale homogenisation which reached a minimum in mid-January 2021. Homogeneous propagation was associated with better correspondence with normally distributed residuals while heterogeneous propagation was more consistent with skewed models. Skewness varied from strongly negative to strongly positive revealing an unappreciated feature of community propagation. Hot spots and super-spreading events are well understood descriptors of regional disease dynamics that would be expected to be associated with positively skewed distributions. Positively skewed behaviour was observed; however, negative skewness indicative of “cold-spots” and “super-isolation” dominated for approximately 8 months during the period of study. In contrast, death metrics showed near constant behaviour in scaling, variance, and skewness metrics over the full period with rural regions preferentially affected, an observation consistent with regional age demographics in England and Wales. Regional positions relative to density scaling laws were remarkably persistent after the first 5–9 days of the available data set. The determinants of this persistent behaviour probably precede the pandemic and remain unchanged.
Neat: Low-Complexity, Efficient On-Chip Cache Coherence

Cache coherence protocols such as MESI that use writer-initiated invalidation have high complexity and sometimes have poor performance and energy usage, especially under false sharing. Such protocols require numerous transient states, a shared directory, and support for core-to-core communication, while also suffering under false sharing. An alternative to MESI's writer-initiated invalidation is self-invalidation, which achieves lower complexity than MESI but adds high performance costs or relies on programmer annotations or specific data access patterns. This paper presents Neat, a low-complexity, efficient cache coherence protocol. Neat uses self-invalidation, thus avoiding MESI's transient states, directory, and core-to-core communication requirements. Neat uses novel mechanisms that effectively avoid many unnecessary self-invalidations. An evaluation shows that Neat is simple and has lower verification complexity than the MESI protocol. Neat not only outperforms state-of-the-art self-invalidation protocols, but its performance and energy consumption are comparable to MESI's, and it outperforms MESI under false sharing.
Combating fake news by empowering fact-checked news spread via topology-based interventions

Rapid information diffusion and large-scaled information cascades can enable the undesired spread of false information. A small-scaled false information outbreak may potentially lead to an infodemic. We propose a novel information diffusion and intervention technique to combat the spread of false news. As false information is often spreading faster in a social network, the proposed diffusion methodology inhibits the spread of false news by proactively diffusing the fact-checked information. Our methodology mainly relies on defining the potential super-spreaders in a social network based on their centrality metrics. We run an extensive set of experiments on different networks to investigate the impact of centrality metrics on the performance of the proposed diffusion and intervention models. The obtained results demonstrate that empowering the diffusion of fact-checked news combats the spread of false news further and deeper in social networks.