Miracle-dz/UniFAR-FTUs huggingface dataset:
Action2Motion: Conditioned Generation of 3D Human Motions

Action recognition is a relatively established task, where given an input sequence of human motion, the goal is to predict its action category. This paper, on the other hand, considers a relatively new problem, which could be thought of as an inverse of action recognition: given a prescribed action type, we aim to generate plausible human motion sequences in 3D. Importantly, the set of generated motions are expected to maintain its diversity to be able to explore the entire action-conditioned motion space; meanwhile, each sampled sequence faithfully resembles a natural human body articulation dynamics. Motivated by these objectives, we follow the physics law of human kinematics by adopting the Lie Algebra theory to represent the natural human motions; we also propose a temporal Variational Auto-Encoder (VAE) that encourages a diverse sampling of the motion space. A new 3D human motion dataset, HumanAct12, is also constructed. Empirical experiments over three distinct human motion datasets (including ours) demonstrate the effectiveness of our approach.
GIS-AHP Multi-Decision-Criteria-Analysis for the Optimal Location of Solar Energy Plants at Indonesia

A reliable tool for site-suitability assessment of solar power plants capable to account for the sustainable development and protection of cultural and biodiversity conservation areas is proposed. We present a novel Analytic Hierarchy Process (AHP) based approach for the Multi-Decision Criteria Analysis (MDCA) of SSI satellite retrieved data and local information sources, it within a GIS platform tailored to fit the needs of energy stakeholders at Indonesia, simultaneously ensuring the conservation of legally protected areas. This imposes significant challenges due to the wide diversification of cultural, natural, and ecological protected areas that need to be considered, in landmarks that demand for high resolution imaging of surface solar irradiance (SSI) within $\pm 4^{\circ}$ of the equator. To overcome these challenges, a GIS spatial weighted overlay analysis for criteria layers such as climatology, topography, electrical grid, and road infrastructure has been performed, it based on the technical and economic feasibility for solar plants deployment within three approximation schemes focused on their proximity to the existing (i) power network, (ii) road infrastructure, and (iii) community settlements. Here, we focused on the West Kalimantan Province of Borneo Island (WKP), it mainly due to its possibility of onshore inter-connectivity and energy share with Malaysia and Brunei, and the high national and international importance that brings forward the protection of the biodiversity of Borneo. It has been found that the optimal location of PV plants can be reduced to just $0.03\%$ ($46.60~km^{2}$) and $0.07\%$ ($108.58~{km}^{2}$) of WKP, in what we report as the best-suitable conditions out of the $33.05\%$ exploitable area found after the exclusion of conservation areas. This corresponds to .
The relationship between testicular activity, accessory sex glands, and circulating steroid concentration during the reproductive cycle in a male Indian vespertilionid bat, Scotophilus heathi

The relationship between spermatogenesis, accessory sex glands, Leydig cell activity, and circulating concentrations of testosterone and androstenedione in male Scotophilus heathi, the greater yellow bat, was studied. Scotophilus heathi accumulated fat prior to winter dormancy at Varanasi, India. Spermatogenesis was seasonal and extended over the period November to March, with the testes becoming quiescent during winter. Monthly changes in testis and accessory sex gland masses showed two peaks, in November and January. Accessory gland secretory activity and fructose concentrition showed only one peak, during January and February, which coincided with the mating period. The mass in the epididymides and their histological changes reflect the influence of testicular spermatogenesis. However, sperm were found in the cauda epididymidis during the spermatogenically quiescent period of winter dormancy. The Leydig cells showed intense side chain cleavage (SCC) enzyme activity from August to December. In late January and February, males had smaller Leydig cells and low SCC activity. Monthly changes in serum testosterone concentration included two peaks in November and January, coinciding with peak spermatogenic activity, whereas androstenedione showed only one peak, an unusually high concentration in November. The circulating serum androstenedione concentration may be responsible for the unique reproductive activity noted in this bat.
Poynting vector, super-Poynting vector, and principal observers in electromagnetism and general relativity

In electromagnetism, the concept of Poynting vector as measured by an observer is well known. A mathematical analogue in general relativity is the super-Poynting vector of the Weyl tensor. Observers for which the (super-)Poynting vector vanishes are called principal. When, at a given point, the electromagnetic field is non-null, or the gravitational field is of Weyl–Petrov type I or D, principal observers instantaneously passing through that point always exist. We survey characterizations of such observers and study their relation to arbitrary observers. In the non-null electromagnetic case it is known that, given any observer, there is a principal observer which moves relative to the first in the direction of his Poynting vector. Replacing Poynting by super-Poynting yields a possible gravitational analogue; we show that this analogy indeed holds for any observer when the Petrov type is D, but only for a one-dimensional variety of observers when the Petrov type is I. We provide algorithms to obtain the principal observers directly from the electric and magnetic fields (in the electromagnetic case) or electric and magnetic parts of the Weyl tensor (in the gravitational case) relative to an arbitrary observer. It is found that in Petrov type D doubly aligned non-null Einstein–Maxwell fields (which include all classical charged black hole solutions) the Poynting and super-Poynting vectors are aligned, at each point and for each observer, and the principal observers coincide. Our results are illustrated in simple examples.
Quantum Advantage in Information Retrieval

Random access codes have provided many examples of quantum advantage in communication, but concern only one kind of information retrieval task. We introduce a related task -- the Torpedo Game -- and show that it admits greater quantum advantage than the comparable random access code. Perfect quantum strategies involving experimentally accessible three-level systems emerge via analysis in terms of the discrete Wigner function. The example is leveraged to an operational advantage in a pacifist version of the strategy game Battleship. We pinpoint a characteristic of quantum systems that enables quantum advantage in any bounded-memory information retrieval task. While preparation contextuality has previously been linked to advantages in random access coding, we focus here on a different characteristic called sequential contextuality. It is shown not only to be necessary and sufficient for quantum advantage, but also to quantify the degree of advantage.
The influence of types of toilets used and personal hygiene on the prevalence of helminthosis among primary school children in Zaria, Kaduna State

Stool samples were carried out to determine the influence of some factors on the prevalence of helminthosis among primary school children in Zaria between March, 2007 and Febuary , 2009. Faecal Samples were collected from 1,204 children from ages 6-12 years in 20 primary schools randomly selected. The samples were processed using formal ether concentration techniques. Of the 1,204 children examined. 690 (57.3%) of the study population were infected with helminthes. The factors like toilet facilities and personal hygiene were found to influence the prevalence of helminth parasites.
Multidimensional parameter estimation of heavy‐tailed moving averages

In this article we present a parametric estimation method for certain multiparameter heavy‐tailed Lévy‐driven moving averages. The theory relies on recent multivariate central limit theorems obtained via Malliavin calculus on Poisson spaces. Our minimal contrast approach is related to previous papers, which propose to use the marginal empirical characteristic function to estimate the one‐dimensional parameter of the kernel function and the stability index of the driving Lévy motion. We extend their work to allow for a multiparametric framework that in particular includes the important examples of the linear fractional stable motion, the stable Ornstein–Uhlenbeck process, certain CARMA(2, 1) models, and Ornstein–Uhlenbeck processes with a periodic component among other models. We present both the consistency and the associated central limit theorem of the minimal contrast estimator. Furthermore, we demonstrate numerical analysis to uncover the finite sample performance of our method.
DeepPeep

The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption and growing commercialization of DNNs have underscored the importance of intellectual property (IP) protection. Devising techniques to ensure IP protection has become necessary due to the increasing trend of outsourcing the DNN computations on the untrusted accelerators in cloud-based services. The design methodologies and hyper-parameters of DNNs are crucial information, and leaking them may cause massive economic loss to the organization. Furthermore, the knowledge of DNN’s architecture can increase the success probability of an adversarial attack where an adversary perturbs the inputs and alters the prediction. In this work, we devise a two-stage attack methodology “DeepPeep,” which exploits the distinctive characteristics of design methodologies to reverse-engineer the architecture of building blocks in compact DNNs. We show the efficacy of “DeepPeep” on P100 and P4000 GPUs. Additionally, we propose intelligent design maneuvering strategies for thwarting IP theft through the DeepPeep attack and proposed “Secure MobileNet-V1. ” Interestingly, compared to vanilla MobileNet-V1, secure MobileNet-V1 provides a significant reduction in inference latency (≈60%) and improvement in predictive performance (≈2%) with very low memory and computation overheads.
Optimal Delegation Under Unknown Bias: The Role of Concavity

A principal is uncertain of an agent's preferences and cannot provide monetary transfers. The principal, however, does control the discretion granted to the agent. In this paper, we provide a simple characterization of when it is optimal for the principal to screen by offering different terms of discretion to the agent. When the principal's utility is sufficiently concave, it is optimal for the principal to pool and to offer all agents the same discretion. Thus, for any number of agents and any distribution over agent preferences, the optimal contract is simple: the principal sets a cap and forbids actions above this cap (interval delegation). For less concave preferences, it is optimal for the principal to screen. The principal benefits by providing agents a choice between interval delegation and gap delegation, which allows for more extreme actions but prohibits intermediate actions. Moreover, we provide new intuition for the optimality of interval delegation when the principal knows the agent's preferences: the payoff distributions generated by sets containing gaps are mean-preserving spreads of those generated by intervals.
Exactly solvable single-trace four point correlators in $\chi$CFT$_4$

In this paper we study a wide class of planar single-trace four point correlators in the chiral conformal field theory ($\chi$CFT$_4$) arising as a double scaling limit of the $\gamma$-deformed $\mathcal{N}=4$ SYM theory. In the planar (t'Hooft) limit, each of such correlators is described by a single Feynman integral having the bulk topology of a square lattice "fishnet" and/or of an honeycomb lattice of Yukawa vertices. The computation of this class of Feynmann integrals at any loop is achieved by means of an exactly-solvable spin chain magnet with $SO(1,5)$ symmetry. In this paper we explain in detail the solution of the magnet model as presented in our recent letter and we obtain a general formula for the representation of the Feynman integrals over the spectrum of the separated variables of the magnet, for any number of scalar and fermionic fields in the corresponding correlator. For the particular choice of scalar fields only, our formula reproduces the conjecture of B. Basso and L. Dixon for the fishnet integrals.
e$^+$@JLab White Paper: An Experimental Program with Positron Beams at Jefferson Lab

Positron beams, both polarized and unpolarized, are identified as essential ingredients for the experimental program at the next generation of lepton accelerators. In the context of the Hadronic Physics program at the Jefferson Laboratory (JLab), positron beams are complementary, even essential, tools for a precise understanding of the electromagnetic structure of the nucleon, in both the elastic and the deep-inelastic regimes. For instance, elastic scattering of (un)polarized electrons and positrons off the nucleon allows for a model independent determination of the electromagnetic form factors of the nucleon. Also, the deeply virtual Compton scattering of (un)polarized electrons and positrons allows us to separate unambiguously the different contributions to the cross section of the lepto-production of photons, enabling an accurate determination of the nucleon Generalized Parton Distributions (GPDs), and providing an access to its Gravitational Form Factors. Furthermore, positron beams offer the possibility of alternative tests of the Standard Model through the search of a dark photon, the precise measurement of electroweak couplings, or the investigation of lepton flavor violation. This white paper discusses the perspectives of an experimental program with positron beams at JLab.
Updated predictions for gravitational waves produced in a strongly supercooled phase transition

We update predictions for the gravitational wave (GW) signal from a strongly supercooled phase transition in an illustrative classically conformal U(1)B−L model. We implement ∝ γ2 scaling of the friction on the bubble wall and update the estimates for the efficiency factors for GW production from bubble collisions and plasma-related sources. We take into account the fact that a small decay rate of the symmetry-breaking field may lead to brief matter-dominated era after the transition, as the field oscillates around its minimum before decaying. We find that a strong bubble collision signal occurs in a significant part of the parameter space, and that the modified redshift of the modes that re-enter the horizon during the matter-dominated period generates a characteristic tilted 'plateau' in the spectrum. The GW spectrum in this model would be detectable in the low-frequency range, e.g., by LISA, and in the mid-frequency range, e.g., by AION/MAGIS and AEDGE, and in the high-frequency range by LIGO and ET. The peak frequency of the signal is limited from below by collider constraints on the mass of the U(1)B−L gauge boson, while at high frequencies the slow decay of the scalar field and the resulting matter-dominated era diminishes the GW signal.
Hierarchical mergers in young, globular and nuclear star clusters: black hole masses and merger rates

Hierarchical mergers are one of the distinctive signatures of binary black hole (BBH) formation through dynamical evolution. Here, we present a fast Monte Carlo approach to simulate hierarchical mergers in nuclear star clusters (NSCs), globular clusters (GCs) and young star clusters (YSCs). Hierarchical mergers are orders of magnitude more common in NSCs than they are in both GCs and YSCs, because of the different escape velocity. In our fiducial model, the fraction of hierarchical mergers over all mergers is $\sim{}0.15$, $\sim{}6\times{}10^{-3}$ and $\sim{}10^{-4}$ in NSCs, GCs and YSCs, respectively. The mass distribution of hierarchical BBHs strongly depends on the properties of first-generation BBHs, such as their progenitor's metallicity. In our fiducial model, we form black holes (BHs) with masses up to $\sim{}10^3$ M$_\odot$ in NSCs and up to $\sim{}10^2$ M$_\odot$ in both GCs and YSCs. When escape velocities in excess of 100 km s$^{-1}$ are considered, BHs with mass $>10^3$ M$_\odot$ are allowed to form in NSCs. Hierarchical mergers lead to the formation of BHs in the pair instability mass gap and intermediate-mass BHs (IMBHs), but only in metal-poor environments. In our fiducial model, at metallicity $Z\sim{}0.0002$, the fraction of BBH mergers with primary BH in the pair instability mass gap is $\sim{}7\times{}10^{-3}$, $3\times{}10^{-4}$ and $5\times{}10^{-6}$ in NSCs, GCs and YSCs, respectively. In metal-poor NSCs, the fraction of BBH mergers with primary mass in the IMBH regime is $\sim{}5\times{}10^{-4}$. The local BBH merger rate in our models ranges from $\sim{}10$ to $\sim{} 60$ Gpc$^{-3}$ yr$^{-1}$; hierarchical BBHs in NSCs account for $\sim{}10^{-2}- 0.2$ Gpc$^{-3}$ yr$^{-1}$, with a strong upper limit of $\sim{}10$ Gpc$^{-3}$ yr$^{-1}$.
End-to-end information extraction from business documents

We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.
A Power Analysis for Knockoffs with the Lasso Coefficient-Difference Statistic

In a linear model with possibly many predictors, we consider variable selection procedures given by $$ \{1\leq j\leq p: |\widehat{\beta}_j(\lambda)| > t\}, $$ where $\widehat{\beta}(\lambda)$ is the Lasso estimate of the regression coefficients, and where $\lambda$ and $t$ may be data dependent. Ordinary Lasso selection is captured by using $t=0$, thus allowing to control only $\lambda$, whereas thresholded-Lasso selection allows to control both $\lambda$ and $t$. The potential advantages of the latter over the former in terms of power---figuratively, opening up the possibility to look further down the Lasso path---have been quantified recently leveraging advances in approximate message-passing (AMP) theory, but the implications are actionable only when assuming substantial knowledge of the underlying signal. In this work we study theoretically the power of a knockoffs-calibrated counterpart of thresholded-Lasso that enables us to control FDR in the realistic situation where no prior information about the signal is available. Although the basic AMP framework remains the same, our analysis requires a significant technical extension of existing theory in order to handle the pairing between original variables and their knockoffs. Relying on this extension we obtain exact asymptotic predictions for the true positive proportion achievable at a prescribed type I error level. In particular, we show that the knockoffs version of thresholded-Lasso can perform much better than ordinary Lasso selection if $\lambda$ is chosen by cross-validation on the augmented matrix.
Field characterisation of the phenotypic variability in the Italian rice germplasm

Plant breeding is amongst the most promising technologies to further enhance yields of major crops. Effective breeding strategies require phenotypic characterisation of the available germplasm, which is supported by the availability of relevant field experimental datasets. We characterised the phenotypic expression of fourteen yield components traits in a panel of 40 cultivars selected to maximise the phenotypic diversity present in the Italian rice germplasm in a two-season field experiment. The measured range of phenotypic variation measured was high (CVs ranging from 5.9% to 45.4%) and multiple strong associations emerged in all the analysed traits (0.32 < ρ < 0.81). Our findings confirmed that proper balance between sink and source organs favours grain production, as well as the extended duration of phenological phases. Aboveground biomass accumulation before flowering also positively correlated with yield (ρ = 0.39). The released dataset depicts the phenotypic variability in Italian rice cultivars and could be used to improve crop modelling applications in the area.
Probing Thermally Activated Atomic and Nanocrystalline Defect Motion Through Noise Processes in 
RuO2
 Nanowires

The present-day nanodevice dimensions continuously shrink, with the aim to prolong Moore's law. As downsizing meticulously persists, undesirable dynamic defects, which cause low-frequency noise and structural instability, play detrimental roles on limiting the ultimate performance and reliability of miniaturized devices. A good understanding and a meaningful control of the defect kinetics then become fundamental and urgent issues. Here we report observations of thermally activated atomic defect motion as well as nanocrystalline defect motion through electrical noise processes in metallic RuO$_2$ rutile nanowires around room temperature. First, we extract the energy distribution function and the number density of mobile atomic defects (oxygen vacancies). Second, we obtain the geometrical size, grain-boundary bonding strength, and relaxation times of dynamic nanocrystallites. Our results show clearly a powerful probe for effective and noninvasive characterizations of nanostructures and nanomaterials for which quantitative information about mechanical hardness, breakdown current density, and/or resistance noise is essential.
Growing Efficient Deep Networks by Structured Continuous Sparsification

We develop an approach to training deep networks while dynamically adjusting their architecture, driven by a principled combination of accuracy and sparsity objectives. Unlike conventional pruning approaches, our method adopts a gradual continuous relaxation of discrete network structure optimization and then samples sparse subnetworks, enabling efficient deep networks to be trained in a growing and pruning manner. Extensive experiments across CIFAR-10, ImageNet, PASCAL VOC, and Penn Treebank, with convolutional models for image classification and semantic segmentation, and recurrent models for language modeling, show that our training scheme yields efficient networks that are smaller and more accurate than those produced by competing pruning methods.