Miracle-dz/UniFAR-FTUs huggingface dataset:
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github. io.
ASP: Automatic Selection of Proxy dataset for efficient AutoML

Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters, which may take a large amount of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms and neural architecture search (NAS) algorithms. In this paper, we propose an Automatic Selection of Proxy dataset framework (ASP) aimed to dynamically find the informative proxy subsets of training data at each epoch, reducing the training data size as well as saving the AutoML processing time. We verify the effectiveness and generalization of ASP on CIFAR10, CIFAR100, ImageNet16-120, and ImageNet-1k, across various public model benchmarks. The experiment results show that ASP can obtain better results than other data selection methods at all selection ratios. ASP can also enable much more efficient AutoML processing with a speedup of 2x-20x while obtaining better architectures and better hyper-parameters compared to utilizing the entire dataset.
Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait

Objective: As metabolic cost is a primary factor influencing humans' gait, we want to deepen our understanding of metabolic energy expenditure models. Therefore, this paper identifies the parameters and input variables, such as muscle or joint states, that contribute to accurate metabolic cost estimations. Methods: We explored the parameters of four metabolic energy expenditure models in a Monte Carlo sensitivity analysis. Then, we analysed the model parameters by their calculated sensitivity indices, physiological context, and the resulting metabolic rates during the gait cycle. The parameter combination with the highest accuracy in the Monte Carlo simulations represented a quasi-optimized model. In the second step, we investigated the importance of input parameters and variables by analysing the accuracy of neural networks trained with different input features. Results: Power-related parameters were most influential in the sensitivity analysis and the neural network-based feature selection. We observed that the quasi-optimized models produced negative metabolic rates, contradicting muscle physiology. Neural network-based models showed promising abilities but have been unable to match the accuracy of traditional metabolic energy expenditure models. Conclusion: We showed that power-related metabolic energy expenditure model parameters and inputs are most influential during gait. Furthermore, our results suggest that neural network-based metabolic energy expenditure models are viable. However, bigger datasets are required to achieve better accuracy. Significance: As there is a need for more accurate metabolic energy expenditure models, we explored which musculoskeletal parameters are essential when developing a model to estimate metabolic energy.
Approximate Expressions for the Capillary Force and the Surface Area of a Liquid Bridge between Identical Spheres

We consider a liquid bridge between two identical spheres and provide approximate expressions for the capillary force and the exposed surface area of the liquid bridge as functions of the liquid bridge's total volume and the sphere separation distance. The radius of the spheres and the solid-liquid contact angle are parameters that enter the expressions. These expressions are needed for efficient numerical simulations of drying suspensions.
Hybrid Trajectory Optimization of Simple Skateboarding Tricks through Contact

Trajectories are optimized for a two-dimensional simplified skateboarding system to allow it to perform a fundamental skateboarding trick called an"ollie". A methodology for generating trick trajectories by controlling the position of a point-mass relative to a board is presented and demonstrated over a range of peak jump heights. A hybrid dynamics approach is taken to perform this optimization, with contact constraints applied along a sequence of discrete timesteps based on the board's position throughout designated sections of the trick. These constraints introduce explicit and implicit discontinuities between chosen sections of the trick sequence. The approach has been shown to be successful for a set of realistic system parameters.
Learning Lens Blur Fields

Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high-dimensional neural representation of blur$-$$\textit{the lens blur field}$$-$and a practical method for acquiring it. The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2D point spread function over image plane location, focus setting and, optionally, depth and (2) represent these variations parametrically as a single, sensor-specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel-specific micro-lenses. To learn the real-world blur field of a given device, we formulate a generalized non-blind deconvolution problem that directly optimizes the MLP weights using a small set of focal stacks as the only input. We also provide a first-of-its-kind dataset of 5D blur fields$-$for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Lastly, we show that acquired 5D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model.
Diurnal epigeic activity of myriapods (Chilopoda, Diplopoda)

Soil is inhabited by a huge number of invertebrate species, the so called soil fauna. For some of them, the soil represents a refuge. Out of the soil the environment is less suitable; there is too much light, and the humidity and temperature are more instable. For these reasons many species of soil fauna show specifi c pattern of epigeic activity – they are more active during periods with more favourable conditions, i.e. during spring and autumn in annual rhythm and/or during night in diurnal rhythm.
Global Factors in Non-core Bank Funding and Exchange Rate Flexibility

We show that fluctuations in the ratio of non-core to core funding in the banking systems of advanced economies are driven by a handful of global factors of both real and financial natures, with country-specific factors playing no significant roles. Exchange rate flexibility helps insulate the non-core to core ratio from such global factors but only significantly so outside periods of major global financial disruptions, as in 2008-2009.
Random resetting in search problems

By periodically returning a search process to a known or random state, random resetting possesses the potential to unveil new trajectories, sidestep potential obstacles, and consequently enhance the efficiency of locating desired targets. In this chapter, we highlight the pivotal theoretical contributions that have enriched our understanding of random resetting within an abundance of stochastic processes, ranging from standard diffusion to its fractional counterpart. We also touch upon the general criteria required for resetting to improve the search process, particularly when distribution describing the time needed to reach the target is broader compared to a normal one. Building on this foundation, we delve into real-world applications where resetting optimizes the efficiency of reaching the desired outcome, spanning topics from home range search, ion transport to the intricate dynamics of income. Conclusively, the results presented in this chapter offer a cohesive perspective on the multifaceted influence of random resetting across diverse fields.
Spinors and Descartes' Theorem

Descartes' circle theorem relates the curvatures of four mutually externally tangent circles, three"petal"circles around the exterior of a central circle, forming a"$3$-flower"configuration. We generalise this theorem to the case of an"$n$-flower", consisting of $n$ tangent circles around the exterior of a central circle, and give an explicit equation satisfied by their curvatures. The proof uses a spinorial description of horospheres in hyperbolic geometry.
Network of observations on the atmospheric electrical parameters during geomagnetic storm on 5 April 2010

The effects of a geomagnetic storm on the variation of the atmospheric electric field over Maitri (70°45′S, 11°44′E), Dome C (75°06′S, 123°20′E), and Vostok (78°27′S, 106°52′E) Antarctic research stations are presented in this paper. For the first time, the paper reports the simultaneous observations of the atmospheric electric field/potential gradient (PG) over the three high‐latitude stations at the Southern Hemisphere, and its associated changes due to a substorm phenomenon. PG data obtained from these three stations under fair‐weather conditions on 5 April 2010 are analyzed. The duration of geomagnetic disturbance is classified into three intervals, which contains three consecutive substorms based on the magnetic records of the Maitri station. The substorm is directly related to an enhancement of the magnetospheric convective electric field at high latitude, generally controlled by the solar wind parameters. It is found that the variation in the amplitude of PG depends on the magnetic latitude during substorm onset. During the substorm expansion phase, when the convection cell is at overhead, PG is significantly enhanced due to the downward mapping of the ionospheric horizontal electric field. The present observation demonstrated the changes on PG due to the spatial extension of the convection cell from high latitudes up to middle latitudes.
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning

The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other methods for mitigating distributional shifts have made offline reinforcement learning more effective, the continuous action setting often necessitates various approximations for applying these techniques. Many of these challenges are greatly alleviated in discrete action settings, where offline RL constraints and regularizers can often be computed more precisely or even exactly. In this paper, we propose an adaptive scheme for action quantization. We use a VQ-VAE to learn state-conditioned action quantization, avoiding the exponential blowup that comes with na\"ive discretization of the action space. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme. We further validate our approach on a set of challenging long-horizon complex robotic manipulation tasks in the Robomimic environment, where our discretized offline RL algorithms are able to improve upon their continuous counterparts by 2-3x. Our project page is at https://saqrl.github. io/
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks

We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples to sub-networks with varying widths. Our experiments demonstrate that: (1) SHARCS outperforms or complements existing per-sample adaptive inference methods across various classification tasks in terms of accuracy vs. FLOPs; (2) SHARCS generalizes across different architectures and can be even applied to compressed and efficient transformer encoders to further improve their efficiency; (3) SHARCS can provide a 2 times inference speed up at an insignificant drop in accuracy.
Cuspidal cohomology classes for GL_n(Z)

We prove the existence of a cuspidal automorphic representation $\pi$ for $GL_{79}/\mathbf{Q}$ of level one and weight zero. We construct $\pi$ using symmetric power functoriality and a change of weight theorem, using Galois deformation theory. As a corollary, we construct the first known cuspidal cohomology classes in $H^*(GL_{n}(\mathbf{Z}),\mathbf{C})$ for any $n>1$.
Asymmetric association of rainfall and atmospheric circulation over East Asia with anomalous rainfall in the tropical western North Pacific in summer

Abstract It is well known that precipitation anomalies in the tropical western North Pacific (WNP) significantly affect circulation and rainfall in East Asia during summer. In this study, the authors further investigated this relationship, by examining the anomalies associated with positive and negative precipitation anomalies in the tropical WNP. The results suggest these anomalies are asymmetric between the enhanced and suppressed WNP rainfall. Positive precipitation anomalies over the WNP, in comparison with their negative counterparts, are more closely related to the meridional teleconnection pattern and rainfall anomalies along the East Asian rain belt. The implications of the results are discussed.
Neural prosthesis control restores near-normative neuromechanics in standing postural control

Current lower-limb prostheses do not provide active assistance in postural control tasks to maintain the user’s balance, particularly in situations of perturbation. In this study, we aimed to address this missing function by enabling neural control of robotic lower-limb prostheses. Specifically, electromyographic (EMG) signals (amplified neural control signals) recorded from antagonistic residual ankle muscles were used to drive a robotic prosthetic ankle directly and continuously. Participants with transtibial amputation were recruited and trained in using the EMG-driven robotic ankle. We studied how using the EMG-controlled ankle affected the participants’ anticipatory and compensatory postural control strategies and stability under expected perturbations compared with using their daily passive devices. We investigated the similarity of neuromuscular coordination (by analyzing motor modules) of the participants, using either device in a postural sway task, to that of able-bodied controls. Results showed that, compared with their passive prosthesis, the EMG-controlled prosthesis enabled participants to use near-normative postural control strategies, as evidenced by improved between-limb symmetry in intact-prosthetic center-of-pressure and joint angle excursions. Participants substantially improved postural stability, as evidenced by a reduction in steps or falls using the EMG-controlled prosthetic ankle. Furthermore, after relearning to use residual ankle muscles to drive the robotic ankle in postural control, nearly all participants’ motor module structure shifted toward that observed in individuals without limb amputations. Here, we have demonstrated the potential benefit of direct EMG control of robotic lower limb prostheses to restore normative postural control strategies (both neural and biomechanical) toward enhancing standing postural stability in amputee users. Description Neural control of a robotic ankle prosthesis enables improved stability and near-normative neuromechanics of postural control.
Distribution and Substrate Frequency of Teloschistes chrysophthalmus (L.) Th. Fr. in Iowa, USA

Abstract. A total of 464 Teloschistes chrysophthalmus (L.) Th. Fr. populations were collected, along with winter twigs from their woody substrates, in 56 of Iowa's 99 counties. This lichen is more abundant in western Iowa than in eastern Iowa and is least abundant in the northeast most portion of Iowa. T. chrysophthalmus was found on the bark of 31 distinct woody taxa, but was most frequently found on Gleditsia triacanthos.
Molecular and morphological screening of Podocotyle spp. (Trematoda: Opecoelidae) sheds light on their diversity in Northwest Pacific and eastern European Arctic

Abstract Podocotyle is a genus of marine opecoelid digeneans that parasitize a wide variety of fish as adults. We present the first phylogenetic analysis of several Podocotyle isolates using nuclear 28S rDNA and mitochondrial cox1 DNA regions. New sequences were obtained for Podocotyle specimens from fish caught in the Sea of Okhotsk and the White Sea. Based on morphological and molecular data, eight Podocotyle lineages of species rank were revealed. However, this diversity is poorly formalized within the current taxonomic model of the genus. As a result, we identified Podocotyle cf. angulata, Podocotyle cf. atomon, Podocotyle cf. reflexa, Podocotyle atomon of Sokolov et al., 2019, Podocotyle sp. of Denisova et al., 2023, Podocotyle sp. 1, Podocotyle sp. 2 and Podocotyle sp. 3. We also highlight the unresolved question of the life cycles of representatives of Podocotyle whose intramolluscan stages parasitize the intertidal snails Littorina spp.
Canine parasitoses in north Patagonia (Argentina): comparison between different social and environmental factors

Abstract In many countries, urban areas are commonly contaminated with canine feces that spread parasites, a situation related to the natural and social environment. Dogs having free access to public areas therefore constitutes a health risk. Because in Patagonia there are no records of city comparisons that consider the number of free-roaming dogs and the local environmental and socioeconomic characteristics, in this study we analyse and compare canine parasitoses in two cities of Rio Negro province, Argentina. Canine feces were collected from public areas of El Bolsón and Cipolletti cities and examined using enrichment methods, Kinyoun stain and immunoassay (Copro-Elisa). The total percentage of positive feces in El Bolsón was 68.95% and 16 parasite taxa being identified, whereas in Cipolletti the total positive feces was 41.1%, with 11 taxa. Both cities presented more helminth species than protozoans. The variables that explained the occurrence of all parasites were rainfall and socioeconomic stratum with the highest values being found in low-income areas. Soil moisture is conditioned by rainfall. This study highlights the presence of the zoonotic Echinococcus sp. in dogs in urban areas of the Rio Negro province. The occurrence of this parasite in Cipolletti is unexpected, given that this city is not considered an endemic zone for this disease, which is not included in local monitoring and control plans.
Introduction to A Compendium of Strategies to Prevent Healthcare-Associated Infections In Acute-Care Hospitals: 2022 Updates

Abstract Since the initial publication of A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals in 2008, the prevention of healthcare-associated infections (HAIs) has continued to be a national priority. Progress in healthcare epidemiology, infection prevention, antimicrobial stewardship, and implementation science research has led to improvements in our understanding of effective strategies for HAI prevention. Despite these advances, HAIs continue to affect ∼1 of every 31 hospitalized patients,1 leading to substantial morbidity, mortality, and excess healthcare expenditures,1 and persistent gaps remain between what is recommended and what is practiced. The widespread impact of the coronavirus disease 2019 (COVID-19) pandemic on HAI outcomes2 in acute-care hospitals has further highlighted the essential role of infection prevention programs and the critical importance of prioritizing efforts that can be sustained even in the face of resource requirements from COVID-19 and future infectious diseases crises.3 The Compendium: 2022 Updates document provides acute-care hospitals with up-to-date, practical expert guidance to assist in prioritizing and implementing HAI prevention efforts. It is the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Disease Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of organizations and societies with content expertise, including the Centers for Disease Control and Prevention (CDC), the Pediatric Infectious Disease Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), the Surgical Infection Society (SIS), and others.
Establishment of the Daucus carota SMC-1 Cell Suspension Line for Poliovirus Vaccine Development

Abstract The development of virus-free, oral vaccines against poliovirus capable of inducing mucosal protective immunity is needed to safely combat this pathogen. In the present study, a carrot cell line expressing the poliovirus VP2 antigen was established at the level of callus and cell suspensions, exploring the effects of culture media (MS and B5), supplementation with urea, phytoregulators (2,4-D : KIN), and light conditions (continuous light, photoperiod, and total darkness). The best callus growth was obtained on B5 medium supplemented with 2 mg/L of 2,4-D + 2 mg/L kinetin and 0.0136 g/L of urea and in continuous light conditions. Suspension cultures of the SMC-1 line in 250 mL Erlenmeyer flasks had a maximum growth of 16.07 ± 0.03 g/L DW on day 12 with a growth rate of µ=0.3/d and a doubling time of 2.3 days. In a 2 L airlift bioreactor, the biomass yield achieved was 25.6 ± 0.05 g/L DW at day 10 with a growth rate of µ= 0.58/d and doubling time of 1.38 d. Cell growth was 1.5 times higher in bioreactors than in shake flasks, highlighting that both systems resulted in the accumulation of VP2 throughout the time in culture. The maximum VP2 yield in flasks was 387.8 µg/g DW at day 21, while in the reactor it was 550.2 µg/g DW at day 18. In conclusion, bioreactor-based production of the VP2 protein by the SMC-1 suspension cell line offers a higher productivity when compared to flask cultures, offering a key perspective to produce low-cost vaccines against poliomyelitis.