Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow-based Autoregressive Structured Prediction of Human Motion

A new method is proposed for human motion predition by learning temporal and spatial dependencies in an end-to-end deep neural network. The joint connectivity is explicitly modeled using a novel autoregressive structured prediction representation based on flow-based generative models. We learn a latent space of complex body poses in consecutive frames which is conditioned on the high-dimensional structure input sequence. To construct each latent variable, the general and local smoothness of the joint positions are considered in a generative process using conditional normalizing flows. As a result, all frame-level and joint-level continuities in the sequence are preserved in the model. This enables us to parameterize the inter-frame and intra-frame relationships and joint connectivity for robust long-term predictions as well as short-term prediction. Our experiments on two challenging benchmark datasets of Human3.6M and AMASS demonstrate that our proposed method is able to effectively model the sequence information for motion prediction and outperform other techniques in 42 of the 48 total experiment scenarios to set a new state-of-the-art.
Spatial phase sensitivity for oscillators close to the saddle-node homoclinic bifurcation

The traditional phase sensitivity function (PSF) has manifested its efficacy in investigating synchronization behaviors for limit-cycle oscillators. However, some subtle details may be ignored when the phase value is accumulated in space or the perturbation is space-dependent. In this paper, we compared spatial PSF with the traditional PSF for oscillators close to the saddle-node homoclinic (SNH) bifurcation, also known as saddle-node on invariant circle (SNIC) bifurcation. It is found that the spatial phase sensitivity function could reveal the phase accumulation feature on the limit cycle. Moreover, it is proved that for any two-dimensional smooth dynamical system, type II phase response curve is the only possible type. Finally, the synchronization distributions of uncoupled SNH oscillator driven by common and independent noises are studied, which shows the spacedependent coupling function of common noise could have significant influences on the synchronization behavior.
The Burden of Being a Bridge: Understanding the Role of Multilingual Users during the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic triggers infodemic over online social networks. It is thus important for governments to ensure their official messages outpace misinformation and efficiently reach the public. Some countries and regions that are currently worst affected by the virus including Europe, South America and India, encounter an additional difficulty: multilingualism. Understanding the specific role of multilingual users in the process of information diffusion is critical to adjust their publishing strategies for the governments of such countries and regions. In this paper, we investigate the role of multilingual users in diffusing information during the COVID-19 pandemic on popular social networks. We collect a large-scale dataset of Twitter from a populated multilingual region from the beginning of the pandemic. With this dataset, we successfully show that multilingual users act as bridges in diffusing COVID-19 related information. We further study the mental health of multilingual users and show that being the bridges, multilingual users tend to be more negative. This is confirmed by a recent psychological study stating that excessive exposure to social media may result in a negative mood.
A Design Space Study for LISTA and Beyond

In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits the role of unrolling as a design approach for deep networks: to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough design space study for the unrolled models. Among all possible variations, we focus on extensively varying the connectivity patterns and neuron types, leading to a gigantic design space arising from LISTA. To efficiently explore this space and identify top performers, we leverage the emerging tool of neural architecture search (NAS). We carefully examine the searched top architectures in a number of settings, and are able to discover networks that are consistently better than LISTA. We further present more visualization and analysis to "open the black box", and find that the searched top architectures demonstrate highly consistent and potentially transferable patterns. We hope our study to spark more reflections and explorations on how to better mingle model-based optimization prior and data-driven learning.
Chinese Character Decomposition for Neural MT with Multi-Word Expressions

Chinese character decomposition has been used as a feature to enhance Machine Translation (MT) models, combining radicals into character and word level models. Recent work has investigated ideograph or stroke level embedding. However, questions remain about different decomposition levels of Chinese character representations, radical and strokes, best suited for MT. To investigate the impact of Chinese decomposition embedding in detail, i.e., radical, stroke, and intermediate levels, and how well these decompositions represent the meaning of the original character sequences, we carry out analysis with both automated and human evaluation of MT. Furthermore, we investigate if the combination of decomposed Multiword Expressions (MWEs) can enhance the model learning. MWE integration into MT has seen more than a decade of exploration. However, decomposed MWEs has not previously been explored.
Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots

Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling prey to bad local minima. We present a combined sampling and optimization-based planning approach that can cope with challenging terrain. The sampling-based stage computes whole-body configurations and contact schedule, which speeds up the optimization convergence. The optimization-based stage ensures that all the system constraints, such as non-holonomic rolling constraints, are satisfied. The evaluations show the importance of good initial guesses for optimization. Furthermore, they suggest that terrain/collision (avoidance) constraints are more challenging than the robot model’s constraints. Lastly, we extend the optimization to handle general terrain representations in the form of elevation maps.
On the Method of the Determination of the Global Hardness of Atoms and Molecules

In this chapter, the authors have discussed the time evolution of the concepts and various scales for the determination of the global hardness of atoms and molecules. The chapter also attempts to understand the physical basis and operational significance of the concept–global hardness. The physical hardness was introduced and theorized by condensed matter physicists. The chemical hardness was introduced by chemists to generalize and rationalize the HSAB principle. We have tried to establish that the physical hardness and the chemical hardness with evolution of time have converged to one and the same general principle—the hardness. Since, the hardness is a conceptual hypothesis only and not observable; there is no possibility of its quantum mechanical evaluation. Any attempt of modeling this abstract semiotic representation for the purpose of developing some mathematical algorithms and to convert it into theoretical quantities of cognitive representations, it is required that the hardness should be reified in terms of the physicochemical behavior of such conundrums goaded by the quantum mechanical principles. Some scales of measurement of hardness are introduced with the evolution of time. DOI: 10.4018/978-1-4666-1607-3.ch010
Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule

PARP14 is an interferon‐stimulated gene that is overexpressed in multiple tumor types, influencing pro‐tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN‐γ and IL‐4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono‐ADP‐ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono‐ADP‐ribose and poly‐ADP‐ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL‐4 driven pro‐tumor gene expression in macrophages, however it is not clear what roles the non‐enzymatic biomolecular recognition motifs play in PARP14‐driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD+‐binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.
Low plasma carnosinase activity promotes carnosinemia following

21 Objectives A polymorphism in the CNDP1 gene, resulting in decreased plasma carnosinase activity, 22 is associated with a reduced risk for diabetic nephropathy. Because carnosine, a natural 23 scavenger/suppressor of reactive oxygen species, AGE’s and reactive aldehydes, is readily degraded 24 in blood by the highly active carnosinase enzyme, it has been postulated that low serum carnosinase 25 activity might be advantageous to reduce diabetic complications. The aim of this study was to 26 examine whether a low carnosinase activity promotes circulating carnosine levels following 27 carnosine supplementation in humans. 28 Methods Blood and urine was sampled, in 25 healthy subjects following acute supplementation 29 with 60mg/kg BW carnosine. Pre-cooled EDTA tubes were used for blood withdrawal and plasma 30 samples were immediately deproteinized and analyzed for carnosine and beta-alanine by HPLC. 31 CNDP1 genotype, baseline plasma carnosinase activity and protein content were assessed. 32 Results Upon carnosine ingestion, 8 of the 25 subjects (= responders) displayed a measurable 33 increase in plasma carnosine up to 1h following supplementation. Subjects with no measurable 34 increment in plasma carnosine (= non-responders) had an approximately 2-fold higher plasma 35 carnosinase protein content and an approximately 1.5-fold higher activity compared to the 36 responders. The urinary carnosine recovery was 2.6-fold higher in the responders versus non37 responders and was negatively dependent on both the activity and protein content of the plasma 38 carnosinase enzyme. 39 Conclusion A low plasma carnosinase activity promotes the presence of circulating carnosine upon 40 an oral challenge. These data may further clarify the link between CNDP1 genotype, carnosinase 41 and diabetic nephropathy. 42
Demonstration Paper: Monitoring Machine Learning Contracts with QoA4ML

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.
Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads: Vision Paper

Distributed Database Management Systems~(DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance. We propose collecting real-world traces and then applying data generation techniques to synthesize similar realistic traces based on it. Based in this approach, we can obtain workloads for benchmarking, exhibit variability with respect to different aspects of interest while still being similar to the original traces. Varying generation parameters, we are able to support benchmarking what-if scenarios with hypothetical workloads and introduced anomalies.
A Systematic Review of Self-Compassion in Chronic Pain: From Correlation to Efficacy

Abstract Chronic pain (CP) is a common condition affecting millions of people worldwide. Compassion-related interventions are proving to be advantageous in CP, and self-compassion (SC) is hypothesized to be related with pain regulation physiological processes, as well as with psychological benefits in CP. We aimed to review scientific literature on: 1) Compassion-based psychological interventions and their changes in pain outcomes; and 2) associations between SC and pain-related outcomes. We performed a systematic research in four electronic databases: MEDLINE, EMBASE, PsycINFO and the Cochrane Library from inception until April 2020. In Question 1, we included studies involving adult patients with CP who participated in compassion-based psychological interventions. In Question 2, we included studies that examined the associations between SC and pain outcomes in adults with CP. We identified 16 studies. For Question 1, we included seven studies focused on different compassion-based interventions that assessed at least one pain outcome, in a total of 253 participants with CP associated with multiple conditions. For Question 2, we included nine studies, in a total of 1,430 participants, with eight different pain outcomes: Intensity, acceptance, catastrophizing, self-efficacy, disability, distress, pain related coping and anxiety. Considering the high heterogeneity between studies and the poor-quality assessment, we could not draw definitive conclusions on the efficacy of compassion-based interventions nor on the association between SC and pain outcomes. Studies are further discussed in detail. This review can be a starting point for large-scale and high-quality trials in this area as it provides an organized overview of the current literature on this topic.
Job Crafting Paths for Job Engagement: An Empirical Study among Chinese Social Workers

ABSTRACT Drawing on Kahn’s job engagement theory, Wrzesniewski and Dutton’s job crafting theory, and Kristof’s Person-Organization fit theory, this study proposed and tested a hypothetical path model in which social workers’ job engagement was shaped and promoted by job crafting through Person-Organization fit, psychological meaningfulness, psychological safety, and psychological availability. The statistical results on 194 paired responses from 16 social work organizations show that job crafting directly shaped and promoted job engagement, or indirectly, through Person-Organization fit, psychological meaningfulness, and psychological availability. The theoretical contributions of the study are discussed, as are the practical implications.
Eunuchs or Females? Causes and Consequences of Gynodioecy on Morphology, Ploidy, and Ecology of Stellaria graminea L. (Caryophyllaceae)

Plant speciation results from intricate processes such as polyploidization, reproductive strategy shifts and adaptation. These evolutionary processes often co-occur, blurring their respective contributions and interactions in the speciation continuum. Here, relying on a large-scale study, we tested whether gynodioecy triggers the divergent evolution of flower morphology and genome between sexes, and contributes to the establishment of polyploids and colonization of ecological niches in Stellaria graminea. We found that gynodioecy in S. graminea leads to flower morphology divergence between females and hermaphrodites, likely due to sexual selection. Contrary to our expectations, gynodioecy occurs evenly in diploids and tetraploids, suggesting that this reproductive strategy was not involved in the establishment of polyploids. Both diploid and tetraploid females have a larger genome size than hermaphrodites, suggesting the presence of sex chromosomes. Finally, ecology differs between cytotypes and to a lesser extent between sexes, suggesting that the link between environment and presence of females is indirect and likely explained by other aspects of the species’ life history. Our study shows that gynodioecy leads to the consistent evolution of sexual traits across a wide range of populations, cytotypes and environments within a given species, and this likely contributes to the phenotypic and genetic distinctiveness of the species from its sister clades.
Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.
The Impact of Water Sporting Events on Attitudes Toward Physical Activity: Motivational Profiles of Participants in Modern and Traditional Water Events

The aim of this paper was to analyze the relationship between attitudes toward physical activity and participation in water sports events and to recognize the main motives for involvement in these kinds of events. A written paper–pencil diagnostic survey was conducted among 394 participants in two traditional and two modern sports events on water held in Poland to ascertain whether innovative (modern) events are needed in society, and whether they cause an increase in interest in physical activity (more than traditional events). The research results showed that modern sports events on water did not have any more power to attract physically inactive people than traditional water events, did not produce a greater desire to lead an active lifestyle, and did not encourage people to more regularly practice water sports. Moreover, modern events, compared to traditional events, were only a one-time experience and the people who completed survey often wanted to return to traditional sports events which provided participants with greater positive emotions than a modern event. In the case of socio-demographical variables, there was no statistically significant relationship between gender and choice of modern or traditional event, but older people were more likely to choose a traditional event than younger people; modern water events were an attractive option primarily for young people under 30 years of age, furthermore, modern events more often attracted people who had completed higher education. However, it turned out that a modern event on water often attracted more people who had had no experience in this sport discipline rather than people who chose traditional water events. In a sense, modern events are therefore effective in promoting water sports in Polish society. We also distinguished five main groups of participants: healthy lifestyle managers, lovers of sports emotions, water sports malcontents, water sports enthusiasts, and neutrals to water sports. Additionally, we looked into gender-related motives for participation in modern and traditional water events: social and health-related motives proved to be more important for women and men who participated in modern water events. The research results presented in the article expand on the current state of knowledge about mass participation in sport, the impact of sporting events on the promotion of physical activity, and show the motivation behind participation in modern and traditional water sporting events.
A Comprehensive Review of Fluorescence Correlation Spectroscopy

Fluorescence correlation spectroscopy (FCS) is a powerful technique for quantification of molecular dynamics, and it has been widely applied in diverse fields, e.g., biomedicine, biophysics, and chemistry. By time-correlation of the fluorescence fluctuations induced by molecules diffusing through a focused light, FCS can quantitatively evaluate the concentration, diffusion coefficient, and interaction of the molecules in vitro or in vivo. In this review, the basic principle and implementation of FCS are introduced. Then, the advances of FCS variants are reviewed, covering dual-color FCCS, multi-focus FCS, pair correlation function (pCF), scanning FCS, focus-reduced FCS, SPIM-FCS, and inverse-FCS. Besides, the applications of FCS are demonstrated with the measurement of local concentration, hydrodynamic radius, diffusion coefficient, and the interaction of different molecules. Lastly, a discussion is given by summarizing the pros and cons of different FCS techniques, as well as the outlooks and perspectives of FCS.
Advances in Carbon-Based Microfiber Electrodes for Neural Interfacing

Neural interfacing devices using penetrating microelectrode arrays have emerged as an important tool in both neuroscience research and medical applications. These implantable microelectrode arrays enable communication between man-made devices and the nervous system by detecting and/or evoking neuronal activities. Recent years have seen rapid development of electrodes fabricated using flexible, ultrathin carbon-based microfibers. Compared to electrodes fabricated using rigid materials and larger cross-sections, these microfiber electrodes have been shown to reduce foreign body responses after implantation, with improved signal-to-noise ratio for neural recording and enhanced resolution for neural stimulation. Here, we review recent progress of carbon-based microfiber electrodes in terms of material composition and fabrication technology. The remaining challenges and future directions for development of these arrays will also be discussed. Overall, these microfiber electrodes are expected to improve the longevity and reliability of neural interfacing devices.