Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Variational Autoencoder

Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies. A common approach to analyse high-dimensional data that contains missing values is to learn a low-dimensional representation using variational autoencoders (VAEs). However, standard VAEs assume that the learnt representations are i.i. d. , and fail to capture the correlations between the data samples. We propose the Longitudinal VAE (L-VAE), that uses a multi-output additive Gaussian process (GP) prior to extend the VAE's capability to learn structured low-dimensional representations imposed by auxiliary covariate information, and derive a new KL divergence upper bound for such GPs. Our approach can simultaneously accommodate both time-varying shared and random effects, produce structured low-dimensional representations, disentangle effects of individual covariates or their interactions, and achieve highly accurate predictive performance. We compare our model against previous methods on synthetic as well as clinical datasets, and demonstrate the state-of-the-art performance in data imputation, reconstruction, and long-term prediction tasks.
Typing of Salmonella enterica serovar Infantis isolates from 51 outbreaks in Germany between 1974 and 2009 by a novel phage-typing scheme

SUMMARY We developed a new phage-typing method and evaluated its application in combination with XbaI macrorestriction analysis by pulsed-field gel electrophoresis (PFGE) as a useful tool for the long-term epidemiology of Salmonella enterica serovar Infantis. In this study, we investigated 1008 S. Infantis isolates recovered from humans, various animal species and food products from 1973 to 2009. The typing scheme is based on 17 typing phages, defining 61 different patterns within the strain collection. The experiments showed that phage typing is a reliable method for differentiation of outbreaks and sporadic clinical cases as well as for elucidation of chains of transmission. The combined analysis of phage typing and PFGE revealed the existence of epidemic clones with a high stability over time like PT29/XB27 which was identified in nosocomial salmonellosis, community outbreaks as well as in broiler chickens from 2002 to 2009.
A novel mutation in JARID1C/SMCX in a patient with autism spectrum disorder (ASD)

We describe a nondysmorphic patient with developmental delay and autism spectrum disorder who has a missense mutation in the Jumonji AT‐rich interactive domain 1C (JARID1C) gene. This child first presented at 30 months of age with stereotyped and repetitive behaviors, impairment in social reciprocity and in the use of multiple nonverbal behaviors, and developmental delay primarily in the language domain. A diagnosis of autism was made and subsequently confirmed at the current age of 47 months. Cytogenetic and fragile X studies were normal. Mutational analysis revealed a novel missense mutation in exon 16 of the JARID1C gene that results in an arginine to tryptophan substitution at amino acid 766 (R766W). Sequence alignment analysis with multiple available eukaryotic sequences including the homologous proteins of mouse and zebrafish demonstrated that the affected amino acid is conserved. JARID1C has not previously been implicated in autism susceptibility. Recent novel molecular evidence suggests that it is a histone demethylase specific for di‐ and trimethylated histone 3 lysine 4 (H3K4) and functions as a transcriptional repressor by fostering REST‐mediated neuronal gene regulation. The JARID1C‐regulated genes SCN2A, CACNA1H, BDNF, and SLC18A1 have previously been associated with autism and cognitive dysfunction. This patient brings the total number of reported JARID1C mutations to 14. This presentation both extends the range of neurocognitive phenotypes attributable to mutations in this gene and illustrates the importance of molecular studies and DNA sequence analysis for accurate diagnosis of monogenic causes of autism. © 2008 Wiley‐Liss, Inc.
The Dempster-Shafer Theory

The initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered through distributions of random sets. More furtive comparison has been with the traditional Bayesian theory, where D-S theory has been considered a generalisation of it (Schubert, 1994). Cobb and Shenoy (2003) direct its attention to the comparison of D-S theory and the Bayesian formulisation. Their conclusions are that they have the same expressive power, but that one technique cannot simply take the role of the other. The association with artificial intelligence (AI) is clearly outlined in Smets (1990), who at the time, acknowledged the AI community has started to show interest for what they call the Dempster-Shafer model. It is of interest that even then, they highlight that there is confusion on what type of version of D-S theory is considered. D-S theory was employed in an event driven integration reasoning scheme in Xia et al. (1997), associated with automated route planning, which they view as a very important branch in applications of AI. Liu (1999) investigated Gaussian belief functions and specifically considered their proposed computation scheme and its potential usage in AI and statistics. Huang and Lees (2005) apply a D-S theory model in natural-resource classification, comparing with it with two other AI models. Wadsworth and Hall (2007) considered D-S theory in a combination with other techniques to investigate site-specific critical loads for conservation agencies. Pertinently, they outline its positioning with respect to AI (p. 400); The approach was developed in the AI (artificial intelligence) community in an attempt to develop systems that could reason in a more human manner and particularly the ability of human experts to “diagnose” situations with limited information. This statement is pertinent here, since emphasis within the examples later given is more towards the general human decision making problem and the handling of ignorance in AI. Dempster and Kong (1988) investigated how D-S theory fits in with being an artificial analogy for human reasoning under uncertainty. An example problem is considered, the murder of Mr. White, where witness evidence is used to classify the belief in the identification of an assassin from considered suspects. The numerical analyses presented exposit a role played by D-S theory, including the different ways it can act on incomplete knowledge.
Lipopolysaccharide-Induced Activation of β2-Integrin Function in Macrophages Requires Irak Kinase Activity, p38 Mitogen- Activated Protein Kinase, and the Rap1 GTPase

ABSTRACT Lipopolysaccharide (LPS), a component of the outer membrane of gram-negative bacteria, is a potent activator of macrophages. Besides inducing many transcriptional pathways, LPS also elicits rapid morphological changes such as cell spreading. Here we have investigated the signaling pathway that controls macrophage β2-integrin-dependent spreading in response to LPS. We show that inhibition of the adapter protein MyD88, the interleukin-1 receptor-associated kinase Irak, the p38 mitogen-activated protein kinase, or the Ras-like GTPase Rap1 blocks LPS-induced spreading. In addition, Irak activates p38 and stimulates p38-dependent spreading. The activation of p38 by Irak requires Irak's kinase activity. We find that p38 controls spreading independently of its role in transcription but rather through activation of Rap1. Together, our results suggest that β2-integrin-dependent spreading of macrophages in response to LPS is controlled by a linear signaling pathway via MyD88, Irak, p38, and Rap1.
Error resilient dual frame motion compensation for mobile communication

In dual frame motion compensation (DFMC), one short-term reference frame and one long-term reference frame are utilized for motion compensation. This scheme can be utilized to do error resilience, and there have been a number of DFMC based error resilience in the literature. In the paper, an error resilient jump update DFMC (JU-DFMC) with adaptive unequal error protection is proposed. In the proposed error resilient JU-DFMC, a new error resilient prediction structure is firstly presented, in which for different packet loss rates, the reference frames are adaptively adjusted to reduce error propagation. Then an end-to-end distortion model considering data partition is derived and applied to macroblock (MB) level mode decision. Finally an adaptive unequal error protection is proposed, in which a frame level rate distortion cost scheme is used to determine how many times of the header information will be transmitted in a long-term reference frame. Experimental results show that the proposed method can achieve better performance than previous schemes.
Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings

To enable more natural face-to-face interactions, conversational agents need to adapt their behavior to their interlocutors. One key aspect of this is generation of appropriate non-verbal behavior for the agent, for example facial gestures, here defined as facial expressions and head movements. Most existing gesture-generating systems do not utilize multi-modal cues from the interlocutor when synthesizing non-verbal behavior. Those that do, typically use deterministic methods that risk producing repetitive and non-vivid motions. In this paper, we introduce a probabilistic method to synthesize interlocutor-aware facial gestures - represented by highly expressive FLAME parameters - in dyadic conversations. Our contributions are: a) a method for feature extraction from multi-party video and speech recordings, resulting in a representation that allows for independent control and manipulation of expression and speech articulation in a 3D avatar; b) an extension to MoGlow, a recent motion-synthesis method based on normalizing flows, to also take multi-modal signals from the interlocutor as input and subsequently output interlocutor-aware facial gestures; and c) a subjective evaluation assessing the use and relative importance of the different modalities in the synthesized output. The results show that the model successfully leverages the input from the interlocutor to generate more appropriate behavior. Videos, data, and code are available at: https://jonepatr.github. io/lets_face_it/.
Dissolving Decision Making? Models and Their Roles in Decision-Making Processes and Policy at Large

Argument This article studies the roles three science-based models play in Dutch policy and decision making processes. Key is the interaction between model construction and environment. Their political and scientific environments form contexts that shape the roles of models in policy decision making. Attention is paid to three aspects of the wider context of the models: a) the history of the construction process; b) (changes in) the political and scientific environments; and c) the use in policy processes over longer periods of time. Models are more successfully used when they are constructed in a stable political and scientific environment. Stability and certainty within a scientific field seems to be a key predictor for the usefulness of models for policy making. The economic model is more disputed than the ecology-based model and the model that has its theoretical foundation in physics and chemistry. The roles models play in policy processes are too complex to be considered as straightforward technocratic powers.
Solving Constrained CASH Problems with ADMM

The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available. However, CASH solvers do not directly handle black-box constraints such as fairness, robustness or other domain-specific custom constraints. We present our recent approach [Liu, et al., 2020] that leverages the ADMM optimization framework to decompose CASH into multiple small problems and demonstrate how ADMM facilitates incorporation of black-box constraints.
Exploiting Review Neighbors for Contextualized Helpfulness Prediction

Helpfulness prediction techniques have been widely used to identify and recommend high-quality online reviews to customers. Currently, the vast majority of studies assume that a review's helpfulness is self-contained. In practice, however, customers hardly process reviews independently given the sequential nature. The perceived helpfulness of a review is likely to be affected by its sequential neighbors (i.e., context), which has been largely ignored. This paper proposes a new methodology to capture the missing interaction between reviews and their neighbors. The first end-to-end neural architecture is developed for neighbor-aware helpfulness prediction (NAP). For each review, NAP allows for three types of neighbor selection: its preceding, following, and surrounding neighbors. Four weighting schemes are designed to learn context clues from the selected neighbors. A review is then contextualized into the learned clues for neighbor-aware helpfulness prediction. NAP is evaluated on six domains of real-world online reviews against a series of state-of-the-art baselines. Extensive experiments confirm the effectiveness of NAP and the influence of sequential neighbors on a current reviews. Further hyperparameter analysis reveals three main findings. (1) On average, eight neighbors treated with uneven importance are engaged for context construction. (2) The benefit of neighbor-aware prediction mainly results from closer neighbors. (3) Equally considering up to five closest neighbors of a review can usually produce a weaker but tolerable prediction result.
Semantic Visual Navigation by Watching YouTube Videos

Semantic cues and statistical regularities in real-world environment layouts can improve efficiency for navigation in novel environments. This paper learns and leverages such semantic cues for navigating to objects of interest in novel environments, by simply watching YouTube videos. This is challenging because YouTube videos don't come with labels for actions or goals, and may not even showcase optimal behavior. Our proposed method tackles these challenges through the use of Q-learning on pseudo-labeled transition quadruples (image, action, next image, reward). We show that such off-policy Q-learning from passive data is able to learn meaningful semantic cues for navigation. These cues, when used in a hierarchical navigation policy, lead to improved efficiency at the ObjectGoal task in visually realistic simulations. We improve upon end-to-end RL methods by 66%, while using 250x fewer interactions. Code, data, and models will be made available.
Dietary T rans Fatty Acids Intake and its Relation to Dyslipidemia in a Sample of Adults in Depok City , West Java , Indonesia

Introduction: The Basic Health Research of the Ministry of Health Indonesia in 2008 reported that the single most important cause of death was stroke, in both urban and rural populations. The risk factors underlying the cause of death are associated with hypertension, obesity and dyslipidemia. The purpose of this study was to determine the mean intake of trans fatty acids and its relation to dyslipidemia in a sample of Indonesian adults. Methods: A cross-sectional study was conducted on a total of 180 adult male and female respondents aged 35-60 years living in rural and urban areas of Depok city, West Java. Dietary intake was assessed by means of 24-hour recall and semi-quantitative FFQ. Results: The mean intake of trans fatty acids was 0.48% of total calories (urban 0.40% and rural 0.55%). The prevalence of dyslipidemia in the rural and urban subjects were 61.1% and 66.7%, respectively. There was a statistically significant relationship between trans fatty acids intake and hypercholesterolemia and hypertriglyceridemia. Conclusion: The intake of trans fatty acid among the Indonesian adults studied was half the recommended level. The high prevalence of dyslipidemia found indicates the need for intervention to reduce the rising incidence of cardiovascular diseases in Indonesia.
Bullying in Flemish schools: an evaluation of anti-bullying intervention in primary and secondary schools.

BACKGROUND The positive of the Norwegian anti-bullying programme (Olweus, 1992) stimulated other countries to tackle bully/victim problems. However, outcome studies found rather low levels of effect or even inconsistent results. AIMS The purpose of the present study was to evaluate behavioural effectiveness of a school-based anti-bullying approach within Flemish schools. In addition, specific attention was given to the relationship between outcome findings and external support. SAMPLE A total of 18 schools, comprising 1104 primary and secondary school children were recruited for this study. Students ranged in age from 10 to 16 years. METHOD For this study, an experimental pre-test/post-test design was used which included a control group. Three groups were established. The first group, Treatment with Support, involved students from schools that implemented a school-based anti-bullying intervention with additional support from the research group. The second group, Treatment without Support, also involved students from schools that implemented a school-based anti-bullying programme. However, in contrast with those falling under the first condition, this group of schools could not appeal to the research group for additional help. The last group involved students from schools that did not implement the anti-bullying programme and served as a Control condition. Repeated measures analyses of variance were carried out. RESULTS The findings regarding the effects of the school-based anti-bullying intervention programme on the extent of bullying and victimisation showed a mixed pattern of positive changes in primary schools and zero outcomes in secondary schools. The findings regarding the effects of external support revealed limited outcomes. CONCLUSIONS The outcomes of the evaluation study confirm that a school-based anti-bullying intervention strategy can be effective in reducing problems with bullying, especially within primary schools. It was argued that the developmental characteristics of secondary school students interfered with the programme outcomes. In addition, the findings revealed no extra effects of specific training sessions.
Note on Thermalization of Non-resonantly Produced Sterile Neutrinos.

Using an analytic treatment, we discuss the parameter regions of large active-sterile neutrino mixing angles where sterile neutrinos produced in non-resonant flavour oscillations can approach thermalization in several cosmologies. We show that thermalization affects only large active-sterile neutrino mixing already rejected by different limits. Hence, the allowed sterile neutrino parameter regions are unaffected.
Isometric Graph Neural Networks

Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph. Geometric techniques to extract such representations have poor scaling over large graph size, and recent advances in Graph Neural Network (GNN) algorithms have limited ability to reflect graph distance information beyond the first degree neighborhood. To enable this highly desired capability, we propose a technique to learn Isometric Graph Neural Networks (IGNN), which requires changing the input representation space and loss function to enable any GNN algorithm to generate representations that reflect distances between nodes. We experiment with the isometric technique on several GNN architectures for modeling multiple prediction tasks on multiple datasets. In addition to an improvement in AUC-ROC as high as $43\%$ in these experiments, we observe a consistent and substantial improvement as high as 400% in Kendall's Tau (KT), a measure that directly reflects distance information, demonstrating that the learned embeddings do account for graph distances.
Association between ERCC 1 rs 3212986 and ERCC 2 rs 13181 gene polymorphisms in NER pathway and the risk of bladder cancer in a Chinese population

We conducted a case-control study to investigate the association of ERCC1 (rs3212986) and ERCC2 (rs13181) gene polymorphisms with the susceptibility to bladder cancer. A total of 194 bladder cancer patients and 240 control subjects were recruited from the Zhumadian City Center Hospital between March 2012 and March 2014. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was taken to genotype ERCC1 rs3212986 and ERCC2 rs13181. A significant difference was found between the genotype distributions of ERCC1 rs3212986 between the two groups by chi-square test (χ2 = 6.01, P = 0.04). Unconditional logistic regression analyses showed that subjects carrying the CC genotype of ERCC1 rs3212986 were associated with a higher risk of developing bladder cancer than those carrying the AA genotype, and the adjusted OR (95% CI) was 2.05 (1.10-3.83) (P = 0.01). Moreover, we found that the CC genotype of ERCC1 rs3212986 had 1.8 fold risk of bladder cancer compared to the AA+AC genotype (OR = 1.80, 95% CI = 1.01-3.21; P = 0.03). However, we did not find any significant association between the ERCC2 rs13181 polymorphism and susceptibility to bladder cancer. In conclusion, we suggest that the ERCC1 rs3212986 gene polymorphism influences the development of bladder cancer in co-dominant and recessive models.
Nash Equilibrium Problems of Polynomials

This paper studies Nash equilibrium problems that are given by polynomial functions. We formulate efficient polynomial optimization problems for computing Nash equilibria. The Moment-sum-of-squares relaxations are used to solve them. Under generic assumptions, the method can find a Nash equilibrium, if there is one. Moreover, it can find all Nash equilibria if there are finitely many ones of them. The method can also detect nonexistence if there is no Nash equilibrium. Funding: J. Nie was supported by the National Science Foundation [Grant DMS-2110780].
Prediction of NOx emissions throughflame radical imaging and neural network based soft computing

The characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. This paper presents an algorithm for the prediction of NOx (NO and NO2) emissions in flue gas through flame radical imaging, flame temperature monitoring and application of Neural Network techniques. Radiation images of flame radicals OH*, CN*, CH* and C2* are captured using an intensified multi-wavelength imaging system. Flame temperature is determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values of the flame radicals are extracted. These characteristic values, together with the flame temperature, are then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig have shown the effectiveness of the proposed method for the prediction of NOx emissions.