Miracle-dz/UniFAR-FTUs huggingface dataset:
TD(0)-Replay: An Efficient Model-Free Planning with full Replay

In this paper we present a novel reinforcement learning method that allows for full replay of all past experience in every step of a reinforcement learning agent life with reasonable overhead. In particular, we show how to deduce an efficient equivalent backward view by replaying the full past experience online using TD(0) error for a linear model. We call the resultant methods TD(0)-Replay and Sarsa(0)-Rep1an, respectively. We emphasise the already established link between replaying and planning in our algorithm design by comparing it with an Extensive Dyna Planning algorithm, where we show that our method can outperform this expensive form of planning methods. We test the new methods on two different domain problems; Random Walk to test TD(0)-Replay prediction capabilities and Dyna Maze to test Sarsa(0)-Rep1an planning capabilities where we show that our algorithms dominate other replaying and planning methods.
Community engagement to pilot electronic patient-reported outcomes (e-PROs) in early intervention: Lessons learned

Background Electronic data capture is essential to advancing family-centered coordinated care in early intervention (EI). The purpose of this paper is to report on EI service coordinator response to piloting an electronic parent-reported outcome (e-PRO) assessment as part of their routine workflow, including lessons learned that may inform future phases of e-PRO implementation. Methods This second pilot study involved families enrolled in a large EI program (n=1040 families) in concert with their implementation of a statewide quality improvement initiative for care plan development and outcomes reporting. A total of 22 EI service coordinators and supervisors were engaged in 3 phases: initial e-PRO intervention, peer-mentor enhancement, and standard recruitment protocol. Results Implementation of the e-PRO intervention and peer-mentoring enhancement yielded low enrollment rates over the first 6 months (n=17). A standard recruitment protocol has resulted in enrollment growth (n=83) towards the targeted enrollment rate (n=832). Conclusions This study reports on early insights for building and sustaining a productive academic-community partnership for e-PRO implementation to support family-centered coordinated care. Lessons learned from this academic-community partnership with respect to strategies for enhancing community significance, collaboration, return, and control are discussed as they inform further development of this intervention before scale-up.
Enumeration of subdifferentials of piecewise linear functions with abs-normal form

The directional derivatives of a piecewise smooth function at a given point can be computed with Griewank's absolute normal form (ANF). When the given point is a non-differentiable point, the resulting derivative by ordinary algorithmic differentiation is included in the subdifferential. In this paper, with ANF, a method for computing and enumerating the elements of the limiting subdifferential at a given non-differential point is described with branch and bound search. Using such an enumeration, we can compute the values of limiting derivatives and, if required, we can check the first-order optimality of the piecewise linear function derived by the piecewise smooth function given by the form of the evaluation procedure, when the sophisticated algorithm for checking the optimalities is known. The worst-case complexity of the proposed algorithm is exponential in general, but we show that there may be some cases in which computational work may be reduced using the branch and bound search with numerical examples.
Investigation of the population dynamics within a Pseudomonas aeruginosa biofilm using a flow based biofilm model system and flow cytometric evaluation of cellular physiology

Abstract In this study a flow based biofilm model system was used to simulate the formation of Pseudomonas aeruginosa biofilms on a stainless steel surface. To investigate the complexity of biofilm-associated P. aeruginosa populations a combination of microscopic observations and flow cytometric analysis (FCM) was adopted. Biofilm-associated P. aeruginosa cells were evaluated (1) under optimal vs reduced nutrient-availability at the initial adhesion stage, and (2) irrespective of nutrient-availability within a mature biofilm. Microscopic estimation of the extent of attachment revealed more effective colonization upon optimal vs starvation conditions. FCM allowed an in situ evaluation of P. aeruginosa vitality, using cellular redox potential measurements to discriminate active, mid-active and non-active sub-populations. Samples from recently attached cells and mature biofilms showed significant differences in the percentages of bacterial cells from the defined sub-populations. The approach demonstrated that distribution of individual P. aeruginosa sub-populations was influenced by the stage of the biofilm life-cycle and nutrient availability.
Orca: Differential Bug Localization in Large-Scale Services

Today, we depend on numerous large-scale services for basic operations such as email. These services are complex and extremely dynamic as developers continuously commit code and introduce new features, fixes and, consequently, new bugs. Hundreds of commits may enter deployment simultaneously. Therefore one of the most time-critical, yet complex tasks towards mitigating service disruption is to localize the bug to the right commit. This paper presents the concept of differential bug localization that uses a combination of differential code analysis and software provenance tracking to effectively pin-point buggy commits. We have built Orca, a customized code search-engine that implements differential bug localization. On-Call Engineers (OCEs) of Orion, a large enterprise email and collaboration service, use Orca to localize bugs to the appropriate buggy commits. Our evaluation shows that Orca correctly localizes 77% of bugs for which it has been used. We also show that it causes a 3x reduction in the work done by the OCE.
Uncivil and personal? Comparing patterns of incivility in comments on the Facebook pages of news outlets

Social media and its embedded user commentary are playing increasingly influential roles in the news process. However, researchers’ understanding of the social media commenting environment remains limited, despite rising concerns over uncivil comments. Accordingly, this study used a supervised machine learning–based method of content analysis to examine the extent and patterns of incivility in the comment sections of 42 US news outlets’ Facebook pages over an 18-month period in 2015–2016. These outlets were selected as being broadly representative of national, local, conservative, and liberal-news media. The findings provide the first empirical evidence that both the level and the targets of incivility in the comments posted on news outlets’ Facebook pages vary greatly according to such entities’ general type and ideological stance.
Context-aware Advertisment Recommendation on Twitter through Rough sets

The main, if not the only, income for social networks is from advertising. Social media platforms like Twitter have become a main stream communication medium to disseminate information and capture the interest of potential customers. So, it is crucial that the policy implemented to decide which ads to show in proximity of which user’s posts, is the most profitable one: the ads shown should be as much as possible targeted to the user’s interests. In this paper, we propose a context-aware advertising recommendation system that, analyzing the users’ tweets during the timeline, interpretes the personal interests of users through orthopairs (they are equivalent to rough sets) to meet ads and users’ interests at the right time.
Impaired Executive Functioning of Sexual Assault Survivors with Acute Stress Disorder

This study aimed to examine the executive functioning of survivors exposed to recent sexual assaults. Twenty-seven female rape survivors who met the criterion for acute stress disorder (ASD) were enrolled and completed the assessment within 4 weeks after the traumatic experience. Additionally, 25 age-matched female health controls (HC) never exposed to such a traumatic event were enrolled. The assessments evaluated psychiatric symptoms including post-traumatic stress symptoms, depression, anxiety, and trait and state anger; general intelligence indexed by intellectual quotient (IQ); and executive functioning including set-shift/attention, planning, spatial working memory, and inhibition using the Cambridge Neuropsychological Test Automated Battery. The ASD group showed higher levels of depression, anxiety, and state anger, and lower IQ than the HC group. The ASD group also showed global impairment of executive functioning for set-shifting, attention, planning, and response inhibition compared to the HC group. Rather than being associated with low IQ and education levels, these results could be from trauma-related effects in survivors with ASD. Additionally, the state anger level was related to survivors’ deficient executive functioning. The findings indicate the importance of providing assessment and intervention efforts to sexual assault survivors soon after the trauma occurs.
Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation

Domain adaptation can transfer knowledge from the source domain to improve pattern recognition accuracy in the target domain. However, it is rarely discussed when the target domain is unlabeled and heterogeneous with the source domain, which is a very challenging problem in the domain adaptation field. This paper presents a new feature reconstruction method: unconstrained fuzzy feature fusion. Through the reconstructed features of a source and a target domain, a geodesic flow kernel is applied to transfer knowledge between them. Furthermore, the original information of the target domain is also preserved when reconstructing the features of the two domains. Compared to the previous work, this work has two advantages: 1) the sum of the memberships of the original features to fuzzy features no longer must be one, and 2) the original information of the target domain is persevered. As a result of these advantages, this work delivers a better performance than previous studies using two public datasets.
Interval Type-2 Fuzzy weighted Extreme Learning Machine for GDP Prediction

The CO2 emission due to industrialization is a crucial parameter which is directly proportional to the economic growth of any country/nation. However, for the non-accessible nations and war-torn nations with highly unreliable or insufficient macroeconomic data, the prediction of gross domestic product (GDP) is a challenging task. Thus, in this paper, we have proposed a novel approach for the reliable GDP estimation utilizing only the CO2 emission data. For this purpose, transfer learning (TL) is applied which learns on the previously acquired information and solve the new task. The training is performed on the GDP data of the developed nations and then prediction is estimated for the developing nations. This is implemented using kernel extreme learning machine (KELM) in which the output weights are modelled using interval type-2 fuzzy sets (IT2 Fss) for the effective transferal of knowledge from developed to developing nation. Experimental results have shown that the proposed IT2F-KELM provide much-improved RMSE as compared with the traditional KELM.
Scientific Advisory Committees at the World Health Organization: A Qualitative Study of How Their Design Affects Quality, Relevance, and Legitimacy

Governments and international organizations frequently convene scientific advisory committees (SACs) to support decision‐making with scientific advice. In this study, thematic analysis of interviews with 35 senior WHO staff identified five main themes characterizing WHO's experience with designing SACs to ensure quality, relevance, and legitimacy of scientific advice. First, in addition to technical matters, SACs are established to serve broader strategic objectives, including consensus building to promote high‐level political messages. Second, for SACs to be fully independent, they must have autonomy from the institutions convening or funding them, from the institutions from where SAC members are recruited, and from the institutions to whom the advice is directed. Third, since choices affecting quality, relevance, and legitimacy are closely linked, designing SACs often require trade‐offs among these three attributes. Fourth, staff supporting SACs need to balance between safeguarding SACs from external influence and being receptive to the external political environment. Fifth, the design of SACs need to balance the involvement of stakeholders with the power to act on recommendations against the need to protect the independence and integrity of the scientific process. Overall, this study highlights key choices conveners of SACs must make when seeking to promote quality, relevance, and legitimacy of scientific advice.
Deep Learning for Illumination Invariant Facial Expression Recognition

In this work we propose a novel method to address illumination invariance for racial expression recognition. We propose a Deep Convolutional Network (CNN) pre-trained as a Deep Stacked Convolutional Autoencoder (SCAE) in a greedy layer-wise unsupervised fashion. The SCAE model learns to encode racial expression images and produce a feature vector with relatively similar illumination, regardless of the luminance level of the input image. Moreover, we propose fine-tuning the stacked shallow autoencoders after each one of these is trained greedily, rather than just at the end, and show that this approach significantly improves the set of illumination invariant features learnt by the SCAE. Finally, we propose the use of a variant rectifier linear unit transfer function that helps the SCAE model reduce or increase the illumination of images with high or low luminance, and show that the lower and upper bounds greatly influence classification performance. The method proposed provides an increase in classification accuracy of 4% on the KDEF dataset and 8% on the CK+ dataset.
Control Strategies for Reducing VOCs Emission Process Based on Empirical Data

As for the industrial area, the control of VOCs emission has attracted a lot of attentions of researchers for several decades. Appropriate control strategies are fundamental tools to restrain the emission of VOCs. Based on empirical data extracted from the complete set of sources, control strategies for reducing VOCs emission process are proposed in this paper, which are obtained by performing numerical analyses. Empirical data are applied into the proof of theoretical results. Complete sources including industrial source, traffic source, living source and agricultural source are considered. The impacts of output value of general equipment in the industrial source, passenger throughput in traffic source, the size of population in living source and agricultural source on VOCs emissions are discussed. Additionally, control strategies for VOCs emissions are summarized by analyzing extremum of characteristic values obtained from sample sets. Finally, the features of various VOCs emission sources are concluded to guide the adjustment of treatment process, especially the treatment equipment of VOCs.
DNA methylation of FKBP5 and response to exposure‐based psychological therapy

Differential DNA methylation of the hypothalamic‐pituitary‐adrenal axis related gene FKBP5 has recently been shown to be associated with varying response to environmental influences and may play a role in how well people respond to psychological treatments. Participants (n = 111) received exposure‐based cognitive behavioural therapy (CBT) for agoraphobia with or without panic disorder, or specific phobias. Percentage DNA methylation levels were measured for the promoter region and intron 7 of FKBP5. The association between percentage reduction in clinical severity and change in DNA methylation was tested using linear mixed models. The effect of genotype (rs1360780) was tested by the inclusion of an interaction term. The association between change in DNA methylation and FKBP5 expression was examined. Change in percentage DNA methylation at one CpG site of intron 7 was associated with percentage reduction in severity (β = −4.26, p = 3.90 × 10−4), where a decrease in DNA methylation was associated with greater response to therapy. An interaction was detected between rs1360780 and changes in DNA methylation in the promoter region of FKBP5 on treatment outcome (p = .045) but did not survive correction for multiple testing. Changes in DNA methylation were not associated with FKBP5 expression. Decreasing DNA methylation at one CpG site of intron 7 of FKBP5 was strongly associated with decreasing anxiety severity following exposure‐based CBT. In addition, there was suggestive evidence that allele‐specific methylation at the promoter region may also be associated with treatment response. The results of this study add to the growing literature demonstrating the role of biological processes such as DNA methylation in response to environmental influences.
Confused Connections? Targeting White Matter to Address Treatment Resistant Schizophrenia

Despite development of comprehensive approaches to treat schizophrenia and other psychotic disorders and improve outcomes, there remains a proportion (approximately one-third) of patients who are treatment resistant and will not have remission of psychotic symptoms despite adequate trials of pharmacotherapy. This level of treatment response is stable across all stages of the spectrum of psychotic disorders, including early phase psychosis and chronic schizophrenia. Our current pharmacotherapies are beneficial in decreasing positive symptomology in most cases, however, with little to no impact on negative or cognitive symptoms. Not all individuals with treatment resistant psychosis unfortunately, even benefit from the potential pharmacological reductions in positive symptoms. The existing pharmacotherapy for psychosis is targeted at neurotransmitter receptors. The current first and second generation antipsychotic medications all act on dopamine type 2 receptors with the second generation drugs also interacting significantly with serotonin type 1 and 2 receptors, and with varying pharmacodynamic profiles overall. This focus on developing dopaminergic/serotonergic antipsychotics, while beneficial, has not reduced the proportion of patients experiencing treatment resistance to date. Another pharmacological approach is imperative to address treatment resistance both for response overall and for negative symptoms in particular. There is research suggesting that changes in white matter integrity occur in schizophrenia and these may be more associated with cognition and even negative symptomology. Here we review the evidence that white matter abnormalities in the brain may be contributing to the symptomology of psychotic disorders. Additionally, we propose that white matter may be a viable pharmacological target for pharmacoresistant schizophrenia and discuss current treatments in development for schizophrenia that target white matter.
A Multi-resolution Approach for Color Correction of Textured Meshes

Mesh texturing is an essential part of 3D scene reconstruction that enables a more realistic perception than the geometry alone and even compensates for inaccurate geometry. In this work we present a flexible formulation for color correction of textured scenes based on color augmentation per face. It can be employed as a post-processing step after selecting the best keyframe per face to compensate for color differences between pairs of neighboring faces. We present a Markov Random Field (MRF) formulation to find the best keyframes as well as the optimal color augmentations. We use a simple model to avoid reflection and camera vignetting during the view selection. Our model for color correction finds the piecewise-linear augmentation to be added to the texture patches of faces. It encourages smoothness inside every fragment while compensating color differences along view transitions. Moreover, we speed up the optimization by breaking down the formulation into multiple binary MRFs that estimate the best augmentations from coarse to fine resolutions. The experimental results prove our method outperforming the state of the art methods.
Secure Power Allocation of Two-Way Relaying with an Untrusted Denoise-and-Forward Relay

This paper proposes an secure transmit power allocation (STPA) scheme for a two-way denoise-and-forward (DNF) relaying network with the aim of maximizing the secrecy sum rate of the network. Here, the DNF relay could act not only as a forwarder but also a potential eavesdropper, i.e., an untrusted relay. First, the secure-rate-region of the two sources that their messages can be strictly kept secret is derived from an information theory point of view. On this basis, an optimal power allocation problem subject to an individual peak-power limit is studied, giving closed-form expressions of the individual transmit powers than can maximize the network secrecy sum rate. Computer simulation results show that compared with the conventional equal-power allocation scheme, the proposed STPA scheme is capable of enhancing the secrecy sum rate and reducing the intercept probability simultaneously.
Recognition and Prediction of Human-Object Interactions with a Self-Organizing Architecture

The recognition and prediction of human actions are challenging perception tasks that require reasoning upon a large space of fine-grained body motion patterns. In this work, we propose a hierarchical self-organizing architecture which jointly learns to recognize and predict human-object interactions from RGB-D videos. Our model consists of a hierarchy of Grow-When-Required (GWR) networks which process and learn co-occurring actions and objects from the training data. Our goal is to learn prototype body motion patterns when manipulating objects as well as to internally store prototype transitions of body postures over time. The architecture can generate sequences of arbitrary length given an observed initial motion pattern as well as predict future action labels. Experimental results on a dataset of daily activities demonstrate that our architecture recognizes ongoing actions and predicts the upcoming ones with high accuracy. The generated body pose trajectories demonstrate that our architecture is suitable to be further applied to the problem of the look-ahead planning of a robotic response in a human-robot interaction scenario.