Inference for Clustered Mixed Outcomes from a Multivariate Generalized Linear Mixed Model

Multivariate generalized linear mixed models (MGLMM) are used for jointly modeling the clustered mixed outcomes obtained when there are two or more responses repeatedly measured on each individual in scientific studies. The relationship among these responses is often of interest. In the clustered mixed data, the correlation could be present between repeated measurements either within the same observer or between different observers on the same subjects. This study proposes a series of in- dices, namely, intra, inter and total correlation coefficients, to measure the correlation under various circumstances of observations from a multivariate generalized linear model, especially for joint modeling of clustered count and continuous outcomes. Bayesian methods are widely used techniques for analyzing MGLMM. The need for noninformative priors arises when there is insufficient prior information on the model parameters. Another aim of this study is to propose an approximate uniform shrinkage prior for the random effect variance components in the Bayesian analysis for the MGLMM. This prior is an extension of the approximate uniform shrinkage prior. This prior is easy to apply and is shown to possess several nice properties. The methods are illustrated in terms of both a simulation study and a case example

Role of GABAergic signaling and the GABAA receptor subunit gene cluster at 15q11-q13 in autism spectrum disorders, schizophrenia, and heroin addiction

AbstractAutism spectrum disorders, schizophrenia, and heroin addiction are all complex disorders with both genetic and environmental components to their etiology. The most common chromosomal abnormality in autism is a maternally derived duplication at 15q11-q13, which is where a cluster of gamma-aminobutyric acid (GABAA) receptor subunit genes lies. In addition, copy number variations in this area have been implicated in the pathogenesis of schizophrenia. These findings suggest that GABAergic signaling might play a crucial role in contributing to susceptibility to the development of autism and schizophrenia. Furthermore, there is considerable evidence supporting a role for GABA neurotransmission in mediating the addictive properties of heroin. Hence, this review explores recent findings related to the involvement of GABAergic system in autism, schizophrenia, and heroin addiction. We also outline the implications that the presence of genetic variants in the GABAA receptor subunit cluster at 15q11-q13 may have on the risk of developing these psychiatric disorders. Finally, we make recommendations for future work that might help define the mechanisms underpinning the neuropathology that contributes to these psychiatric disorders

OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning

Recent works have shown that by using large pre-trained models along with learnable prompts, rehearsal-free methods for class-incremental learning (CIL) settings can achieve superior performance to prominent rehearsal-based ones. Rehearsal-free CIL methods struggle with distinguishing classes from different tasks, as those are not trained together. In this work we propose a regularization method based on virtual outliers to tighten decision boundaries of the classifier, such that confusion of classes among different tasks is mitigated. Recent prompt-based methods often require a pool of task-specific prompts, in order to prevent overwriting knowledge of previous tasks with that of the new task, leading to extra computation in querying and composing an appropriate prompt from the pool. This additional cost can be eliminated, without sacrificing accuracy, as we reveal in the paper. We illustrate that a simplified prompt-based method can achieve results comparable to previous state-of-the-art (SOTA) methods equipped with a prompt pool, using much less learnable parameters and lower inference cost. Our regularization method has demonstrated its compatibility with different prompt-based methods, boosting those previous SOTA rehearsal-free CIL methods' accuracy on the ImageNet-R and CIFAR-100 benchmarks. Our source code is available at https://github.com/jpmorganchase/ovor.Comment: Accepted by ICLR 202

Methyl 4-[(5-chloro­pyrimidin-2-yl)carbamo­yl]benzoate

Mol­ecules of the title compound, C13H10ClN3O3, form centrosymmetric dimers via inter­molecular N—H⋯N hydrogen bonds generating an R 2 2(8) motif. The dimers are further connected through an O⋯Cl—C halogen bond [O⋯Cl = 3.233 (1) Å and O⋯Cl—C = 167.33 (1)°] into a chain along [110]. The secondary amide group adopts a cis conformation. Weak C—H⋯N hydrogen bonds among the methyl benzoate and pyrimidyl rings are also observed in the crystal structure

Impacts of Techno-Dependence in The Mobile Instant Messaging Environment

Mobile Instant Messaging (MIM) services such as LINE, WhatsApp, Facebook Messenger and WeChat have already established a mobile communication environment that extends beyond words and sounds. As MIM provides an effective way to communicate, it can improve workplace efficiency, whether within an organization or among offices spread around the world. Nowadays, MIM has been widely accepted as both a social and work tool. The social interaction overload generated by SNSs contributes to emotional exhaustion. Emotional exhaustion, on the other hand, leads to dissatisfaction and discontinuous usage intentions. In the context of MIMs (since they are relative new apps), users whether are likely to experience emotional exhaustion that is generated by social interaction overload, and therefore discontinue their use of MIMs. In contrast to traditional research on IS continued use in the past which defines dependence as a routine and unconscious usage pattern. MIMs offer a communication method that is faster and easier than phone calls or SMS. It is possible that MIMs bring people closer by allowing their users to understand more of the situational matters related to their friends or family, without being limited by distance. Specifically, social-group functions offered by LINE can encourage users to join certain social groups (for instance, family, colleagues, classmates, or friends). Group members can not only discuss common topics, they can also share their “photo albums,” enabling members to enhance their sense of belonging. At the same time, they have the opportunity to feel a sense of being valued, loved, and needed. Although the mobility and accessibility of mobile devices allow users to instantly contact each other on MIMs and on real-time basis, excessive use of MIMs, or MIM techno-dependence, is likely to generate social-related stress among their users. Therefore, this research attempts to explore the possibility that MIM techno-dependency can have non-detrimental effects, and considers the positive and healthy results from MIM techno-dependency due to an increased sense of belonging. The questions explored include: do MIMs users develop a positive techno-dependence? Does this positive emotional reaction encourage MIMs users to continue their use of MIMs? Since LINE is a relative newcomer to MIM, there is still a dearth of research needed to explore issues related to using MIM as a research tool. This study considers how LINE combines a diverse range of communication approaches—such as voice, texts, maps, pictures, photos, locations, video, and audio—with a variety of community groups such as friends chat, group chat, dynamic news, and official accounts. It seems worthwhile to study characteristics of LINE’s users in order to further explore the issues related to MIM. Through the hypotheses development and a survey research on 685 LINE users, this study inferred that users make frequent use of LINE in the long-term mainly because of four kinds of techno-dependence: people, fun, information, and work. Such techno-dependence generates positive and negative consequences concurrently. On the one hand, because the user’s dependence on LINE enhances his or her belongingness through friends, colleagues, and family, this positive social and emotional reaction will make users satisfied with LINE, and thus increase continuous usage intention for LINE. On the other hand, the user’s dependence on LINE means that they experience social interaction overload resulting in emotional exhaustion. Dependence on LINE leads to users experiencing pressure from both social message overload and social demand overload, resulting in social interaction overload. This negative social and emotional reaction will cause a decrease in user satisfaction with LINE, thereby reducing the continuous usage intention of LINE. Based on these findings, we suggest that LINE-related techno-dependence can enable users to increase their sense of positive social belongingness, but can also cause negative social interaction overload. It is concluded that the consequences of techno-dependence are characterized by both positive and negative emotions. Users’ evaluations of LINE are simultaneously affected by positive and negative social and emotional factors