Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT

The Wald, likelihood ratio, score, and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This allows the application to a variety of IRT models, which are commonly used in practice, e.g., in large-scale educational assessments. An analytical method utilizes the asymptotic distributions of the statistics under alternative hypotheses. We also provide a sampling-based approach for applications where the analytical approach is computationally infeasible. This can be the case with 20 or more items, since the computational load increases exponentially with the number of items. We performed extensive simulation studies in three practically relevant settings, i.e., testing a Rasch model against a 2PL model, testing for differential item functioning, and testing a partial credit model against a generalized partial credit model. The observed distributions of the test statistics and the power of the tests agreed well with the predictions by the proposed methods in sufficiently large samples. We provide an openly accessible R package that implements the methods for user-supplied hypotheses

Free-form Flows: Make Any Architecture a Normalizing Flow

Normalizing Flows are generative models that directly maximize the likelihood. Previously, the design of normalizing flows was largely constrained by the need for analytical invertibility. We overcome this constraint by a training procedure that uses an efficient estimator for the gradient of the change of variables formula. This enables any dimension-preserving neural network to serve as a generative model through maximum likelihood training. Our approach allows placing the emphasis on tailoring inductive biases precisely to the task at hand. Specifically, we achieve excellent results in molecule generation benchmarks utilizing $E(n)$-equivariant networks. Moreover, our method is competitive in an inverse problem benchmark, while employing off-the-shelf ResNet architectures

On the Convergence Rate of Gaussianization with Random Rotations

Gaussianization is a simple generative model that can be trained without backpropagation. It has shown compelling performance on low dimensional data. As the dimension increases, however, it has been observed that the convergence speed slows down. We show analytically that the number of required layers scales linearly with the dimension for Gaussian input. We argue that this is because the model is unable to capture dependencies between dimensions. Empirically, we find the same linear increase in cost for arbitrary input $p(x)$, but observe favorable scaling for some distributions. We explore potential speed-ups and formulate challenges for further research

Maximum Likelihood Training of Autoencoders

Maximum likelihood training has favorable statistical properties and is popular for generative modeling, especially with normalizing flows. On the other hand, generative autoencoders promise to be more efficient than normalizing flows due to the manifold hypothesis. In this work, we introduce successful maximum likelihood training of unconstrained autoencoders for the first time, bringing the two paradigms together. To do so, we identify and overcome two challenges: Firstly, existing maximum likelihood estimators for free-form networks are unacceptably slow, relying on iteration schemes whose cost scales linearly with latent dimension. We introduce an improved estimator which eliminates iteration, resulting in constant cost (roughly double the runtime per batch of a vanilla autoencoder). Secondly, we demonstrate that naively applying maximum likelihood to autoencoders can lead to divergent solutions and use this insight to motivate a stable maximum likelihood training objective. We perform extensive experiments on toy, tabular and image data, demonstrating the competitive performance of the resulting model. We call our model the maximum likelihood autoencoder (MLAE)

t-PA Suppresses the Immune Response and Aggravates Neurological Deficit in a Murine Model of Ischemic Stroke

Introduction: Acute ischemic stroke (AIS) is a potent trigger of immunosuppression, resulting in increased infection risk. While thrombolytic therapy with tissue-type plasminogen activator (t-PA) is still the only pharmacological treatment for AIS, plasmin, the effector protease, has been reported to suppress dendritic cells (DCs), known for their potent antigen-presenting capacity. Accordingly, in the major group of thrombolyzed AIS patients who fail to reanalyze (>60%), t-PA might trigger unintended and potentially harmful immunosuppressive consequences instead of beneficial reperfusion. To test this hypothesis, we performed an exploratory study to investigate the immunomodulatory properties of t-PA treatment in a mouse model of ischemic stroke.Methods: C57Bl/6J wild-type mice and plasminogen-deficient (plg−/−) mice were subjected to middle cerebral artery occlusion (MCAo) for 60 min followed by mouse t-PA treatment (0.9 mg/kg) at reperfusion. Behavioral testing was performed 23 h after occlusion, pursued by determination of blood counts and plasma cytokines at 24 h. Spleens and cervical lymph nodes (cLN) were also harvested and characterized by flow cytometry.Results: MCAo resulted in profound attenuation of immune activation, as anticipated. t-PA treatment not only worsened neurological deficit, but further reduced lymphocyte and monocyte counts in blood, enhanced plasma levels of both IL-10 and TNFα and decreased various conventional DC subsets in the spleen and cLN, consistent with enhanced immunosuppression and systemic inflammation after stroke. Many of these effects were abolished in plg−/− mice, suggesting plasmin as a key mediator of t-PA-induced immunosuppression.Conclusion: t-PA, via plasmin generation, may weaken the immune response post-stroke, potentially enhancing infection risk and impairing neurological recovery. Due to the large number of comparisons performed in this study, additional pre-clinical work is required to confirm these significant possibilities. Future studies will also need to ascertain the functional implications of t-PA-mediated immunosuppression for thrombolyzed AIS patients, particularly for those with failed recanalization

Nitrate stable isotopes and major ions in snow and ice samples from four Svalbard sites

Increasing reactive nitrogen (N-r) deposition in the Arctic may adversely impact N-limited ecosystems. To investigate atmospheric transport of N-r to Svalbard, Norwegian Arctic, snow and firn samples were collected from glaciers and analysed to define spatial and temporal variations (1 10 years) in major ion concentrations and the stable isotope composition (delta N-15 and delta O-18) of nitrate (NO3-) across the archipelago. The delta N-15(NO3-) and delta O-18(NO3-) averaged -4 parts per thousand and 67 parts per thousand in seasonal snow (2010-11) and -9 parts per thousand and 74 parts per thousand in firn accumulated over the decade 2001-2011. East-west zonal gradients were observed across the archipelago for some major ions (non-sea salt sulphate and magnesium) and also for delta N-15(NO3-) and delta O-18(NO3-) in snow, which suggests a different origin for air masses arriving in different sectors of Svalbard. We propose that snowfall associated with long-distance air mass transport over the Arctic Ocean inherits relatively low delta N-15(NO3-) due to in-transport N isotope fractionation. In contrast, faster air mass transport from the north-west Atlantic or northern Europe results in snowfall with higher delta N-15(NO3-) because in-transport fractionation of N is then time-limited

Power analysis for the Wald, LR, score, and gradient tests in a marginal maximum likelihood framework: Applications in IRT

The Wald, likelihood ratio, score and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This avails the application to a variety of IRT models, which are increasingly used in practice, e.g., in large-scale educational assessments. An analytical method utilizes the asymptotic distributions of the statistics under alternative hypotheses. For a larger number of items, we also provide a sampling-based method, which is necessary due to an exponentially increasing computational load of the analytical approach. We performed extensive simulation studies in two practically relevant settings, i.e., testing a Rasch model against a 2PL model and testing for differential item functioning. The observed distributions of the test statistics and the power of the tests agreed well with the predictions by the proposed methods. We provide an openly accessible R package that implements the methods for user-supplied hypotheses

Speech recordings via the internet: An overview of the VOYS project in Scotland

The VOYS (Voices of Young Scots) project aims to establish a speech database of adolescent Scottish speakers. This database will serve for speech recognition technology and sociophonetic research. 300 pupils will ultimately be recorded at secondary schools in 10 locations in Scotland. Recordings are performed via the Internet using two microphones (closetalk and desktop) in 22,05 kHz 16 bit linear stereo signal quality. VOYS is the first large-scale and cross-boundary speech data collection based on the WikiSpeech content management system for speech resources. In VOYS, schools receive a kit containing the microphones and A/D interface and they organise the recordings themselves. The recorded data is immediately uploaded to the server in Munich, alleviating the schools from all data-handling tasks. This paper outlines the corpus specification, describes the technical issues, summarises the signal quality and gives a status report.caslpub2536pu