Linear Haskell: practical linearity in a higher-order polymorphic language

Linear type systems have a long and storied history, but not a clear path forward to integrate with existing languages such as OCaml or Haskell. In this paper, we study a linear type system designed with two crucial properties in mind: backwards-compatibility and code reuse across linear and non-linear users of a library. Only then can the benefits of linear types permeate conventional functional programming. Rather than bifurcate types into linear and non-linear counterparts, we instead attach linearity to function arrows. Linear functions can receive inputs from linearly-bound values, but can also operate over unrestricted, regular values. To demonstrate the efficacy of our linear type system - both how easy it can be integrated in an existing language implementation and how streamlined it makes it to write programs with linear types - we implemented our type system in GHC, the leading Haskell compiler, and demonstrate two kinds of applications of linear types: mutable data with pure interfaces; and enforcing protocols in I/O-performing functions

Investigating the Role of Real Divisia Money in Persistence-Robust Econometric Models

This paper investigates the causal relationships between real money and real activity. Whereas previous literature has mainly focused on simple-sum aggregates, we instead use Divisia ones, thus avoiding the so-called Barnett Critique. Standard Granger non-causality tests are implemented in two di¤erent frameworks: Fully Modied VARs (Phillips, 1995) and surplus-lag VARX models (Bauer and Maynard, 2012). These two environments allow modeling mixtures of I(0)/I(1) variables with possible cointegration without pretesting for ntegration nor for the dimension of the cointegration space. Moreover the latter method is also robust to various other forms of persistence such as local-to-unity processes, long memory/fractional integration, or unmodeled breaks-in-mean in the causal variables. By implementing the tests on di¤erent sub-samples identied by standard structural break tests, and using three di¤erent measures of money (DM4, DM4- and DM3), the tests suggest a unidirectional causality from activity to money. Moreover, from one period to another, the whole causal structure of the systems seem to change, as well as the stationarity of the series. At last, the two methodologies return similar results

Imaging dielectric relaxation in nanostructured polymers by frequency modulation electrostatic force microscopy

We have developed a method for imaging the temperature-frequency dependence of the dynamics of nanostructured polymer films with spatial resolution. This method provides images with dielectric compositional contrast well decoupled from topography. Using frequency-modulation electrostatic-force-microscopy, we probe the local frequency-dependent (0.1–100 Hz) dielectric response through measurement of the amplitude and phase of the force gradient in response to an oscillating applied electric field. When the phase is imaged at fixed frequency, it reveals the spatial variation in dielectric losses, i.e., the spatial variation in molecular/dipolar dynamics, with 40 nm lateral resolution. This is demonstrated by using as a model system; a phase separated polystyrene/polyvinyl-acetate (PVAc) blend. We show that nanoscale dynamic domains of PVAc are clearly identifiable in phase images as those which light-up in a band of temperature, reflecting the variations in the molecular/dipolar dynamics approaching the glass transition temperature of PVAc

Vascular Insulin Resistance May Contribute to Health Disparities in People from the Rio Grande Valley

Microvascular blood flow (MBF) increases postprandially in skeletal muscle in response to insulin to aid in myocyte glucose delivery. This MBF response is considered a measure of vascular insulin resistance and can be impaired with altered meal composition, obesity, type 2 diabetes mellitus (T2DM), and insulin resistance. Current studies indicate this MBF response to a mixed meal challenge (MMC) may identify vascular insulin resistance before typically-presenting serum biomarkers of insulin resistance, as it displays more sensitivity than when using an oral glucose challenge (OGC). However, it is unknown if healthy adults residing in the Rio Grande Valley (RGV), an area with a 3x higher prevalence of T2DM vs the national average, demonstrate impaired MBF responses similar to those seen in overt insulin resistance. PURPOSE: To determine microvascular responses in apparently healthy individuals of the RGV between OGC and MMC. METHODS: 17 healthy participants from the RGV (age 25±6 yrs, BMI 25±3 kg/m2, fat mass % 29±9%, and android fat % 31±10.4%) without hypertension, T2DM, or dyslipidemia were administered a MMC and OGC on two separate occasions. Forearm skeletal muscle MBF (measured as acoustic intensity/second (AI/s)) was recorded pre- and 1-hour postprandial via contrast-enhanced ultrasound (CEU). RESULTS: MMC pre- vs. post-prandial demonstrated a 0.59 fold reduction (1.6101 vs. 0.6548 AI/s, 95% CI [-.2871, 3.5073] and [0.887, 1.2209], respectively). OGC pre- vs. post-prandial MBF had a 0.18 fold reduction (1.6734 vs. 1.3693 AI/s, 95% CI [.3755, 2.9714] and [.4725, 2.2661], respectively). MBF in skeletal muscle demonstrated no significant difference between MMC and OGC groups (Mean square= 2.378, F(1, 48) = .320, p = 0.574). CONCLUSION: Unlike healthy Caucasians, apparently healthy residents of the RGV display impaired microvascular responses to MMC, similar to using an OGC, suggesting early vascular insulin resistance. As this population displays significant health disparities for chronic diseases such as T2DM, obesity, and Alzheimer’s, it is plausible that early vascular insulin resistance noted in this population significantly contributes to the increased incidence of these chronic diseases. Additional research is needed to identify mechanisms explaining this population\u27s etiology of impaired MBF responses and vascular insulin resistance

Quantum Einstein-Dirac Bianchi Universes

We study the mini--superspace quantization of spatially homogeneous (Bianchi) cosmological universes sourced by a Dirac spinor field. The quantization of the homogeneous spinor leads to a finite-dimensional fermionic Hilbert space and thereby to a multi-component Wheeler-DeWitt equation whose main features are: (i) the presence of spin-dependent Morse-type potentials, and (ii) the appearance of a q-number squared-mass term, which is of order ${\cal O}(\hbar^2)$, and which is affected by ordering ambiguities. We give the exact quantum solution of the Bianchi type-II system (which contains both scattering states and bound states), and discuss the main qualitative features of the quantum dynamics of the (classically chaotic) Bianchi type-IX system. We compare the exact quantum dynamics of fermionic cosmological billiards to previous works that described the spinor field as being either classical or Grassmann-valued.Comment: 50 page

Learning the Efficient Frontier

The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimization problem with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.Comment: Accepted at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Below the radar: Data, narratives and the politics of irrigation in sub-Saharan Africa

Emerging narratives call for recognising and engaging constructively with small-scale farmers who have a leading role in shaping the current irrigation dynamics in sub-Saharan Africa. This paper explores whether new irrigation data can usefully inform these narratives. It argues that, for a variety of reasons, official irrigation data in sub-Saharan Africa fail to capture the full extent and diverse nature of irrigation and its rapid distributed growth over the last two decades. The paper investigates recent trends in the use of remote sensing methods to generate irrigation data; it examines the associated expectation that these techniques enable a better understanding of current irrigation developments and small-scale farmers' roles. It reports on a pilot study that uses radar-based imagery and analysis to provide new insights into the extent of rice irrigated agriculture in three regions of Tanzania. We further stress that such mapping exercises remain grounded in a binary logic that separates 'irrigation' from other 'non-irrigated' landscape features. They can stem from, and reinforce, a conventional understanding of irrigation that is still influenced by colonial legacies of engineering design and agricultural modernisation. As farmers' initiatives question this dominant view of irrigation, and in a policy context that is dominated by narratives of water scarcity, this means that new data may improve the visibility of water use by small-scale irrigators but may † Jean-Philippe Venot and Sam Bowers are joint first authors. General comments and other correspondence should be addressed to Jean-Philippe Venot. Correspondence on remote sensing radar analysis should be addressed to Sam Bowers. Water Alternatives-2021 Volume 14 | Issue 2 Venot et al.: The politics of irrigation data in Sub-Saharan Africa 547 also leave them more exposed to restrictions favouring more powerful water users. The paper thus calls for moving away from a narrow debate on irrigation data and monitoring, and towards a holistic discussion of the nature of irrigation development in sub-Saharan Africa. This discussion is necessary to support a constructive engagement with farmer-led irrigation development; it is also challenging in that it involves facing entrenched vested interests and requires changes in development practices

Overcoming the Ontology Enrichment Bottleneck with Quick Term Templates

The developers of the Ontology of Biomedical Investigations (OBI) primarily use Protégé for editing. However, adding many classes with similar patterns of logical definition is time consuming, error prone, and requires the editor to have some expertise in OWL. Therefore, the process is poorly suited for a large number of domain experts who have limited experience Protégé and ontology development. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions yet requires minimal manual intervention by OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format which is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment

Deep Learning Body Region Classification of MRI and CT examinations

Standardized body region labelling of individual images provides data that can improve human and computer use of medical images. A CNN-based classifier was developed to identify body regions in CT and MRI. 17 CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test databases originated from a different healthcare network. Accuracy, recall and precision of the classifier was evaluated for patient age, patient gender, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2,934 anonymized CT cases (training: 1,804 studies, validation: 602 studies, test: 528 studies) and 3,185 anonymized MRI cases (training: 1,911 studies, validation: 636 studies, test: 638 studies). 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aged from a few months old to +90 years old. An image-level prediction accuracy of 91.9% (90.2 - 92.1) for CT, and 94.2% (92.0 - 95.6) for MRI was achieved. The classification results were robust across all body regions and confounding factors. Due to limited data, performance results for subjects under 10 years-old could not be reliably evaluated. We show that deep learning models can classify CT and MRI images by body region including lower and upper extremities with high accuracy.Comment: 21 pages, 2 figures, 4 table