Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense output for highly oscillatory numerical solutions

We present a method to construct a continuous extension (otherwise known as dense output) for a numerical routine in the special case of the numerical solution being a scalar-valued function exhibiting rapid oscillations. Such cases call for numerical routines that make use of the known global behaviour of the solution, one example being methods using asymptotic expansions to forecast the solution at each step of the independent variable. An example is oscode, numerical routine which uses the Wentzel-Kramers-Brillouin (WKB) approximation when the solution oscillates rapidly and otherwise behaves as a Runge-Kutta (RK) solver. Polynomial interpolation is not suitable for producing the solution at an arbitrary point mid-step, since efficient numerical methods based on the WKB approximation will step through multiple oscillations in a single step. Instead we construct the continuous solution by extending the numerical quadrature used in computing a WKB approximation of the solution with no additional evaluations of the differential equation or terms within, and provide an error estimate on this dense output. Finally, we draw attention to previous work on the continuous extension of Runge-Kutta formulae, and construct an extension to a RK method based on Gauss--Lobatto quadrature nodes, thus describing how to generate dense output from each of the methods underlying oscode.
Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data

In higher education, predictive analytics can provide action-able insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for diﬀerent prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success. However, little is known about the overall utility of diﬀerent data sources across prediction tasks and the fairness of their predictions with respect to diﬀerent subpopulations. Using data from over 2,000 college students at a large public university, we examined the utility of institutional data, learning management system (LMS) data, and survey data for accurately and fairly predicting short-term and long-term student success. We found that institutional data and LMS data both have decent predictive power, but survey data shows very little predictive utility. Combining institutional data with LMS data leads to even higher accuracy than using either alone. In terms of fairness, using institutional data consistently underestimates historically disadvantaged student subpopulations more than their peers, whereas LMS data tend to overestimate some of these groups more often. Combining the two data sources does not fully neutralize the biases and still leads to high rates of underestimation among disadvantaged groups. Moreover, algorithmic biases aﬀect not only demographic minorities but also students with acquired disadvantages. These analyses serve to inform more cost-eﬀective and equitable use of student data for predictive analytics applications in higher education.
The relation between the diffuse X-ray luminosity and the radio power of the central AGN in galaxy groups

Our understanding of how AGN feedback operates in galaxy clusters has improved in recent years owing to large efforts in multi-wavelength observations and hydrodynamical simulations. However, it is much less clear how feedback operates in galaxy groups, which have shallower gravitational potentials. In this work, using very deep VLA and new MeerKAT observations from the MIGHTEE survey, we compiled a sample of 247 X-ray selected galaxy groups detected in the COSMOS field. We have studied the relation between the X-ray emission of the intra-group medium and the 1.4 GHz radio emission of the central radio galaxy. For comparison, we have also built a control sample of 142 galaxy clusters using ROSAT and NVSS data. We find that clusters and groups follow the same correlation between X-ray and radio emission. Large radio galaxies hosted in the centres of groups and merging clusters increase the scatter of the distribution. Using statistical tests and Monte-Carlo simulations, we show that the correlation is not dominated by biases or selection effects. We also find that galaxy groups are more likely than clusters to host large radio galaxies, perhaps owing to the lower ambient gas density or a more efficient accretion mode. In these groups, radiative cooling of the ICM could be less suppressed by AGN heating. We conclude that the feedback processes that operate in galaxy clusters are also effective in groups.
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.
Learning Heat Diffusion for Network Alignment

Networks are abundant in the life sciences. Outstanding challenges include how to characterize similarities between networks, and in extension how to integrate information across networks. Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. EDNA uses the diffusion signal as a proxy for computing node similarities between networks. Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased robustness against noise, and (iii) superior scaling capacity. The EDNA algorithm is versatile in that other available network alignments/embeddings can be used as an initial baseline alignment, and then EDNA works as a wrapper around them by running the evolutionary diffusion on top of them. In conclusion, EDNA outperforms state-of-the-art methods for network alignment, thus setting the stage for large-scale comparison and integration of networks.
Genetic Variation in Artificially Selected Strains of the Egg Parasitoid, Trichogramma chilonis Ishii (Hymenoptera: Trichogrammatidae) Using Rapd Analysis

Artificial selection is extensively used to develop pesticide resistance in natural enemies. RAPD markers were used to estimate genetic relatedness between the parent and artificially selected hybrids of the egg parasitoid Trichogramma chilonis Ishii resistant to multiple insecticides (700ppm of endosulfan, 540ppm of monocrotophos and 20ppm of fenvalerate) and high temperature (> 40°C). Highly polymorphic markers could be identified through the primers OPF-1 and OPJ-20. RAPD marker from 15 oligomers clearly outgrouped the susceptible parent from artificially selected pesticide-resistant strains. The first group comprised the susceptible strain. The second group comprised the two subclusters, the first subcluster including the strain resistant to endosulfan and fenvalerate, multiple-insecticide resistant strain (MIRS) and high temperature resistant (HTR) strain while the second subcluster consisted of monocrotophos and HTR strains. The discriminating property of RAPD markers allowed differentiation of the resistant strain from the parental susceptible strain. Artificially selected resistant strains shared relatively high similarity (61-66%) with susceptible parent strain as per Jaccard's index.
Loss Aversion, Probability Weighting, and the Demand for Insurance

We examine the demand for insurance within the framework of prospect theory preferences to determine whether such preferences can explain the choice of low deductibles observed in the market. Prospect theory implies individuals make decisions by evaluating gains and losses relative to a reference point, where utility is concave over gains and convex over losses; furthermore, losses are weighed more heavily than gains in this setting. We incorporate such preferences in the utility function for an individual and investigate various reference points for an individual making insurance purchasing decisions. We find that insurance purchase decisions made within the context of prospect theory can explain several phenomena observed in insurance markets: the preference for low deductibles for mandatory insurance, the lack of demand for non-mandatory insurance like catastrophe insurance, and the over-demand to insure small losses as seen with the purchasing of warranties.
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models

Undirected graphical models are compact representations of joint probability distributions over random variables. To carry out an inference task of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, when faced with new tasks, these models (EGMs) often need to be re-trained. Instead, we propose an inference-agnostic adversarial training framework for producing an ensemble of graphical models (AGMs). The ensemble is optimized to generate data, and inference is learned as a by-product of this endeavor. AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. Most importantly, AGMs show significantly better generalization capabilities across inference tasks. AGMs are also on par with GibbsNet, a state-of-the-art deep neural architecture, which like AGMs, allows conditioning on any subset of random variables. Finally, AGMs allow fast data sampling, competitive with Gibbs sampling from EGMs.
Virus-induced transgenerational changes

Our previous experiments showed that infection of Nicotiana tabacum plants with Tobacco mosaic virus (TMV) leads to an increase in homologous recombination frequency (HRF). The progeny of infected plants also had an increased rate of rearrangements in resistance gene-like loci. Here, we report that tobacco plants infected with TMV exhibited an increase in HRF in two consecutive generations. Analysis of global genome methylation showed the hypermethylated genome in both generations of plants, whereas analysis of methylation via 5-MeC antibodies demonstrated both hypomethylation and hypermethylation. Analysis of the response of the progeny of infected plants to TMV, Pseudomonas syringae or Phytophtora nicotianae revealed a significant delay in symptom development. Infection of these plants with TMV or Pseudomonas syringae showed higher levels of induction of PR1 gene expression and higher levels of callose deposition. Our experiments suggest that viral infection triggers specific changes in progeny that promote higher levels of HRF at the transgene and higher resistance to stress as compared to the progeny of unstressed plants. However, data reported in these studies do not allow establishing evidence of a link between recombination frequency and stress resistance. is mechanism occurrence of a pathogen-induced systemic increase in HRF? What is the mechanism of inheritance of high-frequency homologous recombination? Are elevated levels of HRF maintained throughout generations? What other changes occur in progeny of infected plants? by analyzing two consecutive progenies of TMV-infected tobacco cultivar SR1 plants. Both progenies of infected plants showed higher levels of somatic HRF, higher resistance to TMV infection and tolerance to methyl methane sulfonate, an increase in callose deposition as well as a higher steady state PR1 RNA level compared to the progeny of uninfected plants. Analysis of methylation patterns has revealed global genome hypermethylation in both progenies paralleled by hypomethylation in euchromatic areas.
Isocurvature modes: joint analysis of the CMB power spectrum and bispectrum

We perform a joint analysis of the power spectrum and the bispectrum of the CMB temperature and polarization anisotropies to improve the constraints on isocurvature modes. We construct joint likelihoods, both for the existing Planck data, and to make forecasts for the future LiteBIRD and CMB-S4 experiments. We assume a general two-field inflation model with five free parameters, leading to one isocurvature mode (which can be CDM density, neutrino density or neutrino velocity) arbitrarily correlated with the adiabatic mode. We theoretically assess in which cases (of detecting and/or fixing parameters) improvements can be expected, to guide our subsequent numerical analyses. We find that for Planck, which detected neither isocurvature modes nor primordial non-Gaussianity, the joint analysis does not improve the constraints in the general case. However, if we fix additional parameters in the model, the improvements can be highly significant depending on the chosen parameter values. For LiteBIRD+CMB-S4 we study in which regions of parameter space compatible with the Planck results the joint analysis will improve the constraints or the significance of a detection. We find that, while for CDM isocurvature this region is very small, for the neutrino isocurvature modes it is much larger. In particular for neutrino velocity it can be about half of the Planck-allowed region, where the joint analysis reduces the isocurvature error bars by up to 70%. In addition the joint analysis can also improve the error bars of some of the standard cosmological parameters, by up to 30% for θMC for example, by breaking the degeneracies with the correlation parameter between adiabatic and isocurvature modes.
Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling

Topic modeling has been one of the most active research areas in machine learning in recent years. Hierarchical latent tree analysis (HLTA) has been recently proposed for hierarchical topic modeling and has shown superior performance over state-of-the-art methods. However, the models used in HLTA have a tree structure and cannot represent the different meanings of multiword expressions sharing the same word appropriately. Therefore, we propose a method for extracting and selecting collocations as a preprocessing step for HLTA. The selected collocations are replaced with single tokens in the bag-of-words model before running HLTA. Our empirical evaluation shows that the proposed method led to better performance of HLTA on three of the four data sets tested.
List Viterbi Decoding of PAC Codes

Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. Motivated by the fact that the parallel list Viterbi algorithm (LVA) sorts the candidate paths locally at each trellis node, in this work, we adapt the trellis, path metric, and the local sorter of LVA to PAC codes and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the complexity of the local sorting of the paths in LVA relative to the global sorting in the list decoding and we show that LVA has a significantly lower sorting complexity compared with list decoding.
Generalized Maximum Entropy for Supervised Classification

The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for arbitrary decision problems where it corresponds to minimax approaches. This paper establishes a framework for supervised classification based on the generalized maximum entropy principle that leads to minimax risk classifiers (MRCs). We develop learning techniques that determine MRCs for general entropy functions and provide performance guarantees by means of convex optimization. In addition, we describe the relationship of the presented techniques with existing classification methods, and quantify MRCs performance in comparison with the proposed bounds and conventional methods.
Resonator Networks outperform optimization methods at solving high-dimensional vector factorization

We develop theoretical foundations of Resonator Networks, a new type of recurrent neural network introduced in Frady et al. (2020) to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures. Given a composite vector formed by the Hadamard product between a discrete set of high-dimensional vectors, a Resonator Network can efficiently decompose the composite into these factors. We compare the performance of Resonator Networks against optimization-based methods, including Alternating Least Squares and several gradient-based algorithms, showing that Resonator Networks are superior in several important ways. This advantage is achieved by leveraging a combination of nonlinear dynamics and "searching in superposition," by which estimates of the correct solution are formed from a weighted superposition of all possible solutions. While the alternative methods also search in superposition, the dynamics of Resonator Networks allow them to strike a more effective balance between exploring the solution space and exploiting local information to drive the network toward probable solutions. Resonator Networks are not guaranteed to converge, but within a particular regime they almost always do. In exchange for relaxing this guarantee of global convergence, Resonator Networks are dramatically more effective at finding factorizations than all alternative approaches considered.
Classical communication through quantum causal structures

Quantum mechanics allows operations to be in indefinite causal order. Recently there have been active discussions on enhanced communication strategies through exotic causal structures. In light of this, through the process matrix formalism, we formulate different classical capacities for a bi-partite quantum process. We find that a one-way communication protocol through an arbitrary process cannot outperform a causally separable process, i.e., we can send at most one bit per qubit. Next, we study bi-directional communication through a causally separable process. Our result shows, a bi-directional protocol cannot exceed the limit of one way communication protocol. Finally, we generalise this result to multi-party broadcast communication protocol through a definite ordered process.
Biological credit assignment through dynamic inversion of feedforward networks

Learning depends on changes in synaptic connections deep inside the brain. In multilayer networks, these changes are triggered by error signals fed back from the output, generally through a stepwise inversion of the feedforward processing steps. The gold standard for this process -- backpropagation -- works well in artificial neural networks, but is biologically implausible. Several recent proposals have emerged to address this problem, but many of these biologically-plausible schemes are based on learning an independent set of feedback connections. This complicates the assignment of errors to each synapse by making it dependent upon a second learning problem, and by fitting inversions rather than guaranteeing them. Here, we show that feedforward network transformations can be effectively inverted through dynamics. We derive this dynamic inversion from the perspective of feedback control, where the forward transformation is reused and dynamically interacts with fixed or random feedback to propagate error signals during the backward pass. Importantly, this scheme does not rely upon a second learning problem for feedback because accurate inversion is guaranteed through the network dynamics. We map these dynamics onto generic feedforward networks, and show that the resulting algorithm performs well on several supervised and unsupervised datasets. We also link this dynamic inversion to Gauss-Newton optimization, suggesting a biologically-plausible approximation to second-order learning. Overall, our work introduces an alternative perspective on credit assignment in the brain, and proposes a special role for temporal dynamics and feedback control during learning.
Prediction of Traffic Flow via Connected Vehicles

We propose a short-term traffic flow prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by connected vehicles (CV) technology. To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory. We solve the STP problem with a Deep Neural Networks (DNN) in a multitask learning setting augmented by input from CV. Results show that our approach, namely MTL-CV, with an average Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to single task learning with artificial neural network (ANN), ANN had a lower performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical similarities between segments, in contrast to using direct historical trends in the measure, because trends may not exist in the measure but do in the similarities.
Exploring Consequences of Simulation Design for Apparent Performance of Statistical Methods. 2: Results from simulations with normally and uniformly distributed sample sizes

This report continues our investigation of effects a simulation design may have on the conclusions on performance of statistical methods. In the context of meta-analysis of log-odds-ratios, we consider five generation mechanisms for control probabilities and log-odds-ratios. Our first report (Kulinskaya et al. 2020) considered constant sample sizes. Here we report on the results for normally and uniformly distributed sample sizes.