Miracle-dz/UniFAR-FTUs huggingface dataset:
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

Healthcare representation learning on the Electronic Health Records is crucial for downstream medical prediction tasks in health informatics. Many NLP techniques, such as RNN and self-attention, have been adapted to learn medical representations from hierarchical and time-stamped EHRs data, but fail when they lack either general or task-specific data. Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning. To address the challenges, we propose a Transformer-based representation learning approach: Mutual Integration of Patient journey and medical Ontology (MIPO), which is a robust end-to-end framework. Specifically, the proposed method focuses on task-specific representation learning by a sequential diagnoses predictive task, which is also beneficial to the ontology-based disease typing task. To integrate information in the patient's visiting records, we further introduce a graph-embedding module, which can mitigate the challenge of data insufficiency in healthcare. In this way, MIPO creates a mutual integration to benefit both healthcare representation learning and medical ontology embedding. Such an effective integration is guaranteed by joint training over fused embeddings of the two modules, targeting both task-specific prediction and ontology-based disease typing tasks simultaneously. Extensive experiments conducted on two real-world benchmark datasets have shown MIPO consistently achieves better performance than state-of-the-art methods no matter whether the training data is sufficient or not. Also, MIPO derives more interpretable diagnose embedding results compared to its counterparts.
Measurement of the branching fraction and a search for a second-class current in the

The (cid:1) (cid:1) ! (cid:2)(cid:3) (cid:1) (cid:3) þ (cid:3) (cid:1) (cid:4) (cid:1) decay with the (cid:2) ! (cid:5)(cid:5) mode is studied using 384 fb (cid:1) 1 of data collected by the BABAR detector. The branching fraction is measured to be ð 1 : 60 (cid:2) 0 : 05 (cid:2) 0 : 11 Þ (cid:3) 10 (cid:1) 4 . It is found that (cid:1) (cid:1) ! f 1 ð 1285 Þ (cid:3) (cid:1) (cid:4) (cid:1) ! (cid:2)(cid:3) (cid:1) (cid:3) þ (cid:3) (cid:1) (cid:4) (cid:1) is the dominant decay mode with a branching fraction of ð 1 : 11 (cid:2) 0 : 06 (cid:2) 0 : 05 Þ (cid:3) 10 (cid:1) 4 . The ﬁrst error on the branching fractions is statistical and the second systematic. Note that no particle identiﬁcation algorithm is applied to the charged tracks to distinguish pions from kaons. In addition, a 90% conﬁdence level upper limit on the branching fraction of the (cid:1) (cid:1) ! (cid:2) 0 ð 958 Þ (cid:3) (cid:1) (cid:4) (cid:1) decay is measured to be 7 : 2 (cid:3) 10 (cid:1) 6 . This last decay proceeds through a second-class current and is expected to be forbidden in the limit of isospin symmetry.
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

This appendix is organized as follows: We ﬁrst present an extended overview of the standard treatment effect estimation setup and discuss differences with the time-to-event setting (Appendix A). Then, we give an extended review of strategies for nonparametric estimation of survival dynamics (Appendix B). In Appendix C we discuss technical details – assumptions and proofs – and Appendix D we discuss implementation. Appendix E contains additional descriptions of datasets and experimental setup and Appendix F presents additional results. Appendix G contains the NeurIPS checklist.
Incorporating Syntactic Uncertainty in Neural Machine Translation withForest-to-seuqence Mode

Previous work on utilizing parse trees of source sentence in Attentional Neural Machine Translation was promising. However, current models suffer from a major drawback: they use only 1-best parse tree which may lead to translation mistakes due to parsing errors. In this paper we propose a forest-to-sequence Attentional Neural Machine Translation model which uses a forest instead of the 1-best tree. Our method utilizes phrase structure of source sentence given by exponentially many grammar trees (compacted in a packed forest) to compensate parsing errors. Experiments on English to Japanese, German and Persian dataset demonsterate superiority of our method over the treeto-sequence and vanilla Attentional Neural Machine Translation models.
Exploring the Impact of Image Resolution on Chest X-ray Classification Performance

Deep learning models for image classification have often used a resolution of 224 × 224 pixels for computational reasons. This study investigates the effect of image resolution on chest X-ray classification performance, using the ChestX-ray14 dataset. The results show that a higher image resolution, specifically 1024 × 1024 pixels, has the best overall classification performance, with a slight decline in performance between 256 × 256 to 512 × 512 pixels for most of the pathological classes. Comparison of saliency map-generated bounding boxes revealed that commonly used resolutions are insufficient for finding most pathologies.
The Role of Endophilin A1 in Lipopolysaccharide-Induced Parkinson’s Disease Model Mice

Background: Endophilin A1 (EPA1) is encoded by the SH3GL2 gene, and SH3GL2 was designated as a Parkinson’s disease (PD) risk locus by genome-wide association analysis, suggesting that EPA1 may be involved in the occurrence and development of PD. Objective: To investigate the role of EPA1 in lipopolysaccharide (LPS)-induced PD model mice. Methods: The mice PD model was prepared by injecting LPS into the substantia nigra (SN), and the changes in the behavioral data of mice in each group were observed. The damage of dopaminergic neurons, activation of microglia, and reactive oxygen species (ROS) generation were detected by immunofluorescence method; calcium ion concentration was detected by calcium content detection kit; EPA1 and inflammation and its related indicators were detected by western blot method. EPA1 knockdown was performed by an adeno-associated virus vector containing EPA1-shRNA-eGFP infusion. Results: LPS-induced PD model mice developed behavioral dysfunction, SN dopaminergic nerve damage, significantly increased calcium ion, calpain 1, and ROS production, activated NLRP1 inflammasome and promoted pro-inflammatory cell release, and SN EPA1 knockdown improves behavioral disorders, alleviates dopaminergic neuron damage, reduces calcium, calpain 1, ROS generation, and blocks NLRP1 inflammasome-driven inflammatory responses. Conclusion: The expression of EPA1 in the SN of LPS-induced PD model mice was increased, and it played a role in promoting the occurrence and development of PD. EPA1 knockdown inhibited the NLRP1 inflammasome activation, decreased the release of inflammatory factors and ROS generation, and alleviated dopaminergic neuron damage. This indicated that EPA1 may participating in the occurrence and development of PD.
Noise Stability of Functions with Low Influences: Invariance and Optimality II - The Non Reversible Case

In this paper we study functions with low influences on product probability spaces. These are functions f : Ω 1 × · · · × Ω n → R that have E[Var Ωi [f ]] small compared to Var[f ] for each i. The analysis of boolean functions f : {−1, 1} n → {−1, 1} with low influences has become a central problem in discrete Fourier analysis. It is motivated by fundamental questions arising from the the theory of social choice in economics and from the construction of probabilistically checkable proofs in theoretical computer science. We generalize an invariance principle recently obtained with O'Donnell and Oleszkiewicz for multilinear polynomials with low influences and bounded degree [20, 21]. The generalization proven here shows invariance of the joint distribution of several multi-linear polynomials. This in turn allows to obtain optimal bounds on " noise sensitivity " defined by non-reversible noise operators generalizing the results of [20, 21, 7]. We present two applications of the generalized invariance principle to the theory of social choice. We show that Majority is asymptotically the most predictable function among all low influence functions given a random sample of the voters. Moreover, we derive an almost tight bound in the context of Condorcet aggregation and low influence voting schemes on a large number of candidates. In particular, we show that for every low influence aggregation function, the probability that Condorcet voting on k candidates will result in a unique candidate that is preferable to all other is k −1+o(1). This matches the asymptotic behavior of the majority function for which the probability is k −1−o(1). Interestingly, we observe that the probability that Condorcet ranking via Majority will result in a linear order is exp(−(1 + o(1))k 5/3). It is not clear if Majority asymptotically maximizes the probability of obtaining a linear ranking among all low influence functions.
Near-Linear Time Approximation Algorithms for Curve Simplification ∗

We consider the problem of approximating a polygonal curve P under a given error criterion by another polygonal curve P ′ whose vertices are a subset of the vertices of P . The goal is to minimize the number of vertices of P ′ while ensuring that the error between P ′ and P is below a certain threshold. We consider two fundamentally different error measures — Hausdorff and Fréchet error measures. For both error criteria, we present near-linear time approximation algorithms that, given a parameter ε > 0, compute a simplified polygonal curve P ′ whose error is less than ε and size is at most the size of the optimal simplified polygonal curve with error ε/2. We consider monotone curves in the case of Hausdorff error measure and arbitrary curves for the Fréchet error measure. We present experimental results demonstrating that our algorithms are simple and fast, and produce close to optimal simplifications in practice.
Smith ScholarWorks Smith ScholarWorks

Charitable giving can boost an individual’s image, and organizations can cap-italize on this by engaging in “image-fundraising. ” Public announcements of donations give individuals the opportunity to demonstrate their generosity and are found to increase giving. This paper evaluates whether generosity inferred from charitable giving is discounted when donations are made in response to image-fundraising. I show in an experimental study that others reward larger donations, and that image-fundraising increases giving. However, others account for the conditions under which donations are made and reduce rewards for giving in an image-fundraising environment. While image-fundraising beneﬁts charitable organizations, individuals are not recompensed for donating more in this setting
Ravotti, D. (2017). Quantitative Mixing for Locally Hamiltonian Flows with Saddle Loops on Compact Surfaces. Annales

Given a compact surface M with a smooth area form ω, we consider an open and dense subset of the set of smooth closed 1-forms on M with isolated zeros which admit at least one saddle loop homologous to zero and we prove that almost every element in the former induces a mixing flow on each minimal component. Moreover, we provide an estimate of the speed of the decay of correlations for smooth functions with compact support on the complement of the set of singularities. This result is achieved by proving a quantitative version for the case of finitely many singularities of a theorem by Ulcigrai (Ergod Theory Dyn Syst 27(3):991– 1035, 2007), stating that any suspension flow with one asymmetric logarithmic singularity over almost every interval exchange transformation is mixing. In particular, the quantitative mixing estimate we prove applies to asymmetric logarithmic suspension flows over rotations, which were shown to be mixing by Sinai and Khanin.
Polyphonic Note and Instrument Tracking Using Linear Dynamical Systems

In this paper, a system for automatic transcription of multiple-instrument polyphonic music is proposed, which supports tracking multiple concurrent notes using linear dynamical systems (LDS). The system is based on a spectrogram factorisation model which extends probabilistic latent component analysis (PLCA), and supports the detection of multiple pitches, instrument contributions, and pitch deviations. In order to jointly track multiple concurrent pitches, the use of LDS as prior to the PLCA model is proposed. LDS parameters are learned in a training stage using score-informed transcriptions; for LDS inference, online and offline variants are evaluated. The MAPS piano music dataset and the Bach10 multi-instrument dataset are used for note tracking experiments, with the latter dataset also being evaluated with respect to instrument assignment performance. Results show that the proposed LDS-based method can successfully track multiple concurrent notes, leading to an improvement of over 3% in terms of note-based F-measure for both datasets over benchmark note tracking approaches.
Retrospective Analysis of Controversial Subtopics on COVID-19 in Japan

For efficient political decision-making in an emergency situation, a thorough recognition and understanding of the polarized topics is crucial. The cost of unmitigated polarization would be extremely high for the society;therefore, it is desirable to identify the polarizing issues before they become serious. With this in mind, we conducted a retrospective analysis of the polarized subtopics of COVID-19 to obtain insights for future policymaking. To this end, we first propose a framework to comprehensively search for controversial subtopics. We then retrospectively analyze subtopics on COVID-19 using the proposed framework, with data obtained via Twitter in Japan. The results show that the proposed framework can effectively detect controversial subtopics that reflect current reality. Controversial subtopics tend to be about the government, medical matters, economy, and education;moreover, the controversy score had a low correlation with the traditional indicators--scale and sentiment of the subtopics--which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between subtopics that became highly controversial and ones that did not despite their large scale.
Using the Blended Learning Approach in a Quantitative Literacy Course

Abstract The efforts to improve the quantitative reasoning (quantitative literacy) skills of college students in the United States have been gaining momentum in recent years. At the same time, the blended learning approach to course delivery has gained in popularity, promising better learning with flexible modalities and pace. This paper presents the results of an exploratory study that compares student learning outcomes and student attitudes over several semesters of blended versus traditional delivery in the upper-division quantitative literacy course at Point Loma Nazarene University.
Testing using Privileged Information by Adapting Features with Statistical Dependence – Supplemental Kwang In Kim

This supplemental material provides additional related work discussion (Sec. 1), additional background information to the Hilbert-Schmidt independence criterion used in our TUPI algorithm (Sec. 2.1) and in the visual attributes rank learning problem on which we evaluate our TUPI approach (Sec. 2.2), and a brief summary of our denoising algorithm with an algorithm description (Sec. 3). We also provide further experimental details and results including tests of statistical significance, parameter sensitivity, and simple failure cases (Sec. 4), plus additional future work ideas (Sec. 6). Lastly, we present details of our adaptations of related base-lines (Sec. 5): of Kim et al.’s algorithm [6] which only applies to one-dimensional test time features (Sec. 5.1); and of the CoConut algorithm proposed by Khamis and Lampert [5] (Sec. 5.2) for TUPI in visual attribute ranking problems, and our approaches to combine our algorithm with CoConut to exploit these two complementary algorithms for increased performance (Sec. 5.3). Some contents from the main paper are reproduced so that this document is self-contained.
BLOW-UP OF WAVES ON SINGULAR SPACETIMES WITH GENERIC SPATIAL METRICS

which take the form M = R+ ×M , where (M,g) is a three-dimensional closed (connected) Riemannian manifold of vanishing or negative constant sectional curvature, endowed with the metric g = −dt2 + a(t)2g for some smooth scale factor a. These arise by assuming the universe to be spatially homogeneous and isotropic, both of which are in accordance with what is physically observed on large scales. To derive a reasonable model of our universe and the Big Bang in particular, it is necessary to consider such FLRW spacetimes that solve the Einstein equations in presence of matter. The most common approach, and one of the simplest, models the universe as an irrotational ideal fluid with energy density ρ and pressure p, and assumes the linear equation of phase p = (γ−1)ρ for γ ∈ (2/3, 2]. For γ−1 ≥ 0, this can be interpreted as the square of the speed of sound cs within the fluid. The upper bound γ = 2 then corresponds to a stiff fluid, i.e. cs = c = 1, while spacetimes with γ = 2 3 do not admit a past singularity. An FLRW spacetime with constant spatial sectional curvature κ then solves the resulting Einstein-Euler-system if and only if the Friedman equations (see (2.2) and (2.3)) are satisfied.
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN

We present a method for implementing an Ef-ﬁcient Unitary Neural Network (EUNN) whose computational complexity is merely O (1) per parameter and has full tunability, from spanning part of unitary space to all of it. We apply the EUNN in Recurrent Neural Networks, and test its performance on the standard copying task and the MNIST digit recognition benchmark, ﬁnding that it signiﬁcantly outperforms a non-unitary RNN, an LSTM network, an exclusively partial space URNN and a projective URNN with comparable parameter numbers.
Simplex eues for Hot-Data Download

In cloud storage systems, hot data is usually replicated over multiple disks/servers in order to accommodate simultaneous access by multiple users as well as increase the fault tolerance of the system. Recent cloud storage research has proposed using availability codes, which is a special class of erasure codes, as a more storage-eecient way to store hot data. ese codes enable data recovery from multiple, small disjoint groups of servers. e number of the recovery groups is referred to as the availability and the size of each group as the locality of the code. Up till now, we have very limited knowledge on how code locality and availability aaect data access time. Data download from these systems involves multiple fork-join queues operating in-parallel, making the analysis of access time a very challenging problem. In this paper, we present an analysis of average data access time in storage systems employing simplex codes, which are an important, in certain sense optimal, class of availability codes. We generalize the analysis for codes with locality 2 and any degree of availability. Speciically, using a queueing theoretic approach, we derive bounds and approximations on the average response time for two diierent Poisson request arrival models. We also compare two scheduling strategies for reduced access time and load balancing.
The Spatial and Temporal Harmonic Balance Method for Obtaining Periodic Responses of a Nonlinear Partial Differential Equation With a Linear Complex Boundary Condition

The spatial and temporal harmonic balance (STHB) method is used to solve the periodic solution for a nonlinear partial di erential equation (PDE) demonstrated by a nonlinear string equation with a linear complex boundary condition, and stablity analysis is conducted for the periodic solutions using Hill's method. In order to avoid the integration procedure for discretizing the PDE to obtain the ordinary di erential equations (ODEs), spatial and temporal harmonic balance procedures are conducted simultaneously, which can be e ciently achieved by the discrete sine transform and the fast Fourier transform. An additional coordinate associated with the generalized coordinates of the trial functions for the spatial discretization is introduced to make the solution satisfy all boundary conditions, and a relationship of the additional coordinate and the generalized coordinates is developed and used in the STHB method so that the test functions can be the same to the trial functions. Jacobian matrix of the harmonic balanced residual is obtained analyti∗the author's current institute is Georgia Institute of Technology, Atlanta, Georgia, 30332 cally, which can be used in Newton method for solving the periodic response. The STHB method and Jacobian matrix make the calculation of the periodic solution for the nonlinear string with a linear spring boundary condition e cient and easy to be implemented by computer programs. The relationship between the Jacobian matrix and the system matrix of the linearized ODEs are developed, so that one can directly obtain the Toeplitz form of the system matrix, and Hill's method can be used to analyze the stability with the eigenvalues of the Toeplitz-form system matrix without the derivation of the ODEs. The frequency curve of the periodic solutions is obtained and their stability is indicated by the method in this work. INTRODUCTION A nonlinear partial di erential equation (PDE) can be solved by di erent methods, such as the perturbation methods [1], the nite element method, and Galerkin method [2]. To obtain the periodic response of the nonlinear PDE, harmonic balance method [3] and incremental harmonic balance (IHB) method [4]
International Journal of Renewable Energy Development

Pressure on alternative fuels and strict environmental regulations are driving a strategic shift in the efficient use of renewable biofuels. One of the promising biofuel candidates recently interested by scholars is a biological or organic additive that is added into diesel or biodiesel fuel to improve engine performance and reduce pollutant emissions. With efforts to improve efficiency and combustion quality in cylinders, combustion characteristics, flame structure and emission formation mechanism in compression ignition (CI) engines using blended fuel with organic additives have been studied on the effect of additive properties on the combustion behaviour. In this review, the physicochemical properties of typical organic additives such as ethers compounds and their effects on engine performance and emission characteristics have been discussed and evaluated based on conclusions of recent relevant literature. The results of the analysis revealed the prospect of using ether additives to improve combustion in cylinders and reduce pollutant emissions from CI engines. Obviously, the presence of higher oxygen content, lower viscosity and density, and higher cetane number resulted in a positive change in the combustion dynamics as well as a chain of mechanisms for the formation of pollutant precursors in the cylinder. Therefore, ether additives have a significant contribution to the sustainable energy strategy of the transportation sector in the next period when internal combustion engines still dominate in the competition for energy system choices equipped on vehicles.