Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival Analysis via Ordinary Differential Equations

Abstract This article introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, also enables the development of a widely applicable, scalable, and easy-to-implement procedure for estimation and inference. Specifically, the ODE modeling framework unifies many existing survival models, such as the proportional hazards model, the linear transformation model, the accelerated failure time model, and the time-varying coefficient model as special cases. The generality of the proposed framework serves as the foundation of a widely applicable estimation procedure. As an illustrative example, we develop a sieve maximum likelihood estimator for a general semiparametric class of ODE models. In comparison to existing estimation methods, the proposed procedure has advantages in terms of computational scalability and numerical stability. Moreover, to address unique theoretical challenges induced by the ODE notion, we establish a new general sieve M-theorem for bundled parameters and show that the proposed sieve estimator is consistent and asymptotically normal, and achieves the semiparametric efficiency bound. The finite sample performance of the proposed estimator is examined in simulation studies and a real-world data example. Supplementary materials for this article are available online.
RESEARCH ON LEAD UPTAKE AND TOLERANCE IN SIX PLANTS

To identify plants hypertolerant to heavy metal lead (Pb), the content and distribution of lead, total heavy metal translocation of lead, and the index of root tolerance of six dominant species established in the vicinity of a Pb/Zn mine tailings pond were researched in this study. The six plants studied were Vicia amoena, Melilotus suaveolens, Rumex acetosa, Elymus dahuricus, Medicago sativa, and Bidens maximowicziana. The six set concentrations of lead were 100, 200, 400, 800, 1000, and 1500 mg·L -1. The major results are as follows:1) The lead content in above-ground parts and roots of Bidens maximowicziana and Rumex acetosa increased linearly with Pb concentration. The lead content trends for the other four species increased with increased Pb concentration to a certain point and then declined. 2) The total heavy metal translocation of Bidens maximowicziana and Rumex acetosa, (10.5 mg and 9.9 mg, respectively) are higher than the other four plants. 3) The index of root tolerance of Bidens maximowicziana and Rumex acetosa are dramatically higher than for the other plants. We conclude that Bidens maximowicziana and Rumex acetosa, which are tolerant to Pb, could translocate lead from the root to the stems and leaves, and so they should be preferred agents for phytoremediation of lead polluted areas. Physiological activities of the other four plants are destroyed with Pb treatment of 1500 mg·L -1.
Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People

The adoption of deep learning in healthcare is hindered by their “black box” nature. In this paper, we explore the RETAIN architecture for the task of glucose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasets by comparing its statistical and clinical performances against two deep models and three models based on decision trees. We show that the RETAIN model offers a very good compromise between accuracy and interpretability, being almost as accurate as the LSTM and FCN models while remaining interpretable. We show the usefulness of its interpretable nature by analyzing the contribution of each variable to the final prediction. It revealed that signal values older than 1[Formula: see text]h are not used by the RETAIN model for 30[Formula: see text]min ahead of time prediction of glucose. Also, we show how the RETAIN model changes its behavior upon the arrival of an event such as carbohydrate intakes or insulin infusions. In particular, it showed that the patient’s state before the event is particularly important for the prediction. Overall the RETAIN model, thanks to its interpretability, seems to be a very promising model for regression or classification tasks in healthcare.
Horus: Using Sensor Fusion to Combine Infrastructure and On-board Sensing to Improve Autonomous Vehicle Safety

Studies predict that demand for autonomous vehicles will increase tenfold between 2019 and 2026. However, recent high-profile accidents have significantly impacted consumer confidence in this technology. The cause for many of these accidents can be traced back to the inability of these vehicles to correctly sense the impending danger. In response, manufacturers have been improving the already extensive on-vehicle sensor packages to ensure that the system always has access to the data necessary to ensure safe navigation. However, these sensor packages only provide a view from the vehicle's perspective and, as a result, autonomous vehicles still require frequent human intervention to ensure safety. To address this issue, I developed a system, called Horus, that combines on-vehicle and infrastructure-based sensors to provide a more complete view of the environment, including areas not visible from the vehicle. I built a small-scale experimental testbed as a proof of concept. My measurements of the impact of sensor failures showed that even short outages (1 second) at slow speeds (25 km/hr scaled velocity) prevents vehicles that rely on on-vehicle sensors from navigating properly. My experiments also showed that Horus dramatically improves driving safety and that the sensor fusion algorithm selected plays a significant role in the quality of the navigation. With just a pair of infrastructure sensors, Horus could tolerate sensors that fail 40% of the time and still navigate safely. These results are a promising first step towards safer autonomous vehicles.
Nonlinear computations in spiking neural networks through multiplicative synapses

The brain performs many nonlinear computations through intricate spiking neural networks (SNNs). How neural network dynamics relate to arbitrary computations under these constraints is still an open question. As a strong constraint, these networks are hypothesized to be robust to perturbations and use minimal energy. The theory of Spike Coding Networks (SCNs) derives the required connectivity and dynamics for both information representation and linear dynamical systems from first principles, and achieves robustness and efficiency. Nonlinear dynamical systems have thus far only been implemented in SCNs by filtering neural inputs through sets of nonlinear dendritic basis functions. While this approach works well, it relies on providing a rich enough basis set as well as supervised training of the connectivity weights. Another way to implement nonlinear computations is through multiplicatively interacting synapses. However, there is currently no principled way to implement such synapses in SCNs. Here, we extend the core SCN derivations to implement polynomial dynamical systems, from which also the need for such multiplicatively interacting synapses arises. We demonstrate our approach with a highly accurate Lorenz attractor implementation, as well as a second-order approximation of a double pendulum. We additionally demonstrate how to implement higher-order polynomials using sequential networks with only pair-wise synapses. Finally, we derive upper bounds and expected numbers of connections based on the sparsity of the underlying representation. Overall, our work provides an alternative way to directly implement nonlinear computations in spike coding networks, and expands our understanding about the potential functions of multiplicative synapses. Furthermore, due to the high accuracy and low energy usage of our approach, this work may be of interest for neuromorphic computing.
Energy-Efficient NOMA Multicasting System for 5G Cellular V2X Communications with Imperfect CSI

Vehicle-to-everything (V2X) is a modern vehicular technology that improves conventional vehicle systems in traffic and communications. V2X communications demand energyefficient and high-reliability networking because of massive vehicular connections and high mobility wireless channels. Nonorthogonal multiple access (NOMA) is a promising solution for 5G V2X services that intend to guarantee high reliability, quality-of-service (QoS) provisioning, and massive connectivity requirements. In V2X, it is vital to inspect imperfect CSI because the high mobility of vehicles leads to more channel estimation uncertainties. Unlike existing literatures, we propose energy-efficient roadside units (RSUs) assisted NOMA multicasting system for 5G cellular V2X communications, and investigate the energy-efficient power allocation problem. The proposed system multicast the information through low complexity optimal power allocation algorithms used under channel outage probability constraint of vehicles with imperfect CSI, QoS constraints of vehicles, and transmit power limits constraint of RSUs. The formulated problem with the channel outage probability constraint is a nonconvex probabilistic optimization problem. This problem is solved efficiently by converting the probabilistic problem through relaxation into a non-probabilistic problem.
Holomorphic duality for countable discrete groups

In 2008, the author proposed a version of duality theory for (not necessarily, Abelian) complex Lie groups, based on the idea of using the Arens-Michael envelope of topological algebra and having an advantage over existing theories in that the enclosing category in it consists of Hopf algebras in the classical sense. Recently these results were refined and corrected by O.Yu. Aristov. In this paper, we propose a generalization of this theory to the class of (not necessarily Abelian) countable discrete groups.
Application of The Wind-driven Model to a Sample of Tidal Disruption Events

An origin of the optical/ultraviolet (UV) radiation from tidal disruption events (TDEs) has recently been discussed for different scenarios, but observational support is generally lacking. In this Letter, we test the applicability of the “wind-driven model” for a sample of UV/optical TDEs. With the model, we aim to derive the physical properties of the optical/UV TDEs, such as mass-loss rates and characteristic radii. The model assumes optically thick continuous outflows like stellar winds, and one key question arises: how is the wind-launched radius connected to physical processes in TDEs? Here we propose one possibility: through a comparison between the escape velocities estimated from their black hole masses and the wind velocities estimated from observed line widths, the outflow is launched from the self-interaction radius (RSI) where the stellar debris stretched by the tidal force intersects. We show that the escape velocities at RSI are roughly consistent with the wind velocities. By applying the model to a sample of optical/UV TDE candidates, we find that explosive mass ejections (≳10 M⊙ yr−1) from RSI (∼1014 cm) can explain the observed properties of TDEs around peak luminosity. We also apply the same framework to a peculiar transient, AT2018cow. This model suggests that AT2018cow is likely a TDE induced by an intermediate-mass black hole (MBH ∼ 104 M⊙).
Second-Order Asymptotically Optimal Universal Outlying Sequence Detection with Reject Option

Motivated by practical machine learning applications, we revisit the outlying sequence detection problem (Li \emph{et al.}, TIT 2014) and derive fundamental limits of optimal detection when the reject option is allowed for outlying sequences. In outlying sequence detection (OSD) one is given multiple observed sequences, where most sequences are generated i.i. d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated according to anomalous distributions. In OSD, the nominal and anomalous distributions are \emph{unknown}. In this paper, we consider the case where there is a reject option for the OSD, i.e., reject the samples as insufficient for reliable outlying sequence detection (cf. Bartlett \emph{et al.}, JMLR 2008). We study the tradeoff among the probabilities of misclassification error, false alarm and false reject for tests that satisfy weak conditions on the rate of decrease of these error probabilities as a function of sequence length. We propose a second-order asymptotically optimal test which provides a finite sample approximation. We first consider the case of at most one outlying sequence and then generalize our results to multiple outlying sequences where each outlying sequence can follow a different anomalous distribution.
Generative Language Modeling for Automated Theorem Proving

We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community.
Hybrid thermal machines: Generalized thermodynamic resources for multitasking

Thermal machines perform useful tasks--such as producing work, cooling, or heating--by exchanging energy, and possibly additional conserved quantities such as particles, with reservoirs. Here we consider thermal machines that perform more than one useful task simultaneously, terming these "hybrid thermal machines". We outline their restrictions imposed by the laws of thermodynamics and we quantify their performance in terms of efficiencies. To illustrate their full potential, reservoirs that feature multiple conserved quantities, described by generalized Gibbs ensembles, are considered. A minimal model for a hybrid thermal machine is introduced, featuring three reservoirs and two conserved quantities, e.g., energy and particle number. This model can be readily implemented in a thermoelectric setup based on quantum dots, and hybrid regimes are accessible considering realistic parameters.
Free-stream preserving metrics and Jacobian for the conservative finite difference method on curvilinear grids

For the compressible flows simulations, the conservative finite difference based on the upwind schemes, i.e. the linear upwind and WENO, is widely used for their simplicity and conservative property. However, this method loses the geometric conservation law (GCL) identity due to the upwind dissipation when applied on the curvilinear grids. In this paper, we suggest a free-stream preserving metrics and Jacobian in the upwind dissipation to maintain the free-stream preserving identity. This technique avoids destroying the accuracy and the standard forms of the upwind schemes as far as possible. Therefore, this technique is convenient to operate in conservative finite difference method. Some verifications are conducted to show the accuracy in the smooth flow filed and the robustness in the discontinuous regions of the present free-stream preserving method, such as the isotropic vortex problem, the double Mach reflection problem, the transonic flow past NACA0012 airfoil and ONERA M6 wing, etc.
Title: Recent evolution in Rattus norvegicus is shaped by

1 The brown rat, Rattus norvegicus, is both a notorious pest and a 2 frequently used model in biomedical research. By analysing genome 3 sequences of 12 wild-caught brown rats from their ancestral range in 4 NE China, along with the sequence of a black rat, R. rattus, we in5 vestigate the selective and demographic forces shaping variation in the 6 genome. We estimate that the recent effective population size (Ne) 7 of this species = 1.24 × 10, based on silent site diversity. We com8 pare patterns of diversity in these genomes with patterns in multi9 ple genome sequences of the house mouse (Mus musculus castaneus), 10 which has a much larger Ne. This reveals an important role for varia11 tion in the strength of genetic drift in mammalian genome evolution. 12 By a Pairwise Sequentially Markovian Coalescent (PSMC) analysis of 13 demographic history, we infer that there has been a recent population 14 size bottleneck in wild rats, which we date to approximately 20,000 15 years ago. Consistent with this, wild rat populations have experienced 16 an increased flux of mildly deleterious mutations, which segregate at 17 higher frequencies in protein-coding genes and conserved noncoding 18 elements (CNEs). This leads to negative estimates of the rate of adap19 tive evolution (α) in proteins and CNEs, a result which we discuss in 20 relation to the strongly positive estimates observed in wild house mice. 21 As a consequence of the population bottleneck, wild rats also show a 22 markedly slower decay of linkage disequilibrium with physical distance 23 than wild house mice. 24
The Multipurpose Two-Dimensional Servo Systems and Their Applications

The multipurpose method of two-dimensional positioning control problem solving enabling to save energy and equipment recourses in the process optimum rapidity assurance conditions is proposed and investigated in this article. It is shown that these conditions would be obtained ensuring optimum rapidity of positioning in bigger displacement direction and the straight-line movement trajectory in the two-dimensional space during the whole positioning process. The theoretical aspects of stability and accuracy of the proposed method with taking into account non-symmetry of its structure are investigated. The multipurpose control method of flat surfaces scanning systems permitting saving of energy and equipment resources without diminishing of process rapidity and quality is also proposed. The example of multipurpose two-dimensional control method application in the flat ele? trographical carrier surfaces quality investigation system is discussed. In this system the same two-dimensional servo system fulfils both the flat scanning and the two-dimensional positioning system functions.
JeDI: The Jellyfish Database Initiative

JeDI is a scientifically-coordinated global jellyfish database housed at the National Center for Ecological Analysis and Synthesis (NCEAS) , currently holding over 476,000 quantitative, categorical, presence-absence and presence only records on global jellyfish populations spanning the past two centuries. JeDI has been designed as open-access database for all researchers, media and public to use as a current and future research tool and a data hub for general information on jellyfish populations. With this resource, anyone can use JeDI to address questions about the spatial and temporal extent of jellyfish populations at local, regional and global scales, and the potential implications for ecosystem services and biogeochemical processes.
On Training Neural Networks with Mixed Integer Programming

Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NN). However little research has gone into training NNs with solvers. State of the art methods to train NNs are typically gradient-based and require significant data, computation on GPUs and extensive hyper-parameter tuning. In contrast, training with MIP solvers should not require GPUs or hyper-parameter tuning but can likely not handle large amounts of data. This work builds on recent advances that train binarized NNs using MIP solvers. We go beyond current work by formulating new MIP models to increase the amount of data that can be used and to train non-binary integer-valued networks. Our results show that comparable results to using gradient descent can be achieved when minimal data is available.
Quantum Walks

We present a novel application of the HHL (Harrow-Hassidim-Lloyd) algorithm --- a quantum algorithm solving systems of linear equations --- in solving an open problem about quantum walks, namely computing hitting (or absorption) probabilities of a general (not only Hadamard) one-dimensional quantum walks with two absorbing boundaries. This is achieved by a simple observation that the problem of computing hitting probabilities of quantum walks can be reduced to inverting a matrix. Then a quantum algorithm with the HHL algorithm as a subroutine is developed for solving the problem, which is faster than the known classical algorithms by numerical experiments.
The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data

Evaluating relative changes leads to additional insights which would remain hidden when only evaluating absolute changes. We analyze a dataset describing mobility of mobile phones in Austria before, during COVID-19 lock-down measures until recent. By applying compositional data analysis we show that formerly hidden information becomes available: we see that the elderly population groups increase relative mobility and that the younger groups especially on weekends also do not decrease their mobility as much as the others.
New method for rapidly estimating population densities of the concealed wood‐borer Monochamus alternatus (Coleoptera: Cerambycidae) in the field

To rapidly estimate pine sawyer, Monochamus alternatus Hope, population densities in forests, the vertical distributions of M. alternatus oviposition sites and larvae on infested Masson pines (Pinus massoniana Lamb.) were studied. Results showed that the number of oviposition sites on sections of trunks between 0 and 2 m above ground was significantly greater than on sections of trunk above 2 m, and the vertical distribution had a significant logarithmic relationship with trunk height. The larval number of M. alternatus on dead infested trees had a significant difference among heights of trunks. Sections on trunks at 2–4 m usually contained the largest number of M. alternatus larvae, while the number of larvae on trunks above 10 m declined significantly, as well as in the 1 m section of trunk at the base. The vertical distribution of M. alternatus larvae on dead infested pines showed a distinct parabolic relationship with trunk height. The number of oviposition sites of M. alternatus on infested Masson pine trunks revealed a significant exponential relationship with the diameter at breast height (DBH) of trees. A significant positive linear relationship also was observed between M. alternatus larval number and DBH on the host pine trees, as well as between the numbers of larvae and oviposition sites on an individual tree. The total number of larvae in an infested tree could be calculated easily using an established equation, through counting the number of oviposition sites at 3–4 m of trunk aboveground. This study developed a practical method for rapid estimation of M. alternatus populations.