Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple nonparametric two-sample test for the distribution function of event time with interval censored data

For the setting of interval censored data in which the event time is not exactly observed but known to be inside a random interval, a simple nonparametric two-sample test, based on empirical estimates of smooth functionals of the distribution function of event time, is developed to compare the distribution functions of event time for two populations. Monte Carlo simulation studies on Weibull distributions show that this test performs quite well. A real data set from an AIDS clinical trial is used to illustrate the test.
Asymptotic computation of Greeks under a stochastic volatility model

We study asymptotic expansion formulae for numerical computation of Greeks (i.e. sensitivity) in finance. Our approach is based on the integration-by-parts formula of the Malliavin calculus. We propose asymptotic expansion of Greeks for a stochastic volatility model using the Greeks formula of the Black-Scholes model. A singular perturbation method is applied to derive asymptotic Greeks formulae. We also provide numerical simulation of our method and compare it to the Monte Carlo finite difference approach.
Acceleration Measurement-Based Disturbance Observer Control for a Belt-Drive Servo Instrumentation

An acceleration measurement-based disturbance observer (DOB) control method is proposed to capture and eliminate transient and dramatic dynamic disturbance torque in the flexible belt-drive servo instrumentation. The essence of this method is to incorporate the acceleration difference between the motor side and the load side to implement a DOB in the dual-rate control loop. The acceleration difference is sensitive to the rate variation caused by the motor-load large span distribution. The input signal of the motor inner loop with the high bandwidth is equivalently processed to obtain the estimated motor-side acceleration. However, the observed acceleration disturbance suffers from low-frequency drift and noise accumulation, resulting in system instability under integral characteristics. Therefore the $H_{\infty }$ principle is used to ensure stronger stability and the compensator is bandpass-corrected to enhance the anti-disturbance performance in a limited bandwidth. The proposed method is experimentally validated to effectively suppress the disturbance torque after obtaining an optimal trade-off between robust stability and anti-disturbance. The error spikes and buffeting in the flexible transmission chain are further reduced compared with the conventional methods.
Accommodation to wavefront vergence: adapting ‘averse channels’

Purpose: Accommodation responds to wavefront vergence, but the mechanisms for vergence detection are unknown. One possibility is that accommodation responds to the angle of incidence of light at edges blurred by defocus. ‘Averse channels’ that sample light from opposite sides of the pupil were hypothesized by Makous (1968, 1977). Makous named the phenomenon a “transient” Stiles-Crawford effect because he found that there was a reduction in sensitivity to light entering one side of the pupil that lasts for a short period of time. Such ‘averse channels’ that sample modulation across the pupil are a possible mechanism for detecting the sign of defocus. The purpose of the present experiment is to determine whether such ‘averse channels’ can operate separately of each other to specify the sign of defocus for accommodation. Method: Accommodation was monitored continuously while subjects viewed a vertical monochromatic (548nm) luminance edge (1.0 contrast) that stepped either to a far or near direction in a Badal optometer. Various levels of adapting field were used to reduce the contrast of the edge (0.48, 0.36, 0.26, or 0.17 contrast) to determine the contrast threshold for accommodation. In a final experiment, an adapting field entered the eye through the nasal or temporal side of the pupil, to selectively adapt nasally or temporally tuned ‘channels’, while the target stepped randomly toward or away from the eye. The orientation of the vertical edge was either bright on the right side or bright on the left. Results: In a preliminary experiment, only five out of twenty-six subjects showed reliable and consistent responses, with gains >0.5 for both positive and negative step change in vergence. The contrast threshold for accommodation to step changes in target vergence was approximately 26%. Data from three subjects who accommodated reliably to both directions of step changes in vergence do not support the claim that ‘channels’ sample light separately from opposite sides of the pupil to determine the sign of defocus. Conclusion: Potential factors that account for the poor accommodative responses in the present study include: 1) the target was illuminated with monochromatic light, 2) the target was a simplified stimulus in which the blur was in only one direction (one spatial phase), 3) the target was viewed monocularly, 4) the 3mm artificial pupil increased depth-of-focus and removed the normal dynamic behavior of the pupil, 5) our subjects were untrained. The negative results suggest alternate hypotheses: 1)‘averse channels’ work in conjunction with each other, not separately, to detect vergence, 2) ‘averse channels’ can function separately to detect vergence, but the signal was undetectable using the current method, and 3) ‘averse channels’ do not mediate vergence detection.
Phase retrieval from modulus data

Phase retrieval implies extraction of the phase of a complex signal f from its modulus |f|. We give examples where an additional constraint is imposed: knowledge of the modulus of F, the Fourier transform of f. The retrieval is accomplished by computer processing of samples of |f| and |F|. The problems of noisy data and nonuniqueness are addressed.
4D portrait generation based on neural radiance field and facial expression similarity

This paper introduces a method to generate 4D portrait of a person that can be played over a long period. 4D portrait is free-viewpoint video of a person with temporal changes in facial expression. In our proposed method, the parameters that represent person’s facial expressions and head poses are obtained from the video captured by a monocular RGB camera with a continuously moving viewpoint. A neural radiance field (NeRF) is trained from the captured video and estimated parameters. Using the radiance field, 4D portrait is generated based on the similarity of the person's facial expressions.
Improving Separation Performance of Wireless Communication Signals with Antenna Angle Adjustment

Blind source separation (BSS) is a research hotspot in signal processing and neural networks. Based on its inherent advantages, BSS has become a promising approach to realize spectrum utilization and interference cancellation simultaneously. However, BSS encounters the robustness problems in practice. In order to improve the blind separation performance of wireless communication signals, we propose a BSS method with antenna angle adjustment at the receive end. The main idea is to change the mixing situation by adjusting the angle based on the characteristic that the antenna gain is affected by the angle. According to the position information of transmitted signals and receiving antennas, we can find the optimal angle where the output signal to interference ratio (SIR) achieves the maximum. The simulation results show that the proposed method can improve the separation performance significantly.
Population Genetic Structure of a Rare Butterfly in a Fragmented South Florida Ecosystem

Simple Summary Biodiversity loss at both species and genetic scales has been exacerbated in recent decades due to habitat fragmentation and destruction. These losses have been especially devastating to insects. Butterflies are charismatic microfauna that offer an excellent opportunity to evaluate the consequences of how fragmentation disrupts gene flow between population segments and may serve as indicators for other less visible insect species. The Florida duskywing butterfly is a rare species that is dependent on a critically endangered habitat, the Florida pine rocklands. We ran a series of genetic studies, akin to DNA fingerprinting, to assess the genetic health of the Florida duskywing as well as to evaluate the level of connectivity between the remaining populations. Despite declining population sizes, we found that populations still hold a unique genetic diversity. Genetic results also showed that these butterflies could be clustered into mainland and Florida Keys population groups, which were connected by a moderate gene flow. Additionally, no sampled individuals tested positive for a bacterial infection that has harmed other butterfly populations, meaning that individuals can be moved between populations without major concern for bacterial spread. In summary, this work supports translocations of individuals between populations to (1) facilitate an enhanced gene flow and (2) reinforce small population sizes. Abstract We investigated the genetic structure and diversity between populations of a rare butterfly, the Florida duskywing (Ephyriades brunnea floridensis E. Bell and W. Comstock, 1948) (Lepidoptera: Hesperiidae) across a network of South Florida pine rockland habitat fragments. Based on 81 individuals from seven populations and using multiple polymorphic microsatellite loci, our analyses support the presence of mainland Florida (peninsular) and Florida Keys (island) population groupings, with a moderate, asymmetrical gene flow connecting them, and the presence of private alleles providing unique identities to each. We additionally found that despite a prevalence in many Lepidoptera, the presence of Wolbachia was not identified in any of the samples screened. Our findings can be used to inform conservation and recovery decisions, including population monitoring, organism translocation, and priority areas for management, restoration or stepping-stone creation to help maintain the complex genetic structure of separate populations.
Non‐Gaussian state space models in decomposition of ice core time series in long and short time‐scales

Statistical modelling of six time series of geological ice core chemical data from Greenland is discussed. We decompose the total variation into long time‐scale (trend) and short time‐scale variations (fluctuations around the trend), and a pure noise component. Too heavy tails of the short‐term variation makes a standard time‐invariant linear Gaussian model inadequate. We try non‐Gaussian state space models, which can be efficiently approximated by time‐dependent Gaussian models. In essence, these time‐dependent Gaussian models result in a local smoothing, in contrast to the global smoothing provided by the time‐invariant model. To describe the mechanism of this local smoothing, we utilise the concept of a local variance function derived from a heavy‐tailed density. The time‐dependent error variance expresses the uncertainty about the dynamical development of the model state, and it controls the influence of observations on the estimates of the model state components. The great advantage of the derived time‐dependent Gaussian model is that the Kalman filter and the Kalman smoother can be used as efficient computational tools for performing the variation decomposition. One of the main objectives of the study is to investigate how the distributional assumption on the model error component of the short time‐scale variation affects the decomposition. Copyright © 2009 John Wiley & Sons, Ltd.
Digital Text/Tool Selection and Integration: What Professors Teach

The DigiLit Framework suggests criteria for digital text and tool selection (content accuracy, intuitiveness, interactivity, quality) and integration (model a literacy skill or strategy, guide a literacy skill or strategy, model digital feature use, guide digital feature use) in literacy lessons. Using survey research, we explored which DigiLit criteria literacy professors prepared preservice teachers to use in K-12 settings. Participants included 199 literacy professors (194 from USA and one each from Australia, Canada, Caribbean, Middle East and Europe). We used a multivariate outcome logit/probit model to analyze how this was related to (a) professor characteristics, (b) institution characteristics, and (c) time. Findings showed that certain professor characteristics (e.g., interest in integrating technology, being knowledgeable about digital literacies), institution characteristics (e.g., access to equipment, professional development, technical support, incentives), and time to plan and practice integration were related to literacy professors’ increased preparation of preservice teachers to use digital text or tool selection and integration. These findings provide specific ways to improve literacy teacher preparation by providing specific kinds of supports.
Overexpression of TREM1 is Associated with the Immune-Suppressive Microenvironment and Unfavorable Prognosis in Pan-Cancer

Background Triggering receptors expressed by myeloid cells-1 (TREM1) is a receptor belonging to the immunoglobulin superfamily and plays an important role in pro-inflammation in acute and chronic inflammatory disorders. However, the understanding of the immunomodulatory roles of TREM1 in the tumor microenvironment remains incomplete. Methods The expression patterns of TREM1 mRNA in tumors and adjacent normal tissues were compared by analyzing data obtained from the Genotype-Tissue Expression and The Cancer Genome Atlas datasets. Survival analysis was performed to determine the prognostic value of TREM1. Functional enrichment analysis was applied to decipher the discrepancy in biological processes between high- and low-TREM1 groups across various cancers. The correlation between TREM1 and immune cell infiltration determined by using multiple algorithms was evaluated with the Pearson method. Four independent immunotherapy cohorts were adopted to validate the role of TREM1 as a biomarker. Results TREM1 was elevated in most cancers as verified with clinical samples. Overexpression of TREM1 was linked with undesirable prognosis in patients. Further analysis revealed that TREM1 was positively correlated with immune response, pro-tumor pathways, and myeloid cell infiltration, while being negatively correlated with CD8+ T cell (including infiltration level and biological processes). Concordantly, tumors with high TREM1 levels were more resistant to immunotherapy. Through connective map analysis, therapeutically potential compounds like tozasertib and TPCA-1 were identified, which can be used synergistically with immunotherapy to improve the poor prognosis of patients with high TREM1 levels. Conclusion Through a systematic and comprehensive pan-cancer analysis, we demonstrated that overexpression of TREM1 in tumors correlated closely with unfavorable outcome, infiltration of immune-suppressive cells, and immune regulation, which highlights its potential use as a tumor prognostic biomarker and a novel target for immunotherapy.
Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we propose a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.
Quasi-geostrophic modes in the Earth's uid core with an outer

Seismic waves sensitive to the outermost part of the Earth’s liquid core seem to be aected by a stably stratied layer at the core-mantle boundary. Such a layer could have an observable signature in both long-term and short-term variations of the magnetic eld of the Earth, which are used to probe the ow at the top of the core. Indeed, with the recent SWARM mission, it seems reasonable to be able to identify waves propagating in the core with period of several months, which may play an important role in the large-scale dynamics. In this paper, we characterize the inuence
Millimeter-wave spatial splitting and combining for use in gap-waveguide-integrated grid amplifiers and antenna arrays

Conventional power splitters and combiners employing substrate-based transmission lines suffer from both conductor and dielectric losses, in particular at mm-wave frequencies. These losses increase when the number of channels increase, thereby reducing the efficiency even further. The primary aim the thesis work is to minimize these losses by spatially power combining (or splitting) the electromagnetic fields. This is will be done in a planar fashion and be packaged at the same time using gap-waveguide technology. Spatial power combining techniques are also used in grid amplifiers. A gap-waveguide packaged and integrated grid amplifier is therefore an interesting application to consider. Indeed, conventional grid amplifiers typically employ different feed networks including both dielectric lenses and polarizers, which are bulky and lossy and suffer from DC biasing problems. To overcome these problems, we propose to split and combine the power through a reflector-wall-based back-to-back gap waveguide structure. First, an offset reflector wall has been designed and simulated in receive mode in which the reflector aperture is illuminated with a single large port and fields are analyzed at the focal point. The horn feed is designed on the basis of an encircled power analysis in the focal plane. The reflector wall is then illuminated by the designed feed and the resultant planar fields from this reflector are fed back into a second feed making up the back-toback structure. The fields emanating from the offset reflector wall are equal in phase but tapered in magnitude. In order to more gradually taper the field leading to a more uniform amplitude distribution a slightly larger offset reflector wall has been designed. The planar waterfronts of the fields of the reflector couple into an array of waveguides using customized ridge gap-waveguide transitions. A fork shaped transition has been designed so that the edges of the walls of the waveguides do not appear abruptly in front of the planar fields. This ensures that the planar field divides and propagates into the waveguides with minimal reflection. This work is important because it represents a low-loss power splitting and combining solution of mm-wave systems with S11 less than -10 dB that covers the frequency range from 74 GHz to 110 GHz. It also forms the basis for a mm-wave grid amplifier in gap waveguide technology.
Modeling uncertainty of large flexible structures with unknown modal parameters

A procedure for control-oriented modeling of uncertainty is proposed for large flexible structures. Techniques on quantification of errors in modal truncated nominal models are developed for the case where a finite number of upper and lower bounds of the unknown modal parameters are given. A feasible set of systems matching the conditions is introduced, and then error bounds covering the feasible set are established in the frequency domain. The bounds are easily checked using linear programming for any user-specified frequency. The feasibility of the proposed scheme is illustrated by numerical study on an ideal flexible beam example.
Discrimination of intracerebral hematoma and intraventricular hemorrhage in initial CT images using hierarchical region extraction method

Intracerebral hematoma (ICH) is a blood clot that forms when a blood vessel in the brain ruptures for some reason and the spilled blood coagulates. ICH has a high morbidity and mortality rate, accounting for approximately 10% of all strokes. Manual segmentation of ICH in head CT images is very complicated, time consuming, and troublesome. When ICH perforates the ventricular wall and blood flows into the ventricle, there is little difference in CT value between ICH and intraventricular hemorrhage (IVH), and the boundary between them is unclear. Convolutional neural network (CNN) has proven to be a reliable method in the field of image recognition. In addition, quantification of ICH may aid in decision making in ICH treatment. In this study, we introduce CNN in a stepwise manner to differentiate ICH and IVH in the process and extract ICH regions. The results in 18 stroke patients show that our method is promising in the extraction of ICH regions with an accuracy of 75.2%.
Measurement of Thermal Conductivity by Utilization of the Peltier Effect

A special technique for the accurate measurement of thermal conductivity is discussed. The method involves use of the Peltier heat to maintain a temperature gradient along the specimen. Straightforward measurements allow calculation of the absolute value of the thermoelectric power, thermal conductivity, and electrical resistivity. An especially useful feature of the method is that the thermoelectric figure of merit is given in terms of the ratio of two voltages. The theory is presented for the case in which the radiative heat transfer is important. The method has been tested experimentally at 300°K only, but analysis suggests that accurate measurements of thermal conductivity can be made by this technique on low thermal conductivity materials of small dimensions up to 1000°K.
Research on application of evidence theory in uncertainty reasoning

Use of the basic probability assignment function,the belief function and the plausibility function in evidence theory to describe and deal with the uncertainty of knowledge. This paper advised a specific probability assignment function and a new combination rule,and established a model of uncertain reasoning based on it. The example shows that the model can effectively measure the uncertainty of the final conclusions.