Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting

Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988–1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely.
Malnutrition leads to the progression of coronary artery calcification in hemodialysis patients

Background Malnutrition is considered a risk factor for cardiovascular disease in patients with chronic kidney disease. However, no in vivo studies have reported on using optical coherence tomography to evaluate the effect of nutritional status on coronary atherosclerosis in hemodialysis patients. We aimed to conduct a detailed analysis of the effect of nutritional status on the coronary arteries in hemodialysis patients. Methods Among 64 hemodialysis patients who underwent percutaneous coronary interventions, 41 that underwent optical coherence tomography imaging were included in this study. And, among them, 24 patients that could also be evaluated using OCT also at the 6-month follow-up were included in this study. The patients were divided into two groups based on nutritional evaluation using the geriatric nutritional risk index. Culprit and non-culprit lesions were evaluated at baseline and after 6 months. Results In the culprit lesions at baseline, the length of the lipid plaque was significantly smaller in the malnutrition group. In contrast, the thickness and length of the calcified plaque and the angle of the calcified nodule were significantly larger (each p < 0.01). In the non-culprit lesions, the 6-month change in the angle of the calcified plaque was significantly greater in the malnutrition group (p = 0.02). The significant factors that affected the change in the angle of calcification were "malnutrition at geriatric nutritional risk index" [odds ratio, 8.17; 95% confidence interval, 1.79 to 37.33; p < 0.01] and "serum phosphorus level" (odds ratio, 3.73; 95% confidence interval, 1.42 to 9.81; p < 0.01). Conclusions Appropriate management of nutritional status is crucial for suppressing the progression of coronary artery disease in hemodialysis patients.
Positioning accuracy of a large‐aperture hydrophone array for sperm whale research

Source positioning accuracy of a large‐aperture (1 km) hydrophone array was investigated, using 2‐D and 3‐D algorithms. The array was used for positioning and SL determination of sperm whales off the coast of Northern Norway during the summers of 1997 and 1998 (see Mo/hl, this volume). Positioning accuracy is dependent on mainly four factors: (1) positioning accuracy of the hydrophones, (2) variation in sound velocity of the medium, (3) inaccuracies in the measurements of the time‐of‐arrival differences, and (4) choice of algorithm (errors associated with geometric over‐simplification). Errors introduced by the factors (1) and (3) had small effects compared with errors introduced by the factors (2) and (4). The total estimated error varied considerably in different areas covered by the array and for different array configurations. Sound velocity showed only modest variation with depth, except for a −0.2‐m/s per meter gradient in the 0–100‐m depth interval caused by summer heating of the surface water. For.
Terahertz nonlinear optics using intra‐excitonic quantum well transitions: Sideband generation and AC Stark splitting

We present experiments investigating nonlinear optics in the terahertz (THz) range related to the 1s–2p intra‐excitonic heavy‐hole transition in GaAs/AlGaAs multiquantum wells (MQWs). Driven by intense THz fields of the Dresden free‐electron laser the system exhibits two different phenomena: (i) we observe efficient near‐infrared (NIR) sideband generation as a perturbative effect in the transmission of a narrowband NIR laser, and (ii) we present unambiguous evidence for the intra‐excitonic Autler–Townes splitting, a non‐perturbative effect.
High-Energy Gamma Rays from PKS 1406–076 and the Observation of Correlated Gamma-Ray and Optical Emission

We report simultaneous observations of the optically violent variable (OVV) quasar PKS 1406-076 in the optical and gamma-ray regime. Gamma-ray emission is detected with very high significance. Both the gamma-ray and optical properties of PKS 1406-076 are typical for the group of gamma-bright active galactic nuclei detected by EGRET. During 1993 January the source was unusually bright in both frequency ranges. During this period we observed a rapid, symmetrical flare in the optical wavelength range which lasted for about 4 days. EGRET detected a similar rapid flare peaking about 22 hr after the optical outburst. Because such rapid flares are rare events, it is likely that the outbursts in the two frequency regimes are correlated. Correlated outbursts with similar profiles and a lag between the optical and gamma-ray maxima of 22 hr are inconsistent with simple versions of models suggested for the emission of high-energy radiation from blazars.
Creation of a Digital Rig Vedic Index (Anukramani) for Computational Linguistic Tasks

Rig Veda, the oldest text in Vedic Sanskrit, is a collection of hymns addressed to various devat¯a s. We present an index of Rig Vedic verses along with the respective devat¯a , r.s. i and chandas which is, in short, a digitized form of the well known Rig Vedic Anukraman. ¯i . We then build several deep learning based language models from the corpus. The models are tested on a downstream classiﬁcation task that predicts the devat¯a associated with a given verse. The six classiﬁers we test are based on FastText, ELMo, AL-BERT, Word2Vec, GloVe and RoBERTa language models pretrained on the GRETIL data. We ﬁnd that RoBERTa outperforms the other models on this task and achieves an accuracy of 77.3%.
Pyroelectric energy conversion: hysteresis loss and temperature sensitivity of a ferroelectric material

Measurements of polarization versus applied electric field have been made on specimens of lead zirconate modified with Sn4+ and Ti4+ as a function of temperature. These data were analyzed to estimate the amount of hysteretic loss which may be encountered in heat‐to‐electrical conversion using this ferroelectric. The hysteretic loss is determined to be small (8% to 16% of the total electrical energy). Also analyzed was the sensitivity of the electrical energy output to the operating temperature. Eighty percent of the maximum energy production is estimated to be available over a 16 °C range of conversion cycle mean temperatures (for cycles with 25 °C temperature excursions).
DFS-GAN: stabilizing training of generative adversarial networks through discarding fake samples

Abstract. Generative adversarial networks (GANs) are generative models based on game theory. Because the relationship between generator and discriminator must be carefully adjusted during the training process, it is difficult to get stable training. Although some solutions are proposed to alleviate this issue, it is still necessary to discuss how to improve the stability of GANs. We propose a GAN we call the discarding fake samples (DFS)-GAN. During the training process, some generated samples are unable to fool the discriminator and provide a relatively invalid gradient for the discriminator. So, in the stabilized discriminator module (SDM), we discard the fake but easily discriminated samples. At the same time, we propose a new loss function, SGAN-gradient penalty 1. We explain the rationale of SDM and our loss function from a Bayesian decision perspective. We inferred the best number of discarded fake samples and verified the selected parameters’ effectiveness by experiments. The Fréchet inception distance (FID) value of DFS-GAN is 14.57 ± 0.19 on Canadian Institute for Advanced Research-10 (CIFAR-10), 20.87 ± 0.33 on CIFAR-100, and 92.42 ± 0.43 on ImageNet, which is lower than that of the current optimal method. Moreover, SDM module can be used in many GANs to decrease the FID value if their loss functions fit.
Visual-inertial odometry based on tightly-coupled encoder

In order to solve the problem of inaccurate scale optimization of visual inertial odometer (VIO) algorithm under uniform motion, this paper presents a Visual-Inertial-Encoder Tightly-Coupled Odometry (VIETO) algorithm, and describes VIETO initialization as an optimal estimation problem in the sense of maximum-a-posteriori (MAP) estimation. Firstly, the pre-integration theory of encoder is introduced in this paper so that the scale and velocity information can be obtained by using the encoder to measure the pre-integration during the visual MAP estimation, which provides a good initial value for the optimal estimation of IMU parameters. Secondly, the encoder error term and random plane constraint are introduced into the visual inertia optimization framework to further constrain pose estimation. Finally, we apply VIETO to the monocular inertial ORB-SLAM3 system. By comparing the algorithm with other similar algorithms on the DS dataset, the results prove the effectiveness of the system.
Object identification-based stall detection and stall legitimacy analysis for traffic patterns

Abstract. Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth.
A compact compressive hyperspectral imaging system based on metasurface

Traditional hyperspectral imagers rely on scanning either the spectral or spatial dimension of the hyperspectral cube with spectral filters or line-scanning which can be time consuming and generally require precise moving parts, increasing the complexity. More recently, snapshot techniques have emerged, enabling capture of the full hyperspectral datacube in a single shot. However, some types of these snapshot system are bulky and complicated, which is difficult to apply to the real world. Therefore, this paper proposes a compact snapshot hyperspectral imaging system based on compressive theory, which consists of the imaging lens, light splitter, micro lens array, a metasurface-covered sensor and an RGB camera. The light of the object first passes through the imaging lens, and then a splitter divides the light equally into two directions. The light in one direction pass through the microlens array and then the light modulation is achieved by using a metasurface on the imaging sensor. Meanwhile, the light in another direction is received directly by an RGB camera. This system has the following advantages: first, the metasurface supercell can be well designed and arranged to optimize the transfer matrix of the system; second, the microlens array guarantee that the light incident on the metasurface at a small angle, which eliminate the transmittance error introduced by the incidence angle; third, the RGB camera is able to provide side information and help to ease the reconstruction.
An efficient laplace transform solution for multiaquifer systems

An efficient numerical algorithm is developed for groundwater flow in multiaquifer systems. The method is based on Herrera's formulation which eliminates the need of explicitly modeling aquitards. Laplace transform is applied to the governing integrodifferential equations, which removes not only the time dependence in the solution system, but also the integral expression. The final system is a set of nontransient, linear partial differential equations in two spatial dimensions. A regular finite difference method is used for its solution. The solution in the Laplace transform domain is then inverted to the time domain using either a slow or a quick scheme, depending on the requirement of speed and accuracy. Examples of a two-aquifer one-aquitard system involving radial and regional geometries are examimed.
Thickness- and temperature-dependent magnetodynamic properties of yttrium iron garnet thin films

The magnetodynamical properties of nanometer-thick yttrium iron garnet films are studied using ferromagnetic resonance as a function of temperature. The films were grown on gadolinium gallium garnet substrates by pulsed laser deposition. First, we found that the damping coefficient increases as the temperature increases for different film thicknesses. Second, we found two different dependencies of the damping on film thickness: at room temperature, the damping coefficient increases as the film thickness decreases, while at T = 8 K, we find the damping to depend only weakly on the thickness. We attribute this behavior to an enhancement of the relaxation of the magnetization by impurities or defects at the surfaces.
Clinical implementation of advanced control in anaesthesia

A multi-sensor fusion system for monitoring and control of depth of anaesthesia in the operating theatre is described. Two types of anaesthesia measures are considered, the first being based on auditory evoked responses, while the second measure is based on cardiovascular system signals. The first measure is based on analysis of the signal using wavelets and a neuro-fuzzy system, while the second measure is based on analysis of the measurements using a rule-based fuzzy logic system. The two measures are combined to give a final level of depth of anaesthesia using another rule-based fuzzy logic system. The depth of anaesthesia level is fed back to a target controlled infusion system (TCI) to control the infusion of the drug propofol for maintaining a constant level of depth of anaesthesia.
Performance Modeling for Short-Term Cache Allocation

Short-term cache allocation grants and then revokes access to processor cache lines dynamically. For online services, short-term allocation can speed up targeted query executions and free up cache lines reserved, but normally not needed, for performance. However, in collocated settings, short-term allocation can increase cache contention, slowing down collocated query executions. To offset slowdowns, collocated services may request short-term allocation more often, making the problem worse. Short-term allocation policies manage which queries receive cache allocations and when. In collocated settings, these policies should balance targeted query speedups against slowdowns caused by recurring cache contention. We present a model-driven approach that (1) predicts response time under a given policy, (2) explores competing policies and (3) chooses policies that yield low response time for all collocated services. Our approach profiles cache usage offline, characterizes the effects of cache allocation policies using deep learning techniques and devises novel performance models for short-term allocation with online services. We tested our approach using data processing, cloud, and high-performance computing benchmarks collocated on Intel processors equipped with Cache Allocation Technology. Our models predicted median response time with 11% absolute percent error. Short-term allocation policies found using our approach out performed state-of-the-art shared cache allocation policies by 1.2–2.3X.
Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing Systems

We study the multi-machine task scheduling problem in an integrated serverless edge and cloud computing system, where tasks can be scheduled locally on edge processors or offloaded to cloud servers, with the objective of minimizing the makespan, i.e., the total time to finish all tasks. The system is semi-online, where the edge processing delays of the tasks are known as priori, but the cloud processing delays remain unknown due to the uncertainty introduced by uploading and loading delay (loading the software environment). The problem is NP-hard in nature, and therefore we resort to approximation schemes and propose a novel algorithm named multi-machine with restart scheduling (MRS). MRS utilizes task restart, where a task that is cancelled will be restarted later when its processing time exceeds the threshold, and the threshold can be adaptively adjusted. We derive an competitive ratio for MRS so that its worst-case gap from the optimal solution is bounded. We also implement the MRS scheduler in a real-world system, which schedules a diverse set of Deep Neural Network (DNN) inference tasks. It shows that MRS achieves significant reduction in makespan compared to existing benchmark schemes.
3D Grasp Pose Generation from 2D Anchors and Local Surface

This work proposes a three-dimensional (3D) robot grasp pose generation method for robot manipulators from the predicted two-dimensional (2D) anchors and the depth information of the local surface. Compared to the traditional image-based grasp area detection methods in which the grasp pose is only presented by two contact points, the proposed method can generate a more accurate 3D grasp pose. Furthermore, different from the 6-DoF object pose regression methods in which the point cloud of the whole objects is considered, the proposed method is very lightweight, since the 3D computation is only processed on the depth information of the local grasp surface. The method consists of three steps: (1) detecting the 2D grasp anchor and extracting the local grasp surface from the image; (2) obtaining the average vector of the objects’ local grasp surface from the objects’ local point cloud; (3) generating the 3D grasp pose from 2D grasp anchor based on the average vector of local grasp surface. The experiments are carried out on the Cornell and Jacquard grasp datasets. It is found that the proposed method yields improvement in the grasp accuracy compared to state-of-the-art 2D anchor methods. And the proposed method is also validated on the practical grasp tasks deployed on a UR5 arm with Robotiq Grippers F85. It outperforms state-of-the-art 2D anchor methods on the grasp success rate for dozens of practical grasp tasks.
Online Resource Optimization for Elastic Stream Processing with Regret Guarantee

Recognizing the explosion of large-scale real-time analytics needs, a plethora of stream processing systems, such as Apache Storm and Flink, have been developed to support such applications. Under these systems, a stream processing application is realized as a directed acyclic graph (DAG) of operators, where the resource configuration of each operator has a significant impact on its overall throughput and latency performance. However, there is a lack of dynamic resource allocation schemes, which are theoretically sound and practically implementable, especially under the drastically changing offered load. To address this challenge, we present Dragster1, an online-optimization-based dynamic resource allocation scheme for elastic stream processing. By combining the online optimization framework with upper confidence bound (UCB) techniques, Dragster can guarantee, in expectation, a sub-linear increase in the throughput regret w.r. t. time. To demonstrate the efficacy, we implement Dragster to improve the throughput of Flink applications over Kubernetes. Compared to the state-of-the-art algorithm Dhalion, Dragster can achieve a 1.8X-2.2X speed-up in converging to the optimal configuration. It can contribute to 20.0%-25.8% gain in tuple-processing goodput and 14.6%-15.6% cost-savings.
Structured Concurrency: A Review

Today, mobile applications use thousands of concurrent tasks to process multiple sensor inputs to ensure a better user experience. With this demand, the ability to manage these concurrent tasks efficiently and easily is becoming a new challenge, especially in their lifetimes. Structured concurrency is a technique that reduces the complexity of managing a large number of concurrent tasks. There have been several languages or libraries (e.g., Kotlin, Swift, and Trio) that support such a paradigm for better concurrency management. It is worth noting that structured concurrency has been consistently implemented on top of coroutines across all these languages and libraries. However, there are no documents or studies in the literature that indicate why and how coroutines are relevant to structured concurrency. In contrast, the mainstream community views structured concurrency as a successor to structured programming; that is, the concept of “structure” extends from ordinary programming to concurrent programming. Nevertheless, such a viewpoint does not explain, as the concept of structured concurrency came out more than 40 years later after structured programming was introduced in the early 1970s, whereas concurrent programming started in the 1960s. In this paper, we introduce a new theory to complement the origin of structured concurrency from historical and technical perspectives—it is the foundation established by coroutines that gives birth to structured concurrency.