Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor

This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data. The primary operations of the 3D SIFT code are implemented on a graphics processing unit (GPU), including convolution, sub-sampling, and 4D peak detection from scale-space pyramids. The performance improvements are quantified in keypoint detection and image-to-image matching experiments, using 3D MRI human brain volumes of different people. Computationally efficient 3D keypoint descriptors are proposed based on the Binary Robust Independent Elementary Feature (BRIEF) code, including a novel descriptor we call Ranked Robust Independent Elementary Features (RRIEF), and compared to the original 3D SIFT-Rank method\citep{toews2013efficient}. The GPU implementation affords a speedup of approximately 7X beyond an optimised CPU implementation, where computation time is reduced from 1.4 seconds to 0.2 seconds for 3D volumes of size (145, 174, 145) voxels with approximately 3000 keypoints. Notable speedups include the convolution operation (20X), 4D peak detection (3X), sub-sampling (3X), and difference-of-Gaussian pyramid construction (2X). Efficient descriptors offer a speedup of 2X and a memory savings of 6X compared to standard SIFT-Rank descriptors, at a cost of reduced numbers of keypoint correspondences, revealing a trade-off between computational efficiency and algorithmic performance. The speedups gained by our implementation will allow for a more efficient analysis on larger data sets. Our optimized GPU implementation of the 3D SIFT-Rank extractor is available at https://github.com/CarluerJB/3D_SIFT_CUDA.
Comparative development of limb musculature in phylogenetically and ecologically divergent lizards

Squamate reptiles (lizards, snakes, and amphisbaenians) exhibit incredible diversity in their locomotion, behavior, morphology, and ecological breadth. Although they often are used as models of locomotor diversity, surprisingly little attention has been given to muscle development in squamate reptiles. In fact, the most detailed examination was conducted almost 80 years ago and solely focused on the proximal limb regions. Herein, we present forelimb and hindlimb muscle morphogenesis data for three lizard species with different locomotion and feeding strategies: the desert grassland whiptail lizard, the central bearded dragon, and the veiled chameleon. This study fills critical gaps in our understanding of muscle morphogenesis in squamate reptiles and presents a comparative and temporospatial analysis of muscle development.
Geometric mean of bipartite concurrences as a genuine multipartite entanglement measure

In this work we propose the geometric mean of bipartite concurrences as a genuine multipartite entanglement measure. This measure achieves the maximum value for absolutely maximally entangled states and has desirable properties for quantifying potential quantum resources. The simplicity and symmetry in the definition facilitates its computation for various multipartite entangled states including the GHZ states and the $W$ states. With explicit examples we show that our measure results in distinct entanglement orderings from other measures, and can detect differences in certain types of genuine multipartite entanglement while other measures cannot. These results justify the practical application of our measure for tasks involving genuine multipartite entanglement.
Fatigue-strength prediction of microelectronics solder joints under thermal cyclic loading

The stress-strain analyses for the solder joints in a thin single outline package (TSOP), a ball grid array (BGA) assembly, and a leadless ceramic chip carrier (LCCC) are carried out to investigate the plastic-creep behavior, and stress relaxation behavior due to the temperature cycling or isothermal cyclic loading. The temperature dependence of plastic behavior (yield stress) and creep behavior (creep properties) are taken into consideration in all numerical analyses. The results of finite element analysis (FEA) show that in an accelerated temperature cycling test, long high-temperature and low-temperature dwell times do not contribute to increase the cyclic inelastic equivalent strain range in solder joints, although the creep behavior occurring during the dwell times in an operating condition is very important to be taken into consideration for estimating the fatigue life of solder joints. Based upon the results of the strain analyses, some efficient testing processes of temperature cycling and isothermal fatigue tests for the microelectronic solder joints are proposed, and the cycling tests are carried out. The experimental results show a good agreement with the analytic results.
An improved voltage vector control method for Neutral-Point-Clamped inverters

A new PWM method for Neutral-Point-Clamped (NPC) inverters is introduced. The method is based on closed-loop control of the line-to-line voltage vectors. It uses independent hysteresis comparator controllers to regulate the direct and quadrature axis components of the three phase output voltages. The controllers select the appropriate inverter output voltage vectors through an EPROM table. The closed-loop control allows a high performance over the whole range of operation, even when low speed devices such as the GTO are used. A neutral-point potential control is described, which is capable of stabilizing the variations within fixed limits during steady and transient states. The principle of the method is discussed and the vector selection technique is presented. The effectiveness in the output-voltage-waveform generation and the balance of the DC-link capacitor voltages are verified by simulation and experiment. <<ETX>>
A short review and discussion about the limiting factors, which can hold back wind and photovoltaic power plants from its presently exponential growth

The present nearly exponential growth of the cumulative installation power of wind and photovoltaic power plants is very promising in the context of a rapid reduction of the emission of CO2, which can lead to a swift mitigation climate change if this growth behavior is maintained in the future. In this review we identify a set of ten limiting factors that can restrain, or halt back, an exponential growth of these variable renewable power plants (VREs) in the future. If the exponential growth slows down to a linear growth, such a case would extent considerably the time to reduce the CO2 emissions and the related mitigation of climate change. A scenario that would result to a much higher risk of a future continuation of the economic grow and thrive of the global economy and human society. We argue that if these limiting factors are adequately addressed to an exponential expansion VRE power plants is feasible and can result in the nearly complete avoidance of CO2 emissions as related to energy generation and consumption in 2030.
Innovative Harsh Environment Dry-Tree Support Semi-Submersible for Ultra Deepwater Applications

Natural heave period exceeding over 21 sec is not the only important design criteria for the design of an ultra deepwater semi-submersible for dry-tree support application. The key design features that would enhance the applications of the semi-submersible design for ultra deepwater are vessel motion characteristic, heave natural period, high air-gap, large deck-area, large deck pay-load, reduced mooring load, large riser pull-down load, feasible for dynamic positioning, feasible for dry-tow, feasible for wet-tow, feasible for self-installation, feasible for quay-side top side integration, feasible for float-over installation. This paper presents the design innovations of the semi-submersible that has reached the above listed desired key factors. The designed vessel supports dry-tree with large riser loads resulted from ultra deepwater which is decoupled and is also independent of the designed top side pay load. The design is streamlined to withstand harsh environment. The vessel is designed to favor both options with quay-side deck integration or float-over deck installation. Extreme low accelerations in extreme wave environment on top of the above desired features make this semi-submersible suitable for harsh environment and ultra deepwater applications. A brief summary of hydrodynamic response of the vessel is added in this paper.Copyright © 2009 by ASME
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey

With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example, federated learning, to train a shared deep model across multiple participants. It could effectively take advantage of the resources of each participant and obtain a more powerful learning system. However, integrity and privacy threats in such systems have greatly obstructed the applications of collaborative learning. And a large amount of works have been proposed to maintain the model integrity and mitigate the privacy leakage of training data during the training phase for different collaborative learning systems. Compared with existing surveys that mainly focus on one specific collaborative learning system, this survey aims to provide a systematic and comprehensive review of security and privacy researches in collaborative learning. Our survey first provides the system overview of collaborative learning, followed by a brief introduction of integrity and privacy threats. In an organized way, we then detail the existing integrity and privacy attacks as well as their defenses. We also list some open problems in this area and opensource the related papers on GitHub: https://github.com/csl-cqu/awesome-secure-collebrative-learning-papers.
Past, Present and Future of Computational Storage: A Survey

We live in a data-centric world where we are heading to generate close to 200 Zettabytes of data by the year 2025. Our data processing requirements have also increased as we push to build data processing frameworks that can process large volumes of data in a short duration, a few milli- and even micro-seconds. In the prevalent computer systems designs, data is stored passively in storage devices which is brought in for processing and then the results are written out. As the volume of data explodes this constant data movement has led to a"data movement wall"which hinders further process and optimizations in data processing systems designs. One promising alternative to this architecture is to push computation to the data (instead of the other way around), and design a computational-storage device or CSD. The idea of CSD is not new and can trace its root to the pioneering work done in the 1970s and 1990s. More recently, with the emergence of non-volatile memory (NVM) storage in the mainstream computing (e.g., NAND flash and Optane), the idea has again gained a lot of traction with multiple academic and commercial prototypes being available now. In this brief survey we present a systematic analysis of work done in the area of computation storage and present future directions.
Seesaw mechanism in the R-parity violating supersymmetric standard model with the gauged flavor U(1)X symmetry

We study the seesaw mechanism in the supersymmetric standard model (SSM) with the ${\mathbb {Z}}_3$ symmetry called Matter triality (M3). The Abelian discrete symmetry prohibits the baryon number violation operators at the (non-)renormalizable level, which ensures the proton longevity. The cubic coupling by right-handed neutrinos generates the Majorana mass term after the right-handed sneutrinos develop into the vacuum expectation values. Due to the R-parity violation, the active neutrino masses can be generated not only from the mixings with the right-handed neutrinos but also from the ones with MSSM-neutralinos. In this paper, we propose a model where M3 is embedded into the gauged flavor symmetry. The flavor charges are constrained by the anomaly cancellation conditions and the requirements to realize the fermion masses and mixing angles in the quark and lepton sectors. We analyze the assignments of the flavor charge, and show that the sterile neutrino mass is allowed to be below the soft SUSY breaking scale, which are constrained by the half lifetime of the neutrinoless double beta decay.
Difuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps

One prominent tactic used to keep malicious behavior from being detected during dynamic test campaigns is logic bombs, where malicious operations are triggered only when specific conditions are satisfied. Defusing logic bombs remains an unsolved problem in the literature. In this work, we propose to investigate Suspicious Hidden Sensitive Operations (SHSOs) as a step towards triaging logic bombs. To that end, we develop a novel hybrid approach that combines static analysis and anomaly detection techniques to un-cover SHSOs, which we predict as likely implementations of logic bombs. Concretely, Difuzer identifies SHSO entry-points using an instrumentation engine and an inter-procedural data-flow analysis. Then, it extracts trigger-specific features to characterize SHSOs and leverages One-Class SVM to implement an unsupervised learning model for detecting abnormal triggers. We evaluate our prototype and show that it yields a precision of 99.02% to detect SHSOs among which 29.7% are logic bombs. Difuzer outperforms the state-of-the-art in revealing more logic bombs while yielding less false positives in about one order of magnitude less time. All our artifacts are released to the community.
HPT/LPT Interaction and Flow Management in the Inter-Turbine Space of a Modern Axial Flow Turbine

Understanding the unsteady flows of turbine sub-component interaction has been an important aspect in the design of modern aircraft engines to improve component performance. This research is focused on the inter-turbine space and its neighboring blade rows where intensive interactions of the two turbine sub-components occur. Via large-scale parallel numerical simulations, the paper presents the loss mechanism of the HPT rotor shock-wake interaction, the interaction loss transfer, analysis of the 1st LPT nozzle loading, the performance and flow capacity of the 1st LPT stage. Flow management is performed via a reduced-shock HPT rotor design and a 2-in spacer in the inter-turbine space, aimed at reducing interaction loss and wake ingestion loss by the LPT. The effectiveness of these means is analyzed and comparisons are made to baseline configurations.Copyright © 2006 by ASME
Online control of the False Discovery Rate in group-sequential platform trials

When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O’Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.
Supervised laser-speckle image sampling of skin tissue to detect very early stage of diabetes by its effects on skin subcellular properties

This paper investigates the effectiveness of an expert system based on K-nearest neighbours algorithm for laser speckle image sampling applied to the early detection of diabetes. With the latest developments in artificial intelligent guided laser speckle imaging technologies, it may be possible to optimise laser parameters, such as wavelength, energy level and image texture measures in association with a suitable AI technique to interact effectively with the subcellular properties of a skin tissue to detect early signs of diabetes. The new approach is potentially more effective than the classical skin glucose level observation because of its optimised combination of laser physics and AI techniques, and additionally, it allows non-expert individuals to perform more frequent skin tissue tests for an early detection of diabetes.
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays

We propose a simple and efficient image classification architecture based on deep multiple instance learning, and apply it to the challenging task of caries detection in dental radiographs. Technically, our approach contributes in two ways: First, it outputs a heatmap of local patch classification probabilities despite being trained with weak image-level labels. Second, it is amenable to learning from segmentation labels to guide training. In contrast to existing methods, the human user can faithfully interpret predictions and interact with the model to decide which regions to attend to. Experiments are conducted on a large clinical dataset of $\sim$38k bitewings ($\sim$316k teeth), where we achieve competitive performance compared to various baselines. When guided by an external caries segmentation model, a significant improvement in classification and localization performance is observed.
Calorie Aware Automatic Meal Kit Generation from an Image

Calorie and nutrition research has attained increased interest in recent years. But, due to the complexity of the problem, literature in this area focuses on a limited subset of ingredients or dish types and simple convolutional neural networks or traditional machine learning. Simultaneously, estimation of ingredient portions can help improve calorie estimation and meal re-production from a given image. In this paper, given a single cooking image, a pipeline for calorie estimation and meal re-production for different servings of the meal is proposed. The pipeline contains two stages. In the first stage, a set of ingredients associated with the meal in the given image are predicted. In the second stage, given image features and ingredients, portions of the ingredients and finally the total meal calorie are simultaneously estimated using a deep transformer-based model. Portion estimation introduced in the model helps improve calorie estimation and is also beneficial for meal re-production in different serving sizes. To demonstrate the benefits of the pipeline, the model can be used for meal kits generation. To evaluate the pipeline, the large scale dataset Recipe1M is used. Prior to experiments, the Recipe1M dataset is parsed and explicitly annotated with portions of ingredients. Experiments show that using ingredients and their portions significantly improves calorie estimation. Also, a visual interface is created in which a user can interact with the pipeline to reach accurate calorie estimations and generate a meal kit for cooking purposes.
Absolute keV x-ray yield and conversion efficiency in over dense Si sub-petawatt laser plasma

Laser-produced plasmas are bright, short sources of x-rays commonly used for time-resolved imaging and spectroscopy. Their usage implies accurate knowledge of laser-to-x-ray conversion efficiency (CE), spectrum, photon yield and angular distribution. Here, we report on soft x-ray emission in the direction close to the target normal from a thin Si foil irradiated by a sub-PW picosecond laser pulse. These absolute measurements cover a continuous and broad spectral range that extends from 4.75 to 7.3 Å (1.7–2.6 keV). The x-ray spectrum consists of spectral line transitions from highly charged ions and broadband emission with contributions from recombination and free-free processes that occur when electrons decelerate in plasma electromagnetic fields. Angular distribution of the emission was investigated via particle-in-cell simulations, which allowed to estimate the yield into the full solid angle. We find that experimental and simulation estimations of laser to free-free emission CE are in good agreement.
Learned Half-Quadratic Splitting Network for Magnetic Resonance Image Reconstruction

Magnetic Resonance (MR) image reconstruction from highly undersampled k -space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a learned half-quadratic splitting algorithm for MR image reconstruction and implements the algorithm in an unrolled deep learning network architecture. We compare the performance of our proposed method on a public cardiac MR dataset against DC-CNN and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results with fewer model parameters and faster reconstruction speed. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is 1 . 76 dB and 2 . 74 dB over LPDNet in peak signal-to-noise ratio on 5 × and 10 × acceleration, respectively. Code for our method is publicly available at https://github.com/hellopipu/HQS-Net .
Heckman-Selection or Two-Part models for alcohol studies? Depends

Aims: To re-introduce the Heckman model as a valid empirical technique in alcohol studies. Design: To estimate the determinants of problem drinking using a Heckman and a two-part estimation model. Psychological and neuro-scientific studies justify my underlying estimation assumptions and covariate exclusion restrictions. Higher order tests checking for multicollinearity validate the use of Heckman over the use of two-part estimation models. I discuss the generalizability of the two models in applied research. Settings and Participants: Two pooled national population surveys from 2016 and 2017 were used: the Behavioral Risk Factor Surveillance Survey (BRFS), and the National Survey of Drug Use and Health (NSDUH). Measurements: Participation in problem drinking and meeting the criteria for problem drinking. Findings: Both U.S. national surveys perform well with the Heckman model and pass all higher order tests. The Heckman model corrects for selection bias and reveals the direction of bias, where the two-part model does not. For example, the coefficients on age are upward biased and unemployment is downward biased in the two-part where the Heckman model does not have a selection bias. Covariate exclusion restrictions are sensitive to survey conditions and are contextually generalizable. Conclusions: The Heckman model can be used for alcohol (smoking studies as well) if the underlying estimation specification passes higher order tests for multicollinearity and the exclusion restrictions are justified with integrity for the data used. Its use is merit-worthy because it corrects for and reveals the direction and the magnitude of selection bias where the two-part does not.