Miracle-dz/UniFAR-FTUs huggingface dataset:
RUnT: A Network Combining Residual U-Net and Transformer for Vertebral Edge Feature Fusion Constrained Spine CT Image Segmentation

Scoliosis, spinal deformity and vertebral spondylolisthesis are spinal disorders with high incidence, which seriously affect people’s lives and health. CT is an important medical tool for the detection and diagnosis of spinal disorders and provides a large amount of pathologically valid information in various clinical practices such as spine pathology assessment and computer-assisted surgical interventions. As the spine presents long span, complex shape of biological curve and high multi-stage similarity in the sagittal plane of CT images. Therefore, fast and accurate spine segmentation technology has become an important research direction for computer-aided diagnosis. We proposed an RUnT network based on the combination of residual U-Net feature extraction network and Vision Transformer structure for fast and efficient automatic segmentation of multiple vertebrae of the spine. The deep vertebral features are first extracted using the residual U-Net network to prevent gradient diffusion while improving the accuracy of vertebral contour segmentation. Then the multi-scale feature maps extracted by the residual structure containing rich vertebral superficial information are input to the edge segmentation module. We designed the vertebral contour feature extraction structure to refine the segmentation boundaries and ensure the segmentation consistency of each vertebra by combining the operations of deconvolution and convolution for three different scales of deep features.Finally, the global information extraction module based on Transformer structure is combined with the local feature extraction module to achieve the blending of global location information of vertebrae with local features through the self-attentive feature map of multi-scale volume. By mixing edge features with semantic features, the semantic confusion arising from the high similarity between vertebrae when the decoder extracts vertebral features is reduced. The model proposed in this paper is experimented on the CTSpine1K and VerSe 20 public datasets. The results show that the model proposed in this paper obtains the state-of-the-art segmentation performance with the average DSC scores of 88.4% and 81.5% on CTSpine1K and VerSe 20, respectively, while reducing the average distance of HD95 from 4.86 to 3.88.
An Inverted Doherty Power Amplifier Insensitive to Load Variation With an Embedded Impedance Sensor in Its Output Power-Combining Network

This article presents an inverted Doherty power amplifier (IDPA) made load insensitive up to 2:1 voltage standing wave ratio (VSWR) across its fractional bandwidth with a very compact wideband impedance sensor embedded in its output power-combining network (OPCN). To correct for load variation, a low-loss tunable resonator (TR) is used to ensure ohmic loads to the main and peaking stages at the center frequency of operation. At off-center frequencies, TR is used to present an ohmic load for the main stage, while a digitally adjustable phase shifter is used to (re)align the main and peaking stage’s current summation in the OPCN. For ohmic load deviations, the main and peaking stage supply voltages and input drives are adjusted to maintain the ideal Doherty’s output power and efficiency profile related to nominal <inline-formula> <tex-math notation="LaTeX">$50 {\Omega }$ </tex-math></inline-formula> loading across the bandwidth. To implement the control of the formerly mentioned technique, a wideband impedance sensor is proposed, which uses the orthogonality of incident and reflected waves and requires only four peak detectors. As proof of principle, a prototype 850–950-MHz IDPA featuring the proposed correction technique, the impedance sensor, and the control loop has been implemented as a printed circuit board (PCB) demonstrator. Measurement results show that the IDPA can maintain constant output power with a tolerance of only <inline-formula> <tex-math notation="LaTeX">${\pm }{\mathrm {0.2}}$ </tex-math></inline-formula> dB while improving the drain efficiency and linearity across the entire fractional bandwidth (11<inline-formula> <tex-math notation="LaTeX">${\%}$ </tex-math></inline-formula>) for a VSWR range of 2:1.
Another Type of Free Choice Effect: Korean Amwu N-Lato

The discussion of Free Choice Items (FCIs) in the literature has been primarily concerned with English any taking a universal-like interpretation (Dayal 1998; Giannakidou 2001; Sæbø 2001, among others). For instance, in (1), any apple among all the apples in the discourse is a permitted option for the addressee to pick. The addressee’s freedom of choice to pick an unrestricted apple can be seen as being inherited by the speaker’s permission or indifference. That is, because the speaker does not care the identity of an apple the addressee would pick, every apple in a given set can be a permitted choice.
Influence of coatings based on Zr and Ti on the wear resistance of cemented carbide tools when turning transport machines’ parts from structural steels

The influence of coatings based on titanium carbide and zirconium carbide, obtained by PVD method, on cutting tool wear resistance under conditions of the longitudinal turning of transport machines’ parts of steel 45 and stainless steel 12H18N10T by inserts of hard alloy grade T15K6 was investigated. It has been experimentally established that the presence and composition of the coating on the working surfaces of the tool in most cases contributes to a significant reduction in the intensity of wear of cemented carbide inserts. When turning stainless steel 12H18N10T, the best result was demonstrated by the coating ZrN + (Ti–Zr) N + TiN, which provided not only the minimum intensity of wear, but also the expansion of the range of optimal cutting speeds. When machining structural steel 45, the maximum reduction in the wear intensity of the cutting inserts was observed with the use of a single-layer TiN coating and a (Ti,Zr)N coating.
Design of Nonreciprocal Filtering Antenna Using Spatio-Temporal Modulated SIW Resonators

This article reports on, for the first time, the design of nonreciprocal filtering antennas using spatio-temporal modulated substrate integrated waveguide (SIW) resonators. It consists of two SIW cavities and a half-mode SIW (HMSIW) cavity, which is designed as the last-stage resonator and the radiator. By integrating time-modulated capacitors on the surface gaps of the three cavities, the nonreciprocal radiation patterns in transmission and reception modes are achieved at the same operating frequency. Three radiation nulls are generated due to the cross-coupling in the time-modulated network and source-load coupling. The theoretical analysis of the co-designed nonreciprocal SIW filtering antenna is detailed, along with methods for its co-simulations and measurements. For the proof-of-concept demonstration, a nonreciprocal SIW filtering antenna operating at 2.4 GHz is designed and fabricated. The measured 10-dB bandwidth and peak gain are 1.7% and 2.6 dBi, respectively. The measured realized gains at transmission and reception show that the reverse isolation is greater than 11 dB within a bandwidth of 30 MHz. The frequency selectivity values are 756 and 368 dB/GHz at the left and right sides of the passband, respectively.
Applied Mixed Models in Medicine, Second Edition, by H. Brown and R. Prescott

Mixed models are important tools for analysis in medical research because they are applicable to correlated data, which are common in medical studies. For example, in a study in which naturally occurring cluster of subjects (e.g., twins) are randomized to one of several treatment groups, measurements from siblings might be more similar than measurements from unrelated subjects, so that measurements on siblings will be correlated and will therefore violate an assumption of independence. Correlation might also be anticipated on the repeated measurements taken on subjects in longitudinal trials. Applied Mixed Models in Medicine is a text that, as stated by the authors, intends “to put all types of mixed models into a general framework and to consider the practical implications of their use at a level that can be understood by applied statisticians and numerate scientists. ” It is the second edition of a book that first appeared in Statistics in Practice, a series of texts that “provide detailed coverage of statistical concepts, methods and worked case studies in specific fields of investigation and study”. The authors justify the need for a second edition by noting that they include one new example and that they have reworked all other examples from the previous edition using Version 9 of SAS, including use of PROC GLIMMIX in SAS to fit generalized linear mixed models and categorical mixed models. (SAS procedures PROC GEMNOD and PROC MIXED are also widely used throughout the text.) Other additions include new sections on mixed models for analysis of cluster randomized trials and bioequivalence studies, two areas of great practical importance. (For example, as noted by the authors, the Food and Drug Administration suggests the use of mixed models to establish bioequivalence on their website www.fda.gov.) The new sections are not long (less than ten pages each), but they each provide background, an example, and relevant SAS code. Readers who were pleased with the previous edition might therefore find it worth their while to purchase the new edition. This text should also appeal to readers who are wishing to gain some familiarity with mixed models and who would like to implement these models in SAS. The book is organized as follows. The first chapter describes some common medical studies, including a cross-over trial, multi-center trial, and repeated measures clinical trial. For each example, the authors first fit a simple model, e.g., they begin with a one-way analysis of variance model for analysis of the cross-over trial. They then slightly complicate the models, to demonstrate and define the following: (i) random effects models that include additional sources of variation in addition to the usual residual variation (e.g., a regression model that
Nanosize Filler Effect on Propagation of Electron Avalanche in Terms of Waveform Characteristics of Partial Discharge

Nanocomposite materials have excellent insulating properties, such as improved treeing resistance. The propagation properties of electrical trees are often determined using optical observations and partial discharge (PD) characteristics regarding phase angle distribution patterns. There is a limit to confirming the effects of physical barriers on the PD by optical observation alone. Conversely, there is a high possibility that the PD waveform reflects the characteristics of the electron avalanche in the discharge space. In this study, we constructed a PD measurement system with the widest possible frequency range and acquired 1000 PD waveforms along the time series. Although the acquired waveforms have a single peak or multiple peaks, adding nanofillers increased the observation ratio of the waveform with multiple peaks. Furthermore, the multiple peaks suggested that the electron avalanche progresses with difficulty considering that the nanofillers easily capture electrons. We investigated the effect of adding nanofillers from the viewpoint of the waveform characteristics. The time difference between the peaks and the peak ratio is also supported by the property changes in the electron avalanche because of the electron capture by nanofillers.
Muscle Protein Synthesis with a Hybrid Dairy and Plant-Based Protein Blend (P4) Is Equal to Whey Protein in a Murine Ageing Model after Fasting

P4, a specific combination of dairy proteins (whey and casein) and plant-based protein isolates (pea and soy), has been shown to provide a more balanced amino acid (AA) profile than its single constituent proteins; however, less is known about how this translates to muscle protein synthesis (MPS). The aim of this study was to investigate the effect of P4 compared to whey or casein against fasted control on MPS. C57BL/6J mice, aged 25 months, were fasted overnight, followed by oral gavage of either whey, P4, casein, or water as a fasted control. Thirty minutes after ingestion, puromycin (0.04 µmol∙g−1 bodyweight) was subcutaneously injected; 30-min thereafter, mice were sacrificed. MPS was measured by the SUnSET method, and signalling proteins were determined in the left-tibialis anterior (TA) muscle by the WES technique. AA composition was determined in plasma and right-TA muscle. Dried blood spots (DBS) were analysed for postprandial AA dynamics at 10, 20, 45, 60 min. MPS was 1.6-fold increased with whey (p = 0.006) and 1.5-fold with P4 compared to fasted (p = 0.008), while no change was seen with casein. This was confirmed by a significant increase of phosphorylated/total ratio of 4E-BP1 for both whey (p = 0.012) and P4 (p = 0.001). No changes were observed in p70S6K and mTOR phosphorylation/total ratio with whey or P4. Intramuscular leucine levels were lower for P4 (0.71 µmol∙g dry weight−1) compared to whey (0.97 µmol∙g dry weight−1) (p = 0.0007). Ten minutes postprandial, DBS showed significantly increased blood AA levels of BCAAs, histidine, lysine, threonine, arginine, and tyrosine for P4 versus fasted. In conclusion, a hybrid mix of dairy and plant-based proteins (P4) resulted in a MPS response that was similar to whey protein in aged mice after fasting. This suggests that other anabolic triggers beyond leucine or the well-balanced amino acid profile and bioavailability of the blend benefit stimulation of MPS.
Improved U-Net for Growth Stage Recognition of In-Field Maize

Precise recognition of maize growth stages in the field is one of the critical steps in conducting precision irrigation and crop growth evaluation. However, due to the ever-changing environmental factors and maize growth characteristics, traditional recognition methods usually suffer from limitations in recognizing different growth stages. For the purpose of tackling these issues, this study proposed an improved U-net by first using a cascade convolution-based network as the encoder with a strategy for backbone network replacement to optimize feature extraction and reuse. Secondly, three attention mechanism modules have been introduced to upgrade the decoder part of the original U-net, which highlighted critical regions and extracted more discriminative features of maize. Subsequently, a dilation path of the improved U-net was constructed by integrating dilated convolution layers using a multi-scale feature fusion approach to preserve the detailed spatial information of in-field maize. Finally, the improved U-net has been applied to recognize different growth stages of maize in the field. The results clearly demonstrated the superior ability of the improved U-net to precisely segment and recognize maize growth stage from in-field images. Specifically, the semantic segmentation network achieved a mean intersection over union (mIoU) of 94.51% and a mean pixel accuracy (mPA) of 96.93% in recognizing the maize growth stage with only 39.08 MB of parameters. In conclusion, the good trade-offs made in terms of accuracy and parameter number demonstrated that this study could lay a good foundation for implementing accurate maize growth stage recognition and long-term automatic growth monitoring.
ChatGPT Informed Graph Neural Network for Stock Movement Prediction

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.
An improved image matching algorithm based on SURF and Delaunay TIN

Image matching is one of the key technologies in the image processing. In order to increase its efficiency and precision, a new method for image matching which based on the improved SURF and Delaunay-TIN is proposed in this paper. Based on the original SURF algorithm, three constraint conditions, color invariant model, Delaunay-TIN, triangle similarity function and photography invariant are added into the original SURF model. With the proposed algorithm, the image color information is effectively retained and the erroneous matching rate of features is largely reduced. The experimental results shows that this proposed method has the characteristics of higher matching speed, uniform distribution of feature points to be matched, and higher correct matching rate than the original algorithm does.
Determination of rail deformations using fiber optic technologies

This paper discusses the application of the fiber-optic technologies in measuring the parameters of the stress-strain state of a seamless track rail. Measuring modules are attached directly to the side surface of the rail and contain fiber-optic sensors with a special structure for changing the refractive index along the length of the base part. The values of deformations are determined during full-scale measurements when analyzing changes in the parameters of the light pulses passing through an optical channel with Bragg gratings. The implemented approaches in measurements and simulation are relevant for the sections of the railway track that are most susceptible to deformations with a large gradient of values, for example, at turnouts, in the places of subgrade heaving, etc.
Reduced computation blind equalization for FIR channel input Markov models

The paper deals with adaptive blind equalization for the transmission of digital modulated signals on an unknown dispersive channel with additive white gaussian noise. The observed signal being modeled as a hidden Markov model(HMM), the expectation-maximization (EM) algorithm can be used to realize both the channel identification and the estimation of the emitted symbols. The paper proposes a new on-line algorithm with reduced complexity. This algorithm, obtained as an approximate solution of the maximum likelihood (ML) problem via the EM sequential algorithm, has strong connections with decision feedback equalizers (DFE) using the recursive least square (RLS) Algorithm.
Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. We develop a shape representation that is sensitive to subtle shape differences but relatively insensitive to noise, and use this representation to detect features of the objects. The features are used, along with a measure for shape similarity, to compute a correspondence between the objects, which then allows shape comparison based on the shape properties at corresponding points. An advantage of this approach is that the final comparisons depend on the similarity-based correspondence and not on a physical three dimensional alignment. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 WASHINGTON UNIVERSITY Sever Institute School of Engineering and Applied Science Department of Computer Science and Engineering Dissertation Examination Committee: Cindy Grimm, Chair Burchan Bayazit Roger Chamberlain Tao Ju Erik Trinkaus Victor Wickerhauser COMPARING FEATURES OF THREE-DIMENSIONAL OBJECT MODELS USING REGISTRATION BASED ON SURFACE CURVATURE SIGNATURES by Timothy David Gatzke A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy May 2007 Saint Louis, Missouri WASHINGTON UNIVERSITY Sever Institute School of Engineering and Applied Science Department of Computer Science and Engineering
RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.