Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Recovery Investigations at the Tank Destroyer Site (41CV1378) at Fort Hood, Coryell County, Texas

Data recovery investigations at the Tank Destroyer site (41CV1378) were conducted in August 2007 for the Texas Department of Transportation (TxDOT). This work was required because of potential impacts to the site from TxDOT’s planned improvements of Tank Destroyer Boulevard and State Highway 9. The investigations focused on a burned rock mound (Feature 1), one-half of which has been destroyed by an adjacent tank trail. The mound contained two internal features: an off-centered earth oven and a small cluster of Rabdotus sp. shells. With the exception of the location of its earth oven, the mound at the Tank Destroyer is typical of a classic central Texas domed mound, though slightly flattened by postdepositional processes. In all, an area of 30.5 m2 and volume of 11.8 m3 of cultural deposits were hand excavated, and an additional ca. 17.3 m2 was mechanically stripped. The mound excavations yielded 5,570.5 kg of burned rocks. Artifacts recovered from mound and nonmound contexts consist of 129 chipped stone tools, 9 cores and core fragments, 4,466 pieces of unmodified debitage, 1 ground stone tool, 2 unmodified bone fragments, 1,415 Rabdotus sp. shells, and 40 historic artifacts. In addition, 413 pieces of microdebitage and 251 Rabdotus sp. shells were recovered from flotation and soil column samples taken from the mound. There was virtually no preservation of vertebrate faunal remains and poor preservation of botanical remains. No economic plants (i.e., food resources) were recovered despite the collection and processing of flotation samples. Sixteen radiocarbon assays on charred wood and Rabdotus sp. shells date the site occupation to 1500 b.c. through a.d. 1650. The date range for the diagnostic projectile points recovered from the site (200 b.c. to a.d. 1200) fits nicely within the range of radiocarbon dates. As a group, the radiocarbon dates and the projectile points suggest that the most intensive period of site use occurred intermittently between 1000 b.c. and a.d. 1200. Like most burned rock mounds, the mound at the Tank Destroyer site consisted of a jumbled mass of burned rocks that episodically accreted around an earth oven. These processes and repeated use over centuries limit our ability to recognize distinct components for analysis. Given these limitations, our analysis took a different approach. While it includes traditional analyses of the lithic, burned rock, and snail assemblages, it also examines social identity during the Late Archaic period in central Texas and the relationships between burned rock mounds and middens and environmental variables through a landscape analysis.
Particle Swarm Optimization for Integer Programming

Particle Swarm Optimization (PSO) has been successfully applied to many optimization problems on real search space. For integer programming, there exist some difficulties and problems for the direct applications of PSO due to the variables belonging to the set of integers. In this paper, a new PSO algorithm is presented for integer programming, which evolves on the set of Integer space. The results regarding the performance of PSO algorithm on the class of problems are reported.
Curve Fitting Based on Uniform B-splines with Shape Parameter

This paper analyses the uniform B-spline model with shape parameters,and applys the model to the curve fitting of discrete data points.In this paper,the value of the shape parameter is studied,and an algorithm based on iterative linear closest points is put forward to rectify the parameter values of data points.The analysis of experimental results demonstrates the validity and robustness of our algorithm.
Study on HowNet-Based Word Similarity Algorithm

Word(sentence) similarity computing based on the "HowNet" usually treats the optimal matches between the primitives or words as the basic unit,and the ultimate outcome can be the sum of weighted counts.However,this approach often results in the information duplication and some irrational constructions.To deal with these issues,this paper propose to calculate the similarity of sets by the statistics on common information(commonality) and the different information(differences) between the two sets of direct primitives.Moreover,the paper introduces this measure into the calculation of sentence similarity.The final experimental analysis shows that the proposed method is more stable and effective.
Factors Related to Severity, Hospitalization, and Mortality of COVID-19 Infection among Patients with Autoimmune Diseases

Patients with an autoimmune disease could be at higher risk of a poor outcome when contracting COVID-19 infection due to aberrant immune responses and use of immunosuppressant therapies for chronic autoimmune treatment. Here, we conducted a retrospective study to identify the factors related to severity, hospitalization, and mortality among patients with autoimmune diseases. We found 165 cases of patients with pre-existing autoimmune diseases who had contracted COVID-19 between March 2020 and September 2022. Data on demographical characteristics; autoimmune diagnosis and treatment; COVID-19 vaccination status; and time, severity, and outcome of COVID-19 infection were collected. Most of the subjects were female (93.3%) and autoimmune diagnoses included systemic lupus erythematosus (54.5%), Sjogren’s syndrome (33.5%), antiphospholipid syndrome (23%), vasculitis (5.5%), autoimmune thyroid disease (3.6%), rheumatoid arthritis (3.03%), and inflammatory bowel disease (3.03%) among other autoimmune diseases. There were four COVID-19-related deaths in this study. Factors associated with moderate to severe COVID-19 infection in patients with autoimmune diseases included not being vaccinated against COVID-19, taking a steroid of ≥10 mg prednisone-equivalent per day, and having a cardiovascular disease. Taking a steroid of ≥10 mg prednisone-equivalent per day was also associated with hospitalization in the event of COVID-19 infection, while cardiovascular diseases also showed a significant correlation to mortality in patients with autoimmune diseases who had been hospitalized with COVID-19 infection.
Design of Chitosan-Coated, Quercetin-Loaded PLGA Nanoparticles for Enhanced PSMA-Specific Activity on LnCap Prostate Cancer Cells

Nanoparticles are designed to entrap drugs at a high concentration, escape clearance by the immune system, be selectively taken up by cancer cells, and release bioactives in a rate-modulated manner. In this study, quercetin-loaded PLGA nanoparticles were prepared and optimized to determine whether coating with chitosan would increase the cellular uptake of the nanoparticles and if the targeting ability of folic acid as a ligand can provide selective toxicity and enhanced uptake in model LnCap prostate cancer cells, which express high levels of the receptor prostate-specific membrane antigen (PSMA), compared to PC-3 cells, that have relatively low PSMA expression. A design of experiments approach was used to optimize the PLGA nanoparticles to have the maximum quercetin loading, optimal cationic charge, and folic acid coating. We examined the in vitro release of quercetin and comparative cytotoxicity and cellular uptake of the optimized PLGA nanoparticles and revealed that the targeted nano-system provided sustained, pH-dependent quercetin release, and higher cytotoxicity and cellular uptake, compared to the non-targeted nano-system on LnCap cells. There was no significant difference in the cytotoxicity or cellular uptake between the targeted and non-targeted nano-systems on PC-3 cells (featured by low levels of PSMA), pointing to a PSMA-specific mechanism of action of the targeted nano-system. The findings suggest that the nano-system can be used as an efficient nanocarrier for the targeted delivery and release of quercetin (and other similar chemotherapeutics) against prostate cancer cells.
Elevated Antibody Titers to Epstein–Barr Virus and Cytomegalovirus in Patients with Drug-Induced Lupus

Systemic lupus erythematosus (SLE) is an autoimmune disease, which has been associated with Epstein–Barr virus (EBV) and Cytomegalovirus (CMV) infection. Drug-induced lupus (DIL) is a lupus-like disease caused by the intake of therapeutic drugs, which has been estimated to cause approximately 10–15% of lupus-like cases. Although SLE and DIL share common clinical symptoms, there are some fundamental differences between DIL and SLE onset. Moreover, it remains to be examined whether environmental factors, such as EBV and CMV infections, may contribute to the development of DIL. This study focused on examining the possible association between DIL and EBV and CMV infections, by examining IgG titers to EBV and CMV antigens in serum samples by enzyme-linked immunosorbent assays. Antibody titers to EBV early antigen–diffuse and CMV pp52 were found to be significantly elevated in both SLE and DIL patients compared to healthy controls, although no correlation was found for antibodies to the two virus antigens in the respective disease groups. Moreover, total IgG titers were reduced in SLE and DIL serum samples, which may reflect a general lymphocytopenia, which commonly is associated with SLE. The current findings support that EBV and CMV infections may contribute to the development of DIL and that onset of both diseases are related.
Research on features extraction and recognition of parts multi-source image

A method to fuse part multi-source image and to extract part image feature based on wavelet transform is presented. Firstly, the part multi-source image is analyzed using wavelet multi-scale transform to obtain the coefficients of wavelet transform, which fuses a part multi-scale image. Then, the edges from fused part multi-source image is detected using wavelet multi-scale edge detection, edge image divides into several areas and counts edge pixels in these areas, the ratio of edge pixels in an area to total pixels in the area is part image feature. Finally, the part multi-source image is realized pattern recognition using neural networks and network technology. Experiment results that the proposed method can efficiently recognize multi-source parts.
Characteristic Mueller matrix responses and correlation analysis for vertical GAA nanowire structure manufacturing using RCWA simulated spectra

Here, we explore the application of Mueller Matrix Spectroscopic Ellipsometry (MMSE) based scatterometry to determining the shape and dimensions of vertical nanowire gate-all-around (GAA) transistors. These transistors could be used in memory applications, like 4F2 3D NAND. The simulated sensitivity of MMSE based scatterometry to changes in the geometry of GAA transistor test structures was characterized at a critical etch back process step to define gate length. MMSE scatterometry simulations were done using rigorous coupled wave analysis (RCWA). We also searched for the optimum azimuthal measurement angle that increases the information available in off-diagonal Mueller matrix elements. A correlation study was done between the etch back height and nanowire diameter critical dimensions to determine if the Mueller matrix response could be isolated to changes in one parameter. The vertical GAA structure was shown to have an isotropic MMSE response at azimuthal measurement angles of 0, 45, and 90 degrees. The simulations show that MMSE scatterometry is sensitive to changes in the structure such as etch back thickness and diameter of the nanowire. These two parameters gave unique Mueller matrix responses, which can be monitored individually. In this simulation study, the nanowire structure was shown to have a differentiable Mueller matrix response for changes to key parameters.
The Effect of Environmental Modifications on Preventing Delirium for the Elderly Patients in the Intensive Care Unit: A Nonrandomized Controlled Trial

Objective: The study aimed to examine the effect of environmental modifications on preventing delirium for the elderly patients in the intensive care unit. A quasi-experimental study with non-randomized independent two groups. Materials and Methods: The study was conducted at a hospital in Bolu, Turkey. The sample was constituted of 60 patients who met the inclusion criteria. In the intervention group (30 patients), the sound level of the environment was improved, the bright light, a largely written calendar and clock were used, and were allowed to use their glasses or hearing aids if they had. No extra regulation has been made regarding the temperature or humidity of the environment. Results: Mean humidity level and the mean sound level in the unit were different, while the mean of the temperature was similar between the groups. The mean sound level was higher in control group. In the intensive care unit, the frequency of delirium was 56.7%, and the risk of delirium was 2.32 times higher in the control group. Conclusion: The study provides scientific evidence to reduce the risk of delirium by a specific care bundle include nursing interventions for elderly patients in intensive care units.
Falls in Older Women and Men: Associated Factors and Sarcopenia

Objective: Falls are the fifth leading cause of death among the older adults, and most of the risk factors of falls are modifiable. We aimed to investigate the clinical factors associated with falls comprehensively, by evaluating the sarcopenia components separately, in each gender. Materials and Methods: This is a cross-sectional study. Six hundred thirty-three female and 269 male outpatients, underwent a comprehensive geriatric assessment, were included. Falls, in the previous 12 months, were recorded. Geriatric evaluation regarding frailty, depression, nutrition, cognitive functions, and disabilities were done with related scales. SARC-F determined sarcopenia risk, and probable sarcopenia was defined according to handgrip strength. Skeletal muscle mass index and muscle performance were measured by bioimpedance analysis and physical performance tests, including 4-meter gait speed (4-m GS) and timed up and go (TUG) tests, respectively. Results: In the study population 35.2% of female and 30.9% of male patients experienced falls. In multivariate analysis, probable sarcopenia in women [odds ratio (OR): 1.56, p=0.034] and longer TUG test performance in men (OR: 1.06, p=0.020) were independently related to falls. In addition, both in women and men urinary incontinence (OR: 1.62, p=0.016 and OR: 1.95, p=0.045, respectively), sarcopenia risk defined by SARC-F (OR: 2.74, p≤0.001 and OR:4.79, p≤0.001, respectively) and depression (OR: 1.56, p=0.025 and OR: 1.93, p=0.046, respectively) were independently related with falls. Conclusion: Depression, incontinence, and sarcopenia risk were independent associated factors for falls in both genders. SARC-F appears to be effective in both genders in predicting the risk of falls. However, muscle strength in women and muscle performance in men get high impact for the falls.
Variation in the Elastic Modulus and Increased Energy Dissipation Induced by Cyclic Straining of Argiope bruennichi Major Ampullate Gland Silk

The trends exhibited by the parameters that describe the mechanical behaviour of major ampullate gland silk fibers spun by Argiope bruennichi spiders is explored by performing a series of loading-unloading tests at increasing values of strain, and by the subsequent analysis of the true stress-true strain curves obtained from these cycles. The elastic modulus, yields stress, energy absorbed, and energy dissipated in each cycle are computed in order to evaluate the evolution of these mechanical parameters with this cyclic straining. The elastic modulus is observed to increase steadily under these loading conditions, while only a moderate variation is found in the yield stress. It is also observed that a significant proportion of the energy initially absorbed in each cycle is not only dissipated, but that the material may recover partially from the associated irreversible deformation. This variation in the mechanical performance of spider silk is accounted for through a combination of irreversible and reversible deformation micromechanisms in which the viscoelasticity of the material plays a leading role.
Convolution and Hankel operator norms for linear systems

Some novel operator norms for the convolution and Hankel operators of time-invariant, linear systems are introduced. Using these norms, new controller design problems arise which will require modifications to the previous H/sup 2/ approach, where a trace operator was used in the performance index. The results suggest that the lambda /sub max/ and d/sub max/ operators are more appropriate if the intention is actually to limit the amplitude variations of the controlled variables rather than minimize mean square error. <<ETX>>
Efficient Simulation of the Multi Asset Heston Model

This paper describes a procedure for efficiently simulating a multi asset Heston model with an arbitrary correlation structure. Very little literature can be found on the topic (e.g. Wadman (2010) and Dimitroff et al. (2011)), the latter being very restrictive on correlation assumptions. The scheme proposed in this text is based on Andersen's Quadratic Exponential (QE) scheme (2008) and operates with an arbitrary input correlation structure, which is partially decorrelated via a Gaussian copula approach to fit the single asset QE prerequisites. Given a long term horizon, it is shown numerically that, in the multi asset QE (MQE) scheme, all combinations of terminal correlations converge quickly to the true terminal correlations for decreasing Monte Carlo time step size, if the input correlation matrix is interpreted as the system's instantaneous correlation matrix. Convergence of vanilla and spread option prices is investigated, in order to verify the appropriate behaviour for higher moments of the marginal and the joint distribution under MQE. Finally, the superiority of MQE vs. Taylor based schemes is shown by comparing convergence of the empirical PDF, calculated with Monte Carlo, to the "exact" function calculated via Fourier inversion.
No-reference image quality assessment based on statistical model of natural image

A new no-reference image quality assessment method was proposed based on the statistical model of natural images in order to assess the distortion of white noise,Gaussian blur and JPEG2000 compression.For natural images,the means of Contourlet subband coefficient amplitudes (MSC) decreased approximately linearly with scale index.Then MSC of high frequency was predicted from MSC of low frequency,which was not badly affected by distortion.The distortion was evaluated using the difference between the predicted means and the real means in distorted image.The metric of subband distortion for different scales,different directions,and different regions was weighted and pooled into a quality score considering characteristic of human visual system.Experimental results showed that the method was consistent with subjective assessment,and exceeded the performance of peak signal to noise rate (PSNR).
Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks

In this paper, we provide a theory of using graph neural networks (GNNs) for \textit{multi-node representation learning}, where we are interested in learning a representation for a set of more than one node such as a link. Existing GNNs are mainly designed to learn single-node representations. When we want to learn a node-set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN. In this paper, we show a fundamental limitation of such an approach, namely the inability to capture the dependence among multiple nodes in a node set, and argue that directly aggregating individual node representations fails to produce an effective joint representation for multiple nodes. A straightforward solution is to distinguish target nodes from others. Formalizing this idea, we propose \text{labeling trick}, which first labels nodes in the graph according to their relationships with the target node set before applying a GNN and then aggregates node representations obtained in the labeled graph for multi-node representations. The labeling trick also unifies a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, ID-GNN, and NBFNet. Besides node sets in graphs, we also extend labeling tricks to posets, subsets and hypergraphs. Experiments verify that the labeling trick technique can boost GNNs on various tasks, including undirected link prediction, directed link prediction, hyperedge prediction, and subgraph prediction. Our work explains the superior performance of previous node-labeling-based methods and establishes a theoretical foundation for using GNNs for multi-node representation learning.
Radar de Parité: An NLP system to measure gender representation in French news stories

We present the Radar de Parit\'e, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new contribution to the NLP literature on French. We also showcase statistics covering over one year's worth of data (282,512 news articles). Our results highlight the underrepresentation of women in news stories, while also illustrating the application of modern NLP methods to measure gender representation and address societal issues.
Accelerating analysis of Boltzmann equations using Gaussian mixture models: Application to quantum Bose-Fermi mixtures

The Boltzmann equation is a powerful theoretical tool for modeling the collective dynamics of quantum many-body systems subject to external perturbations. Analysis of the equation gives access to linear response properties including collective modes and transport coefficients, but often proves intractable due to computational costs associated with multidimensional integrals describing collision processes. Here, we present a method to resolve this bottleneck, enabling the study of a broad class of many-body systems that appear in fundamental science contexts and technological applications. Specifically, we demonstrate that a Gaussian mixture model can accurately represent equilibrium distribution functions, thereby allowing efficient evaluation of collision integrals. Inspired by cold atom experiments, we apply this method to investigate the collective behavior of a quantum Bose-Fermi mixture of cold atoms in a cigar-shaped trap, a system that is particularly challenging to analyze. We focus on monopole and quadrupole collective modes above the Bose-Einstein transition temperature, and find a rich phenomenology that spans interference effects between bosonic and fermionic collective modes, dampening of these modes, and the emergence of hydrodynamics in various parameter regimes. These effects are readily verifiable experimentally.