Miracle-dz/UniFAR-FTUs huggingface dataset:
Missile Autopilot Design: Gain-Scheduling and the Gap Metric

This paper presents a systematic methodology for the synthesis of global gain-scheduled controllers for nonlinear time-varying systems. A controller of this type is used to compute the pitch-axis autopilot of an air-air missile. The missile model used is considered a benchmark for testing autopilot controllers in the academic and industrial communities. The missile's dynamics are linearized at a small set of operating points for which proportional― integral/proportional-type controllers are designed, to shape the frequency response of the linear plants' dynamics. A new set of operating points is computed afterward using the connection between the gap metric and the H ∞ loop-shaping theory. Then, reduced-order, static, H ∞ loop-shaping controllers are designed for this set of points using linear matrix inequality optimization techniques. Finally, the global gain-scheduled controller is obtained by interpolating the proportional―integral/proportional and the loop-shaping controllers' gains over the missile's flight envelope. The simulation results given show the generality and effectiveness ofthe proposed control strategy in terms of the operating point selection, stability, performance and robustness, of the closed loop.
Rainfall Characteristics Associated with the Remnants of Tropical Storm Helene in Upstate South Carolina

Abstract A description is given of rainfall characteristics associated with the remnants of Tropical Storm Helene, which made landfall on the panhandle of Florida and moved through parts of Georgia, South Carolina, and North Carolina during 22–23 September 2000. Also included in the description are rainfall events in advance of the approach of Helene to South Carolina. Emphasis is placed on the implications of these characteristics regarding the ability of the Weather Surveillance Radar-1988 Doppler (WSR-88D) to measure rainfall accurately for this system when employing reflectivity factor–rainfall rate (Z–R) algorithms as defined by the National Weather Service. The results are based on analysis of raindrop size spectra collected with a disdrometer in upstate South Carolina at the Clemson University Atmospheric Research Laboratory. It was found that there were pronounced variations in the Z–R relationship from day to day and within a day that would limit the accuracy of radar measurement of rainfall. A d.
Integrated photometric redshifts and SED fitting as tool for galaxy evolution studies

Abstract Photometric redshifts, i.e. redshifts derived by comparing an observed spectral-energy distribution (SED) to a range of empirical or theoretical SED templates, are commonly used in studies of the high-redshift Universe. Often, the next step is to use these redshifts as fixed input parameters for SED fitting to derive physical properties for each galaxy. However, this two-step approach ignores degeneracies between redshift and, e.g., stellar mass. Here I present first results using an improved approach that integrates both methods. I find that mass determinations are, on average, three times more uncertain than they seem from the common two-step approach. If not accounted for, these underestimated uncertainties can impact our ability of making meaningful comparisons between observations and simulations of galaxy evolution.
Age Rather Than Supplementation with Oat β-Glucan Influences Development of the Intestinal Microbiota and SCFA Concentrations in Suckling Piglets

Simple Summary Early development of the intestinal microbiota is considered critical for enteric health in pigs. Dietary strategies that improve gut microbiota development can thus help prevent disturbances associated with weaning. This study evaluated the effects of a soluble oat β-glucan supplement, compared with a placebo, in five litters of piglets during the suckling period. The supplement was introduced at week 1 of age and was provided three times per week until weaning. The effects of the supplement were evaluated in terms of weight development, concentrations of short-chain fatty acids in plasma and intestinal contents, and microbiota composition in rectal swabs and intestinal contents. The results showed that microbiota composition and short-chain fatty acid concentration changed with piglet age, but with no clear effects of the β-glucan supplement. There were no differences in body weight gain between β-glucan-supplemented and control piglets. Several correlations between specific microbes and short-chain fatty acids were identified. Significant litter effects were also found, confirming the importance of genetics and pen environment in suckling piglets. Abstract The effects of early supplementation with oat β-glucan during the suckling period on piglet gut microbiota composition, concentrations of short-chain fatty acids, and gut physiological markers were assessed. Fifty piglets from five litters, balanced for sex and birth weight, were divided within litters into two treatment groups: β-glucan and control. Piglets in the β-glucan group received the supplement three times/week from day 7 of age until weaning. Rectal swab samples were collected from 10 piglets per treatment group (balanced across litters) from week 1 to week 4, and plasma samples were collected at 1, 3, and 4 weeks of age. Additional samples of intestinal tissues and jugular and portal vein plasma were collected from 10 animals at weaning (one per treatment group and litter). The concentrations of short-chain fatty acids in plasma and the microbiota composition in rectal swabs were mainly influenced by piglet age, rather than the supplement. There were significant differences in microbiota composition between litters and several correlations between concentrations of short-chain fatty acids in plasma and specific microbial taxa in rectal swabs. Overall, β-glucan supplementation did not have any clear impact on the gut environment in suckling piglets, whereas a clear age-related pattern emerged.
Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models

In this article, we introduce the general setting of a multivariate time series autoregressive model with stochastic time‐varying coefficients and time‐varying conditional variance of the error process. This allows modelling VAR dynamics for non‐stationary time series and estimation of time‐varying parameter processes by the well‐known rolling regression estimation techniques. We establish consistency, convergence rates, and asymptotic normality for kernel estimators of the paths of coefficient processes and provide pointwise valid standard errors. The method is applied to a popular seven‐variable dataset to analyse evidence of time variation in empirical objects of interest for the DSGE (dynamic stochastic general equilibrium) literature.
Resonance scaling of circle maps

Throughout this thesis we deal with the scaling properties of resonance (or Arnol'd) tongues of circle maps. The motivation comes from the work of A.M. Davie, which we summarise as necessary. We deal initially with the Sine Circle Map in the form fk,c(x) = x + ci + k sin2 (x) mod 7r, where 0 < Il < ir and 0 < k < 1, and consider rotation numbers of the form a =. Here we investigate the scaling behaviour of the intervals I(k), where, for 3 ci E Li. , fk,c has rotation number , as n -+ oo. We know that IIj (k)I 7kfl. Our cncern is with estimating the behaviour of Yk as k -+ 0, to'which end we improve on the estimates made in the original work by Davie, and consider the effect of errors neglected in the first order approximations. In chapter 3 we deal with the same map, but now considering rotation numbers of the form a = (n + )_1, where n,p, q € N, with p,q co-prime. We investigate the widths of the intervals Ia. Specifically, we consider the asymptotic behaviour of hal as n 00 and k 0 in any manner. This behaviour is related to a polynomial in the first q Fourier coefficients of a particular period 1 map, derived from a transformation on the circle map. We derive the appropriate polynomials for values of q < 20. In the final chapter we consider circle maps derived from the Dissipative Standard Map, fk,n(x,O) = (Jx ksin(2ir9), 9 + ci + Jx ksin(27r0)), where 0 < J < 1. The map may be regarded as a map of the cylinder It x S 1 into itself. The theory of normal hyperbolicity shows that if k is small enough there exists a circle, V, homotopic to {0} x S i , which is invariant under the action of fk,c, and on which is induced an orientation preserving circle homeomorphism. We obtain an approximation to the circle and the associated homeomorphism when ci = cit, such that fk,Q lv has a fixed point, proceeding to investigate the scaling of I I. I from this basis. Finally, we numerically estimate the quantity derived in th'e analytical part of this chapter.
Investigating the Relationships between Taste Preferences and Beverage Intake in Preadolescents

Sugar-sweetened beverages are known promotors of adverse health outcomes. This study aimed to find a relation between taste perception, preferences for beverages, anthropometric parameters, and frequency of beverage consumption. Taste perception of sweetness was tested using an adopted sensitivity test with sucrose and different concentrations of sugar-sweetened apple juice. Furthermore, bitter-compound 6-n-propylthiouracil (PROP) and salty perception were tested and accompanied by a questionnaire on beverage intake. We did not find a clear relationship between taste perception, anthropometrics, and beverage intake. Nevertheless, in males, the bitter intensity perception of PROP was positively correlated with the BMI percentiles (CDC, r = 0.306, p ≤ 0.043) and the waist circumference (r = 0.326, p = 0.031). Furthermore, the liking of sweet taste (p < 0.05) and sweet intensity rating (p < 0.05) of apple juice increased with intensity, and adolescents with overweight or obesity had a higher intake of free sugars from beverages (p < 0.001). The role of taste perception on anthropometric measures and beverage intake remains unclear and requires further investigation.
Rheological and Thermal Behavior of Choline-based Deep Eutectic Ionic Liquid and its Impact on a Poorly Soluble Drug Model

Objective: This study was conducted to prepare a deep eutectic ionic liquid based on choline chloride and malonic acid (Maline) in diff erent molar ratios and evaluate the maline properties to designate the best ratio for solubilizing risperidone, a poorly soluble drug model. Method: Malines were prepared using choline chloride and malonic acid in 1:0.8, 1:0.9 and 1:1 molar ratios. The characterization of malines involved pH rating, rheological test and thermal behavior using DSC. In addition to estimating the interactions that occur between the choline chloride and malonic acid to form the malines theoretically using the computational prediction program Mercury and experimentally by 1H-NMR and FTIR. Results: This study shows that all malines (M1-M3) represents an acidic pH and high viscosity with a non-newtonian behavior (shear thinning property ) at low temperature while a Newtonian behavior (shear rate-independent) at high temperatures. In malines (M1-M3) thermograms, the absence of pure compounds melting point peaks with a glass transition temperature at -14○C that confi rms the DES property. FTIR and 1H-NMR results represent a hydrogen bond formation between choline chloride and malonic acid and further between maline and risperidone which is similar to the computational prediction. Maline (M1) with molar ratio of 1:1 had a preferable solubilizing eff ect on risperidone reaching 20.5 mg/mL, while in (M2) (1:0.9) reaches 13.6 mg/mL, and in (M3) reaches 11.9 mg/mL. Conclusion: Maline (M1) was chosen as the optimum molar ratio to form a deep eutectic ionic liquid that successfully enhances risperidone solubility and boosts its bioavailability.
Knowledge and Practices of Cypriot Bovine Farmers towards Effective and Safe Manure Management

Simple Summary Manure generated in bovine farms may pose human and animal health risks. If not managed and used properly, these risks may be spread to other areas. The risks can be minimised when farmers are aware of them and apply optimum practices at their farms, at storage, processing, and when manure is applied to land as a fertiliser. This research aims to evaluate the Cypriot bovine farmers’ knowledge about the risks from manure and the practices currently applied for manure management through a questionnaire survey. The results indicated some gaps in farmers’ knowledge and some deficiencies in employing optimal management practices. The education level and the farming purpose were identified as determinants of the level of farmers’ knowledge. The conclusion was that farmers’ knowledge must be reinforced through specialised training to ensure proper manure management. Although the current practices partially decrease manure pathogens, interventions to promote the use of more effective treatment methods, such as biogas transformation and composting, would be beneficial. The results could help Cypriot and other competent authorities to develop an action plan to control the health risks by educating farmers, promoting the use of more efficient management methods and developing new legislation. Abstract Manure from bovine farms is commonly used as an organic fertiliser. However, if not properly managed, it can spread significant biological and chemical hazards, threatening both human and animal health. The effectiveness of risk control hugely relies on farmers’ knowledge regarding safe manure management and on the application of suitable management practices. This study aims to evaluate the knowledge and practices of Cypriot bovine farmers towards safer manure management, from its generation to its final use, in line with the One Health approach. Factors affecting farmers’ knowledge and applied practices are also investigated through a questionnaire survey. The questionnaire was developed and sent to all eligible bovine farmers in Cyprus (n = 353), and 30% (n = 105) of them returned the completed questionnaire. Results revealed there are some gaps in farmers’ knowledge. The use of manure for fertilising crops dominated. Only half of the farmers stored manure in appropriate facilities, with 28.5% of them using a dedicated area with cement floors and 21.5% utilising leakproof tanks. The majority (65.7%) stored manure for more than three months before its use as a fertiliser in a dried form. In multiple regression analysis, education level and farming purpose were significant determinants of farmer knowledge. In conclusion, Cypriot farmers’ knowledge must be reinforced to ensure proper manure management. The results highlight the importance of providing relevant training to farmers. Although the current practices partially decrease manure pathogens, interventions to promote the use of more effective treatment methods, such as biogas transformation and composting, would be beneficial.
Convergence rates in the nonrelativistic limit of the cubic Klein-Gordon equation

In this paper, we study the nonrelativistic limit of the cubic nonlinear Klein-Gordon equation in $\mathbb{R}^{3}$ with a small parameter $0<\varepsilon \ll 1$, which is inversely proportional to the speed of light. We show that the cubic nonlinear Klein-Gordon equation converges to the cubic nonlinear Schr\"odinger equation with a convergence rate of order $\varepsilon^{2}$. In particular, for the defocusing case and smooth initial data, we prove error estimates of the form $(1+t)\varepsilon^{2}$ at time $t$ which is valid up to long time of order $\varepsilon^{-1}$; while for nonsmooth initial data, we prove error estimates of the form $(1+t)\varepsilon$ at time $t$ which is valid up to long time of order $\varepsilon^{-\frac{1}{2}}$. These specific forms of error estimates coincide with the numerical results obtained in \cite{BZ19,SZ20}.
Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel Space

We introduce Dynamic Mobile-Former(DMF), maximizes the capabilities of dynamic convolution by harmonizing it with efficient operators.Our Dynamic MobileFormer effectively utilizes the advantages of Dynamic MobileNet (MobileNet equipped with dynamic convolution) using global information from light-weight attention.A Transformer in Dynamic Mobile-Former only requires a few randomly initialized tokens to calculate global features, making it computationally efficient.And a bridge between Dynamic MobileNet and Transformer allows for bidirectional integration of local and global features.We also simplify the optimization process of vanilla dynamic convolution by splitting the convolution kernel into an input-agnostic kernel and an input-dependent kernel.This allows for optimization in a wider kernel space, resulting in enhanced capacity.By integrating lightweight attention and enhanced dynamic convolution, our Dynamic Mobile-Former achieves not only high efficiency, but also strong performance.We benchmark the Dynamic Mobile-Former on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection, and instanace segmentation.For example, our DMF hits the top-1 accuracy of 79.4% on ImageNet-1K, much higher than PVT-Tiny by 4.3% with only 1/4 FLOPs.Additionally,our proposed DMF-S model performed well on challenging vision datasets such as COCO, achieving a 39.0% mAP,which is 1% higher than that of the Mobile-Former 508M model, despite using 3 GFLOPs less computations.Code and models are available at https://github.com/ysj9909/DMF
Iron-peak Element Abundances in Warm Very Metal-poor Stars

We have derived new detailed abundances of Mg, Ca, and the Fe-group elements Sc through Zn (Z = 21−30) for 37 main-sequence turnoff very metal-poor stars ([Fe/H] ≲−2.1). We analyzed Keck HIRES optical and near-UV high signal-to-noise spectra originally gathered for a Be abundance survey. Using typically ∼400 Fe-group lines with accurate laboratory transition probabilities for each star, we have determined accurate LTE metallicities and abundance ratios for neutral and ionized species of the 10 Fe-group elements as well as α elements Mg and Ca. We find good neutral/ion abundance agreement for the six elements that have detectable transitions of both species in our stars in the 3100–5800 Å range. Earlier reports of correlated Sc−Ti−V relative overabundances are confirmed, and appear to slowly increase with decreasing metallicity. To this element trio we add Zn; it also appears to be increasingly overabundant in the lowest-metallicity regimes. Co appears to mimic the behavior of Zn, but issues surrounding its abundance reliability cloud its interpretation.
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)

Large Language Models (LLM) are a new class of computation engines,"programmed"via prompt engineering. We are still learning how to best"program"these LLMs to help developers. We start with the intuition that developers tend to consciously and unconsciously have a collection of semantics facts in mind when working on coding tasks. Mostly these are shallow, simple facts arising from a quick read. For a function, examples of facts might include parameter and local variable names, return expressions, simple pre- and post-conditions, and basic control and data flow, etc. One might assume that the powerful multi-layer architecture of transformer-style LLMs makes them inherently capable of doing this simple level of"code analysis"and extracting such information, implicitly, while processing code: but are they, really? If they aren't, could explicitly adding this information help? Our goal here is to investigate this question, using the code summarization task and evaluate whether automatically augmenting an LLM's prompt with semantic facts explicitly, actually helps. Prior work shows that LLM performance on code summarization benefits from few-shot samples drawn either from the same-project or from examples found via information retrieval methods (such as BM25). While summarization performance has steadily increased since the early days, there is still room for improvement: LLM performance on code summarization still lags its performance on natural-language tasks like translation and text summarization. We find that adding semantic facts actually does help! This approach improves performance in several different settings suggested by prior work, including for two different Large Language Models. In most cases, improvement nears or exceeds 2 BLEU; for the PHP language in the challenging CodeSearchNet dataset, this augmentation actually yields performance surpassing 30 BLEU.
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoor scene understanding. We design a 3D Swin transformer as our backbone network, which enables efficient self-attention on sparse voxels with linear memory complexity, making the backbone scalable to large models and datasets. We also introduce a generalized contextual relative positional embedding scheme to capture various irregularities of point signals for improved network performance. We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset. Our model pretrained on the synthetic dataset not only generalizes well to downstream segmentation and detection on real 3D point datasets, but also outperforms state-of-the-art methods on downstream tasks with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, +1.8 mIoU on ScanNet segmentation (val), +1.9 mAP@0.5 on ScanNet detection, and +8.1 mAP@0.5 on S3DIS detection. A series of extensive ablation studies further validate the scalability, generality, and superior performance enabled by our approach. The code and models are available at https://github.com/microsoft/Swin3D .
Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks

We introduce a method to estimate simultaneously the tail and the threshold parameters of an extreme value regression model. This standard model finds its use in finance to assess the effect of market variables on extreme loss distributions of investment vehicles such as hedge funds. However, a major limitation is the need to select ex ante a threshold below which data are discarded, leading to estimation inefficiencies. To solve these issues, we extend the tail regression model to non-tail observations with an auxiliary splicing density, enabling the threshold to be selected automatically. We then apply an artificial censoring mechanism of the likelihood contributions in the bulk of the data to decrease specification issues at the estimation stage. We illustrate the superiority of our approach for inference over classical peaks-over-threshold methods in a simulation study. Empirically, we investigate the determinants of hedge fund tail risks over time, using pooled returns of 1,484 hedge funds. We find a significant link between tail risks and factors such as equity momentum, financial stability index, and credit spreads. Moreover, sorting funds along exposure to our tail risk measure discriminates between high and low alpha funds, supporting the existence of a fear premium.