Miracle-dz/UniFAR-FTUs huggingface dataset:
Input–Output Finite-Time Reliable Static Output Control of Time-Varying System With Input Delay

This paper investigates input–output finite-time reliable static output feedback (SOF) control of a time-varying system under the influence of both input time delay and actuator failures. An actuator fault model consisting of linear and nonlinear faults is considered during the time-varying control process. The objective is to design a reliable SOF controller that can ensure input–output finite-time stability (IO-FTS) of the resulting closed-loop system. An augmented time-varying Lyapunov functional is constructed, in which some Lyapunov matrices are variable function of time ${t}$ . By dividing the time interval and delay interval into equal segments, the matrix-valued functions are expressed by a linear interpolation formula. Moreover, combining with the single and double Wirtinger-based integral inequalities, delay-dependent IO-FTS conditions are derived. It is shown that the SOF control issue is solved in forms of linear matrix inequalities. In the end, the effectiveness is demonstrated by simulations.
On optimality of Mallows model averaging

In the past decades, model averaging (MA) has attracted much attention as it has emerged as an alternative tool to the model selection (MS) statistical approach. Hansen [\emph{Econometrica} \textbf{75} (2007) 1175--1189] introduced a Mallows model averaging (MMA) method with model weights selected by minimizing a Mallows' $C_p$ criterion. The main theoretical justification for MMA is an asymptotic optimality (AOP), which states that the risk/loss of the resulting MA estimator is asymptotically equivalent to that of the best but infeasible averaged model. MMA's AOP is proved in the literature by either constraining weights in a special discrete weight set or limiting the number of candidate models. In this work, it is first shown that under these restrictions, however, the optimal risk of MA becomes an unreachable target, and MMA may converge more slowly than MS. In this background, a foundational issue that has not been addressed is: When a suitably large set of candidate models is considered, and the model weights are not harmfully constrained, can the MMA estimator perform asymptotically as well as the optimal convex combination of the candidate models? We answer this question in a nested model setting commonly adopted in the area of MA. We provide finite sample inequalities for the risk of MMA and show that without unnatural restrictions on the candidate models, MMA's AOP holds in a general continuous weight set under certain mild conditions. Several specific methods for constructing the candidate model sets are proposed. Implications on minimax adaptivity are given as well. The results from simulations back up our theoretical findings.
CDF II W-mass anomaly and SO(10) GUT

The W-mass anomaly has yet to be established, but a huge proliferation of articles on the subject established the rich potential of such event. We investigate the SO(10) GUT constraints from the recently reported W-mass anomaly. We consider both Supersymmetric (SUSY) and non-supersymmetric (non-SUSY) grand unified theories by studying renormalization group equations (RGEs) for gauge coupling unification and their predictions on proton decay. In the non-SUSY models, single-stage unification is possible if one include a light (around TeV) real triplet Higgs scalar. However, these models predict speedy proton decay, inconsistent with the present experimental bound on the proton decay. This situation may be improved by including newer scalars and new intermediate-mass scales, which are present in the $SO(10)$ GUTs. The standard model is extended to a left-right symmetric model (LR), and the scale of LR breaking naturally introduces the intermediate scale in the model. A single-stage unification is possible even without including any triplet Higgs scalar in a minimal supersymmetric standard model.
Devil in the Number: Towards Robust Multi-modality Data Filter

In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs. For instance, LAION papers employs the CLIP score filter to select data with CLIP scores surpassing a certain threshold. On the other hand, T-MARS achieves high-quality data filtering by detecting and masking text within images and then filtering by CLIP score. Through analyzing the dataset, we observe a significant proportion of redundant information, such as numbers, present in the textual content. Our experiments on a subset of the data unveil the profound impact of these redundant elements on the CLIP scores. A logical approach would involve reevaluating the CLIP scores after eliminating these influences. Experimentally, our text-based CLIP filter outperforms the top-ranked method on the ``small scale"of DataComp (a data filtering benchmark) on ImageNet distribution shifts, achieving a 3.6% performance improvement. The results also demonstrate that our proposed text-masked filter outperforms the original CLIP score filter when selecting the top 40% of the data. The impact of numbers on CLIP and their handling provide valuable insights for improving the effectiveness of CLIP training, including language rewrite techniques.
Probing the Moral Development of Large Language Models through Defining Issues Test

In this study, we measure the moral reasoning ability of LLMs using the Defining Issues Test - a psychometric instrument developed for measuring the moral development stage of a person according to the Kohlberg's Cognitive Moral Development Model. DIT uses moral dilemmas followed by a set of ethical considerations that the respondent has to judge for importance in resolving the dilemma, and then rank-order them by importance. A moral development stage score of the respondent is then computed based on the relevance rating and ranking. Our study shows that early LLMs such as GPT-3 exhibit a moral reasoning ability no better than that of a random baseline, while ChatGPT, Llama2-Chat, PaLM-2 and GPT-4 show significantly better performance on this task, comparable to adult humans. GPT-4, in fact, has the highest post-conventional moral reasoning score, equivalent to that of typical graduate school students. However, we also observe that the models do not perform consistently across all dilemmas, pointing to important gaps in their understanding and reasoning abilities.
Diverse Semantic Image Editing with Style Codes

Semantic image editing requires inpainting pixels following a semantic map. It is a challenging task since this inpainting requires both harmony with the context and strict compliance with the semantic maps. The majority of the previous methods proposed for this task try to encode the whole information from erased images. However, when an object is added to a scene such as a car, its style cannot be encoded from the context alone. On the other hand, the models that can output diverse generations struggle to output images that have seamless boundaries between the generated and unerased parts. Additionally, previous methods do not have a mechanism to encode the styles of visible and partially visible objects differently for better performance. In this work, we propose a framework that can encode visible and partially visible objects with a novel mechanism to achieve consistency in the style encoding and final generations. We extensively compare with previous conditional image generation and semantic image editing algorithms. Our extensive experiments show that our method significantly improves over the state-of-the-art. Our method not only achieves better quantitative results but also provides diverse results. Please refer to the project web page for the released code and demo: https://github.com/hakansivuk/DivSem.
A hybrid quantum-classical approach to warm-starting optimization

The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting the standard algorithm improves the performance. In this paper we compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization and investigate the warm-start approach for different problem instances. In particular, we analyze the extent to which the improved performance of warm-start QAOA is due to quantum effects, and show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.
Do vasomotor nerves significantly regulate cerebral blood flow?

IT IS worthwhile noting at the outset that this controversy is not new, the topic having been debated with vigor for the past 100 years or so. What has changed is the way in which the question at issue has been posed. In earlier experiments, the question was whether or not vasomotor nerves existed; but once they had been demonstrated, the problem was to assign a functional role to them. By the end of the 1930's, during which the action of vasomotor nerves and various drugs on pial vessels had been studied intensively, this question still was far from being resolved. On the one hand, it was clear that there were vasomotor nerves, and it was equally clear that section or stimulation of these nerves could markedly affect the caliber of pial vessels. On the other hand, the responses of pial vessels to such disturbances as hypoxia, hypercapnia, and hypotension were similar whether the nervous pathways were intact or not. Hence it was concluded that vasomotor nerves played little part in the regulation of cerebral vessels; the more important factors were intrinsic to the vascular bed. In the intervening years, extensive research has, if anything, sharpened the controversy. On the one hand, morphological and histological studies have shown that, in virtually every respect, the density of the adrenergic and cholinergic plexus on cerebral blood vessels and the appearance of the terminals at the neuromuscular junction are identical to those in other vascular beds. Pharmacological studies have identified adrenergic constrictor and cholinergic dilator receptors on smooth muscle, and experiments in which cerebral blood flow has been measured quantitatively have in most cases confirmed the earlier actions of vasomotor nerves. On
The Far Reach of Megathrust Earthquakes: Evolution of Stress, Deformation and Seismicity Following the 2004 Sumatra-Andaman Rupture

Starting with the 2004 Mw 9.2 megathrust event, Southeast Asia has been home to an exceptional amount of seismic activity over the past eight years. The series of megathrust earthquakes have been imperfect dominoes, rupturing the northernmost section of the Sunda subduction zone in 2004, then the Nias segment next in line to the south in 2005, followed by the Bengkulu earthquake s750 km further south in 2007. The Bengkulu earthquake skipped over the northern Mentawai segment, which has not ruptured in a great event since 1797. However, the subduction zone has not been silent is this section. Analysis of focal mechanisms and geodetic data reveals the reactivation of the Mentawai backthrust system in the overriding plate, and a large, deep earthquake near the city of Padang in 2009 is shown through finite fault inversions and aftershock analysis to have obliquely ruptured the subducting slab. At the same time, the entire region spanning from the Indian Ocean, through the trench and forearc islands, and throughout Thailand has been aseismically deforming in response to the stress changes in the mantle following the megathrust earthquakes. Geodetic observations of postseismic deformation during the first five years following the 2004 earthquake have shown that the far-field regions of Thailand and the Malay Peninsula have moved more postseismically than coseismically, peaking at s0.4 m of horizontal displacement in Phuket. In 2012, the stress changes associated with this continued postseismic deformation, along with the initial push from the megathrust earthquakes, appears to have triggered the largest instrumentally recorded strike-slip earthquake. This was a complex earthquake, consisting of four conjugate fault segments, that ruptured the diffuse India-Australia plate boundary zone. Understanding how the faults interact throughout the subduction system, from the incoming plate, to the slab, to the megathrust interface, and overriding plate is an essential part of determining the future seismic hazard for Southeast Asia.
Modeling Optical Turbulence with COAMPS During Two Observation Periods at Vandenberg AFB

Abstract : The objective of this thesis is to investigate the forecastability of optical turbulence using the U.S. Navy's Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS). First, a detailed synoptic study was performed over the Eastern Pacific region for observation periods in October 2001 and March 2002 to focus on mesoscale features affecting Vandenberg AFB. Second, a modified version of COAMPS version 2.0. 16 model output was evaluated to ensure reasonable modeling of the mesoscale. Next, temperature and dewpoint temperature vertical profiles of COAMPS, modified with the Turbulent Kinetic Energy (TKE) Method, were compared with balloon-launched rawinsondes, initially, then with higher resolution thermosondes. Optical turbulence parameters were then calculated from the data and a comparison between synthetic profiles and thermosonde-derived profiles were qualitatively and quantitatively studied. Then the vertical resolution of the model was increased for selected forecasts to determine the potential for forecast improvement.
A CONSISTENT SCHEME FOR ESTIMATING DIFFUSIVITIES TO BE USED IN AIR QUALITY MODELS

A vertical diffusivity model consistent with the latest developments in the theory of the atmospheric boundary layer is developed. It considers the Pasquill stability classes, the Monin-Obukhov mixing length, land use and phi-functions. The model constitutes a "cookbook" procedure for estimating vertical diffusivity from easily available meteorology and land use information. A method of interfacing the diffusivity model with grid and trajectory models is discussed. The combination of these models will allow transportation planners and engineers to evaluate the interrelationships of land use, transportation and air quality planning. /FHWA/
Assessing Rodent Species Counts and Diversity in the Not-Grazed Montana De Oro State Park and the Rotationally Grazed Pecho Ranch

Assessing Rodent Species Counts and Diversity in the Not-Grazed Montana De Oro State Park and the Rotationally Grazed Pecho Ranch Nancy Marie Lemos The available data examining the influence of rotational grazing on rodent responses are limited. This study investigated how rotational livestock grazing practices influence small mammal rodent abundances and species diversity. We looked for evidence of variation in the occurrence and/or numbers of certain rodent species among three plant community types (grassland, shrubland, grass-shrub mix), managed with or without grazing. We used Sherman live traps over a total of eight trapping sessions. We totaled 486 trapnights for each of the six plant community and grazing management combinations. The three plant community types were identified by visual cover. Out of the eight different species we captured, only Peromyscus maniculatus (North American deermouse) and Reithrodontomys megalotis (western harvest mouse) had sufficient captures to be analyzed for differences in abundance. Using all eight species to calculate the Shannon’s diversity index for each plot, we found evidence that rodent species diversity is less in grassland habitats than in shrubland habitats or grassland/shrubland mixed habitats (p<0.001). We observed that the North American deermouse was most common in the not-grazed shrub habitat (p<0.05). The North American deermouse preferred the not-grazed grassland habitat to the grazed grassland habitat, and the not-grazed shrub habitat to the grazed shrub habitat. The North American deermouse was more prevalent in the grazed grassland/shrubland mixed habitats than the not-grazed mixed habitat. The western harvest mouse preferred the not-grazed grassland habitat to the grazed grassland habitat. Based on our results, the current rotational livestock grazing practices do not influence biological diversity, so no management adjustments are needed. The North American deermouse abundance would benefit from not-grazing management in a grassland community or in a shrubland community, but would benefit from rotationally grazing management in a mixed grassland/shrubland habitat. From a management perspective, the western harvest mouse would benefit from not-grazing in a grassland community, but appears unaffected by grazing in the shrubland and mixed communities.
Evidence for T-cell clonal expansion in a patient with squamous cell carcinoma of the head and neck.

Squamous cell carcinomas of the head and neck (SCCHN) often contain a large mononuclear cell infiltrate, composed mainly of T-lymphocytes which could reflect an in situ immune reaction against the malignant SCCHN cells. We analyzed the molecular structure of the T-cell receptor (TCR) alpha and beta chains expressed by lymphocytes in the tumor, peripheral blood (PBMC), and in the peritumoral tissue in six cases of localized SCCHN. We first determined V alpha and V beta gene segment subfamily usage by polymerase chain reaction using a panel of V subfamily-specific oligonucleotide primers (V alpha 1-w29 and V beta 1-w24). An apparently unrestricted usage of V alpha or V beta gene segment subfamilies was observed for all three samples from the six cases examined. No major difference was observed in T-cell receptor repertoire expression of PBMC between SCCHN and healthy donors, making unlikely the expression of putative tumor-related superantigen(s) in these patients. Intersample comparisons for a given V alpha or V beta T-cell receptor specificity revealed some differences in V gene segment usage in tumor-infiltrating lymphocytes versus PBMC. A detailed analysis of these V segment subfamily specificities (cloning followed by sequencing and CDR3 size distribution analysis) in one patient revealed the presence of recurrent T-cell receptor transcripts (i.e., identical V-N-J sequences) in the tumor (e.g., 40%) and in PBMC (e.g., 75%). These results show that unique T-cell subpopulations are clonally amplified in SCCHN patients, possibly as a consequence of antigen-driven selection.
Simulation of Climate Change Scenarios for Phu Luong Watershed in Northern Viet Nam

The purpose of this paper is to apply "LARS-WG (Long Ashton Research Station—Weather Generator)" model to simulate the climate change scenarios for Phu Luong watershed in northern Viet Nam. Results indicated that LARS-WG adequately predicted precipitation and temperature with R 2 = 0.80 and 0.73, respectively. Likewise, p-value of F test = 0.062 and p-value of t test = 0.885 for precipitation, meanwhile, for temperature are 0.092 and 0.564 at 0.05 level of significance, respectively. Moreover, results also stated that mean annual precipitation increases 1.62%, 2.17% and 3.96% and mean annual temperature increases 0.6 o C, 0.8 o C and 1.05 o C in 2020, 2030 and 2040, respectively, with respect to those from baseline periods. This study also showed that LARS-WG model was used successfully for Viet Nam's watershed conditions.
MBIR Training for a 2.5D DL network in X-ray CT

In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection technique. The cost we have to pay is that MBIR is computationally expensive. In this work we train a 2.5D deep learning (DL) network to mimic MBIR quality image. The network is realized by a modified Unet, and trained using clinical FBP and MBIR image pairs. We achieve the quality of MBIR images faster and with a much smaller computation cost. Visually and in terms of noise power spectrum (NPS), DL-MBIR images have texture similar to that of MBIR, with reduced noise power. Image profile plots, NPS plots, standard deviation, etc. suggest that the DL-MBIR images result from a successful emulation of an MBIR operator.
Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses

The shift towards electrification and autonomous driving in the automotive industry results in more and more automotive wire harnesses being installed in modern automobiles, which stresses the great significance of guaranteeing the quality of automotive wire harness assembly. The mating of connectors is essential in the final assembly of automotive wire harnesses due to the importance of connectors on wire harness connection and signal transmission. However, the current manual operation of mating connectors leads to severe problems regarding assembly quality and ergonomics, where the robotized assembly has been considered, and different vision-based solutions have been proposed to facilitate a better perception of the robot control system on connectors. Nonetheless, there has been a lack of deep learning-based solutions for detecting automotive wire harness connectors in previous literature. This paper presents a deep learning-based connector detection for robotized automotive wire harness assembly. A dataset of twenty automotive wire harness connectors was created to train and evaluate a two-stage and a one-stage object detection model, respectively. The experiment results indicate the effectiveness of deep learning-based connector detection for automotive wire harness assembly but are limited by the design of the exteriors of connectors.