Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Linear Trend Recovery Using L1 Regularization

This paper studies the recovery of a joint piece-wise linear trend from a time series using `1 regularization approach, called `1 trend filtering (Kim, Koh and Boyd, 2009). We provide some sufficient conditions under which a `1 trend filter can be well-behaved in terms of mean estimation and change point detection. The result is two-fold: for the mean estimation, an almost optimal consistent rate is obtained; for the change point detection, the slope change in direction can be recovered in a high probability. In addition, we show that the weak irrepresentable condition, a necessary condition for LASSO model to be sign consistent (Zhao and Yu, 2006), is not necessary for the consistent change point detection. The performance of the `1 trend filter is evaluated by some finite sample simulations studies.
Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras

Vehicle Re-identification aims to identify a specific vehicle across time and camera view. With the rapid growth of intelligent transportation systems and smart cities, vehicle Re-identification technology gets more and more attention. However, due to the difference of shooting angle and the high similarity of vehicles belonging to the same brand, vehicle re-identification becomes a great challenge for existing method. In this paper, we propose a vehicle attribute-guided method to re-rank vehicle Re-ID result. The attributes used include vehicle orientation and vehicle brand . We also focus on the camera information and introduce camera mutual exclusion theory to further fine-tune the search results. In terms of feature extraction, we combine the data augmentations of multi-resolutions with the large model ensemble to get a more robust vehicle features. Our method achieves mAP of 63.73% and rank-1 accuracy 76.61% in the CVPR 2021 AI City Challenge.
Convergence Analysis of a Local Stationarity Scheme for Rate-Independent Systems and Application to Damage

This paper is concerned with an approximation scheme for rate-independent systems governed by a non-smooth dissipation and a possibly non-convex energy functional. The scheme is based on the local minimization scheme introduced in \cite{efenmielke06}, but relies on local stationarity of the underlying minimization problem. Under the assumption of Mosco-convergence for the dissipation functional, we show that accumulation points exist and are so-called parametrized \added[id=R]{BV-}solutions of the rate-independent system. In particular, this guarantees the existence of parametrized \added[id=R]{BV-}solutions for a rather general setting. Afterwards, we apply the scheme to a model for the evolution of damage.
Methodology for Biasing Random Simulation for Rapid Coverage of Corner Cases in AMS Designs

Exploring the limits of an Analog and Mixed Signal (AMS) circuit by driving appropriate inputs has been a serious challenge to the industry. Doing an exhaustive search of the entire input state space is a time-consuming exercise and the returns to efforts ratio is quite low. In order to meet time-to-market requirements, often suboptimal coverage results of an integrated circuit (IC) are leveraged. Additionally, no standards have been defined which can be used to identify a target in the continuous state space of analog domain such that the searching algorithm can be guided with some heuristics. In this report, we elaborate on two approaches for tackling this challenge - one is based on frequency domain analysis of the circuit, while the other applies the concept of Bayesian optimization. We have also presented our results by applying the two approaches on an industrial LDO and a few AMS benchmark circuits.
Evaluation of Lagergren Kinetics Equation by Using Novel Kinetics Expression of Sorption of Zn 2+ onto Horse Dung Humic Acid (HD-HA)

Extraction and purification of humic acid from dry horse dung powder (HD-HA) was performed successfully and the purified HD-HA was then applied as sorbent to adsorb Zn 2+ . Extraction and purification were performed based on procedure of Stevenson (1994) under atmospheric air. Parameters investigated in this work consist of effect of medium sorption acidity, sorption rate (k a ) and desorption rate constant (k d ), Langmuir (monolayer) and Freundlich (multilayer) sorption capacities, and energy (E) of sorption. The k a and k d were determined according to the kinetic model of second order sorption reaching equilibrium, monolayer sorption capacity (b) and energy (E) were determined according to Langmuir isotherm model, and multilayer sorption capacity (B) was determined based on Freundlich isotherm model. Sorption of Zn 2+ on purified HD-HA was maximum at pH 5.0. The novel kinetic expression resulted from proposed kinetic model has been shown to be more applicable than the commonly known Lagergren equation obtained from the pseudo-first order sorption model. The application of the equation revealed that the intercept of Lagergren equation, ln q e was more complex function of initial concentration of Zn 2+ (a), Langmuir sorption capacity (b), and sorbed Zn 2+ at equilibrium (x e ).
Predicting Intraoperative Hypoxemia with Joint Sequence Autoencoder Networks

We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Our proposed model makes inference on both hypoxemia outcomes and future input sequences, enabled by a joint sequence autoen-coder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns future-indicative latent representation. All decoders share a memory-based encoder that helps capture the global dynamics of patient data. In a large surgical cohort of 73,536 surgeries at a major academic medical center, our model outperforms all baselines and gives a large performance gain over the state-of-the-art hypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents 99.36% precision in predicting hypoxemia and 86.81% precision in predicting the much more severe and rare hypoxemic condition, persistent hypoxemia. With exceptionally low rate of false alarms, our proposed model is promising in improving clinical decision making and easing burden on the health system.
CoSformer: Detecting Co-Salient Object with Transformers

Co-Salient Object Detection (CoSOD) aims at simulating the human visual system to discover the common and salient objects from a group of relevant images. Recent methods typically develop sophisticated deep learning based models have greatly improved the performance of CoSOD task. But there are still two major drawbacks that need to be further addressed, 1) sub-optimal inter-image relationship modeling; 2) lacking consideration of inter-image separability. In this paper, we propose the Co-Salient Object Detection Transformer (CoSformer) network to capture both salient and common visual patterns from multiple images. By leveraging Transformer architecture, the proposed method address the inﬂuence of the input orders and greatly improve the stability of the CoSOD task. We also introduce a novel concept of inter-image separability. We construct a contrast learning scheme to modeling the inter-image separability and learn more discriminative embedding space to distinguish true common objects from noisy objects. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our CoSformer outperforms cutting-edge models and achieves the new state-of-the-art. We hope that CoSformer can motivate future research for more visual co-analysis tasks. experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our CoSformer outperforms cutting-edge models and achieves the new state-of-the-art.
Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, Visual-inertial odometry, and LiDAR-inertial odometry. The Visual-inertial odometry and LiDAR-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10% of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer’s effort to the DARPA Subterranean Challenge where the team won 1st and 2nd place in the Tunnel and Urban Circuits 1, respectively.
Multi-Structural Games and Number of Quantifiers

We study multi-structural games, played on two sets ${\mathcal{A}}$ and ${\mathcal{B}}$ of structures. These games generalize Ehrenfeucht-Fraïssé games. Whereas Ehrenfeucht-Fraïssé games capture the quantifier rank of a first-order sentence, multi-structural games capture the number of quantifiers, in the sense that Spoiler wins the r-round game if and only if there is a first-order sentence ϕ with at most r quantifiers, where every structure in ${\mathcal{A}}$ satisfies ϕ and no structure in ${\mathcal{B}}$ satisfies ϕ. We use these games to give a complete characterization of the number of quantifiers required to distinguish linear orders of different sizes, and develop machinery for analyzing structures beyond linear orders.
Generative models improve radiomics reproducibility in low dose CTs: a simulation study

Radiomics is an active area of research in medical image analysis, however poor reproducibility of radiomics has hampered its application in clinical practice. This issue is especially prominent when radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the reproducibility of radiomic features calculated on noisy CTs by using generative models for denoising. Our work concerns two types of generative models—encoder–decoder network (EDN) and conditional generative adversarial network (CGAN). We then compared their performance against a more traditional ‘non-local means’ denoising algorithm. We added noise to sinograms of full dose CTs to mimic low dose CTs with two levels of noise: low-noise CT and high-noise CT. Models were trained on high-noise CTs and used to denoise low-noise CTs without re-training. We tested the performance of our model in real data, using a dataset of same-day repeated low dose CTs in order to assess the reproducibility of radiomic features in denoised images. EDN and the CGAN achieved similar improvements on the concordance correlation coefficients (CCC) of radiomic features for low-noise images from 0.87 [95%CI, (0.833, 0.901)] to 0.92 [95%CI, (0.909, 0.935)] and for high-noise images from 0.68 [95%CI, (0.617, 0.745)] to 0.92 [95%CI, (0.909, 0.936)], respectively. The EDN and the CGAN improved the test-retest reliability of radiomic features (mean CCC increased from 0.89 [95%CI, (0.881, 0.914)] to 0.94 [95%CI, (0.927, 0.951)]) based on real low dose CTs. These results show that denoising using EDN and CGANs could be used to improve the reproducibility of radiomic features calculated from noisy CTs. Moreover, images at different noise levels can be denoised to improve the reproducibility using the above models without need for re-training, provided the noise intensity is not excessively greater that of the high-noise CTs. To the authors’ knowledge, this is the first effort to improve the reproducibility of radiomic features calculated on low dose CT scans by applying generative models.
Simultaneous estimation of segmented telescope phasing errors and non-common path aberrations from adaptive-optics-corrected images

We investigate the focal plane wavefront sensing technique, known as Phase Diversity, at the scientific focal plane of a segmented mirror telescope with an adaptive optics (AO) system. We specifically consider an optical system imaging a point source in the context of (i) an artificial source within the telescope structure and (ii) from AO-corrected images of a bright star. From our simulations, we reliably disentangle segmented telescope phasing errors from non-common path aberrations (NCPA) for both a theoretical source and on-sky, AOcorrected images where we have simulated the Keck/NIRC2 system. This quantification from on-sky images is appealing, as it’s sensitive to the cumulative wavefront perturbations of the entire optical train; disentanglement of phasing errors and NCPA is therefore critical, where any potential correction to the primary mirror from an estimate must contain minimal NCPA contributions. Our estimates require a one-minute sequence of short-exposure, AO-corrected images; by exploiting a slight modification to the AO-loop, we find that 75 defocused images produces reliable estimates. We demonstrate a correction from our estimates to the primary and deformable mirror results in a wavefront error reduction of up to 67% and 65% for phasing errors and NCPA, respectively. If the segment phasing errors on the Keck primary are on the order of ∼130 nm RMS, we show we can improve the H-band Strehl ratio by up to 10% by using our algorithm. We conclude our technique works well to estimate NCPA alone from on-sky images, suggesting it is a promising method for any AO-system.
Towards Flying through Modular Forms

Modular forms are highly self-symmetric functions studied in number theory, with connections to several areas of mathematics. But they are rarely visualized. We discuss ongoing work to compute and visualize modular forms as 3D surfaces and to use these techniques to make videos flying around the peaks and canyons of these"modular terrains. "Our goal is to make beautiful visualizations exposing the symmetries of these functions.
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for Accurate GAN Evaluation

—Evaluating image generation models such as generative adversarial networks (GANs) is a challenging problem. A common approach is to compare the distributions of the set of ground truth images and the set of generated test images. The Frech´et Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution. In this paper, we argue that this is an over-simpliﬁed assumption, which may lead to unreliable evaluation results, and more accurate density estimation can be achieved using a truncated generalized normal distribution. Based on this, we propose a novel metric for accurate evaluation of GANs, named TREND (TRuncated gEneralized Normal Density estimation of inception embeddings). We demonstrate that our approach signiﬁcantly reduces errors of density estimation, which consequently eliminates the risk of faulty evaluation results. Furthermore, we show that the proposed metric signiﬁcantly improves robustness of evaluation results against variation of the number of image samples.
Bi-seasonal discrete time risk model with income rate two

Abstract This article proceeds calculation of ultimate time survival probability for bi-seasonal discrete time risk model when premium rate equals two. The same model with income rate equal to one was investigated in 2014 by Damarackas and Šiaulys. In general, discrete time and related risk models deal with possibility for a certain version of random walk to hit a certain threshold at least once in time. In this research, the mentioned threshold is the line and random walk consists from two interchangeably occurring independent but not necessarily identically distributed random variables. Most of proved theoretical statements are illustrated via numerical calculations. Also, there are raised a couple of conjectures that a certain recurrent determinants are non-vanishing.
Diversity of Eusocial Bees in Natural and Anthropized Areas of a Tropical Dry Forest in the Parque da Sapucaia (Montes Claros, Minas Gerais, Brazil)

The modification of natural habitats is considered the main cause of the current loss of bee diversity (GonzálezVaro et al., 2013; Collado et al., 2019). On a global scale, native bees are threatened mainly due to human disturbances caused by deforestation, intensification of agriculture and introduction of exotic species (review in Winfree et al., 2009). Although some species of bees are able to live and find resources in anthropized areas, such as urban fragments (Martins et al., 2013), environmental anthropization tends to have a deleterious effect on the community of these insects (Zanette et al., 2005). Because of this, a growing number of studies have investigated how anthropic factors influence the Abstract In the present study we inventoried the diversity of eusocial bees (Hymenoptera: Apidae) in preserved and anthropized areas of a tropical dry forest in the Parque da Sapucaia (Montes Claros, Minas Gerais, Brazil). We tested the hypothesis that the diversity of bee species would: 1) be greater in the preserved areas, 2) respond positively to the structure of the vegetation and 3) decrease during the dry season. We sampled eusocial bee species in 18 plots of 10 x 10 m distributed throughout the park, being nine plots in anthropized areas and nine plots in areas with preserved vegetation. In total we recorded 382 individuals and eight species of eusocial bees. The most abundant species was Oxytrigona tataira (Smith) (N = 233) and the most common species was Trigona spinipes (Fabricius) recorded in 72.2% of the plots. As expected, we found that eusocial bee diversity (Shannon diversity) was higher in preserved plots than in anthropized plots. Tree species richness positively affected bee species richness and abundance, while tree abundance positively influenced the bee abundance and tree height positively affected the bee Shannon diversity, corroborating our expectations. On the other hand, we detected no differences in the diversity of eusocial bees between dry and rainy seasons. Our findings suggest that both natural (vegetation structure) and anthropogenic (habitat modification) factors are important predictors of the diversity of eusocial bee species in tropical dry forests. Sociobiology An international journal on social insects
MP35-20 A NEW URINARY CATHETER DESIGN REDUCES IN VITRO BIOFILM FORMATION BY INFLUENCING HYDRODYNAMICS

AIM To evaluate the performance of a new catheter design based on different hydrodynamics aiming to reduce the development of biofilm, and compare it with a conventional Foley catheter (FC). METHODS The new proposed design (NPD) catheter is a modification of the FC, based on asymmetric positioning of the balloon and additional drainage holes allowing continuous urine drainage and complete voiding of the bladder. A first experiment was undertaken to assess drainage capability, and a second experiment was performed using a bioreactor with a set-up simulating the bladder and using the test catheter as a flow-through system. The biofilm formation of five bacterial species associated with catheter-associated urinary tract infection (CAUTI) was determined after 24 h of incubation using an MTT assay. Morphological evaluation was performed using scanning electron microscopy. In-vitro determination of residual fluid, and quantitative and morphological data on biofilm formation on the intravesical and intraluminal parts of the tested catheters were assessed. RESULTS Residual fluid was significantly higher in the FC (5.60 ± 0.43 mL) compared with the NPD catheter (0.2 ± 0.03 mL). The NPD catheter showed significantly less biofilm formation (P<0.0001) than the FC. Catheter design had a variable effect on biofilm formation depending on the bacterial strain tested. There was significantly less intraluminal biomass compared with intravesical biomass in both catheters (P<0.0001). Multi-layered biofilms that covered the FC surfaces completely were seen for all tested strains, while the NPD catheter surfaces showed reduced biofilm formation. CONCLUSIONS Modifications of the hydrodynamic characteristics of a catheter can significantly reduce bacterial colonization. Integrated design approaches combining chemical, mechanical and topographical elements can help to reduce the occurrence of CAUTI.
supplement: compendium of origins and proteins

Abbreviations: bp base pair(s); cpφ cryptic prophage, in most cases defect, i.e. non-inducible; cSDR constitutive stable DNA replication; D-loop DNA-based displacement loop; DSB double-strand break; dsDNA double-stranded DNA; EMSA electrophoretic mobility shift assay; F4 family 4 (of helicases), e.g. DnaBEco; HGT horizontal gene transfer; hmUra 5-(hydroxymethyl)-2'-deoxyuridine; HTH helix-turn-helix DNA-binding motif; iSDR inducible stable DNA replication; NMR nuclear magnetic resonance; NTP nucleoside 5'-triphosphate; orf open reading frame; PCNA proliferating cell nuclear antigen (sliding clamp); φ (phi) prefix to mark bacteriophage (replicon); p prefix to mark plasmid (replicon); pφ prophage; ppDR protein-primed DNA replication; RCR 'rolling circle' DNA replication; RDR recombination-dependent DNA replication; RF replicative form (of ssDNA phages); R-loop displacement loop, based on a RNA⋅DNA hybrid; RFC replication factor C (clamp loader); SDR stable DNA replication;σDR sigma(σ)-type DNA replication; SF2 superfamily 2 (of helicases), e.g. φT4 UvsW; SSB single-strand (DNA) binding protein; SAP single-strand (DNA) annealing protein; ssDNA singlestranded DNA; TBD thioredoxin binding domain; tDR transcript-driven initiation of DNA replication; (ts) temperature-sensitive mutation;θDR theta(θ)-type DNA replication; TP terminal protein; UDG uracil-DNA glycosylase.
Phytohormone-dependent plant defense signaling orchestrated by oral bacteria of the herbivore Spodoptera litura.

A vast array of herbivorous arthropods live with symbiotic microorganisms. However, little is known about the nature and functional mechanism of bacterial effects on plant defense responses towards herbivores. We explored the role of microbes present in extracts of oral secretion (OS) isolated from larvae of Spodoptera litura, a generalist herbivore, in phytohormone signaling-dependent defense responses in Arabidopsis thaliana (arabidopsis). In response to mechanical damage (MD) with application of bacteria-free OS (OS- ) prepared by sterilization or filtration of OS, arabidopsis leaves exhibited enhanced de novo synthesis of oxylipins, and induction of transcript levels of the responsible genes, in comparison to those in leaves with MD + non-sterilized OS (OS+ ), indicating that OS bacteria serve as suppressors of these genes. By contrast, de novo synthesis/signaling of salicylic acid and signaling of abscisic acid were enhanced by OS bacteria. These signaling networks were cross-regulated by each other. Meta-analysis of OS bacteria identified 70 bacterial strains. Among them was Staphylococcus epidermidis, an anaerobic staphylococcus that was shown to contribute to the suppression/manipulation of phytohormone-dependent plant defense signaling. The presence of OS bacteria was consequently beneficial for S. litura larvae hosted by Brassicaceae.