Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis

Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r. t. real-life data, it is crucial to verify the transferability of the obtained results.We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporating controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between real-life and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians.
Linear response in large deviations theory: a method to compute non-equilibrium distributions

We consider thermodynamically consistent autonomous Markov jump processes displaying a macroscopic limit in which the logarithm of the probability distribution is proportional to a scale-independent rate function (i.e. a large deviations principle is satisfied). In order to provide an explicit expression for the probability distribution valid away from equilibrium, we propose a linear response theory performed at the level of the rate function. We show that the first order non-equilibrium contribution to the steady state rate function, g( x ), satisfies u(x)⋅∇g(x)=−βW˙(x) where the vector field u ( x ) defines the macroscopic deterministic dynamics, and the scalar field W˙(x) equals the rate at which work is performed on the system in a given state x . This equation provides a practical way to determine g( x ), significantly outperforms standard linear response theory applied at the level of the probability distribution, and approximates the rate function surprisingly well in some far-from-equilibrium conditions. The method applies to a wealth of physical and chemical systems, that we exemplify by two analytically tractable models—an electrical circuit and an autocatalytic chemical reaction network—both undergoing a non-equilibrium transition from a monostable phase to a bistable phase. Our approach can be easily generalized to transient probabilities and non-autonomous dynamics. Moreover, its recursive application generates a virtual flow in the probability space which allows to determine the steady state rate function arbitrarily far from equilibrium.
Rotating Motion of the Outflow of IRAS 16293-2422 A1 at Its Origin Point Near the Protostar

The Class 0 protostar IRAS 16293-2422 Source A is known to be a binary system (A1 and A2) or even a multiple system that processes a complex outflow structure. We have observed this source in the C34S, SO, and OCS lines at 3.1 mm with the Atacama Large Millimeter/submillimeter Array. A substructure of this source is traced by our high angular-resolution observation (0.″12; 20 au) of the continuum emission. The northwest–southeast (NW–SE) outflow on a 2″ scale is detected in the SO (J N = 22–11) line. Based on the morphology of the SO distribution, this bipolar outflow structure seems to originate from the protostar A1 and its circumstellar disk, or the circummultiple structure of Source A. The rotation motion of the NW–SE outflow is detected in the SO and OCS emissions. We evaluate the specific angular momentum of the outflowing gas to be (8.6–14.3) × 10−4 km s−1 pc. If the driving source of this outflow is the protostar A1 and its circumstellar disk, it can be a potential mechanism to extract the specific angular momentum of the disk structure. These results can be a hint for the outflow launching mechanism in this source. Furthermore, they provide us with an important clue to resolve the complicated structure of IRAS 16293-2422 Source A.
The impact of ionized outflows from z ∼ 2.5 quasars is not through instantaneous in situ quenching: the evidence from ALMA and VLT/SINFONI

We present high-resolution (∼2.4 kpc) ALMA band 7 observations (rest-frame λ ∼ 250 $\mu$m) of three powerful z ∼ 2.5 quasars (Lbol = 1047.3–1047.5 erg s−1). These targets have previously been reported as showing evidence for suppressed star formation based on cavities in the narrow H α emission at the location of outflows traced with [O iii] emission. Here, we combine the ALMA observations with a re-analysis of the VLT/SINFONI data to map the rest-frame far-infrared emission, H α emission, and [O iii] emission. In all targets, we observe high velocity [O iii] gas (i.e. W80 ∼ 1000–2000 km s−1) across the whole galaxy. We do not identify any H α emission that is free from contamination from AGN-related processes; however, based on SED analyses, we show that the ALMA data contain a significant dust-obscured star formation component in two out of the three systems. This dust emission is found to be extended over ≈1.5–5.5 kpc in the nuclear regions, overlaps with the previously reported H α cavities and is co-spatial with the peak in surface brightness of the [O iii] outflows. In summary, within the resolution and sensitivity limits of the data, we do not see any evidence for a instantaneous shut down of in situ star formation caused directly by the outflows. However, similar to the conclusions of previous studies and based on our measured star formation rates, we do not rule out that the global host galaxy star formation could be suppressed on longer time-scales by the cumulative effect of quasar episodes during the growth of these massive black holes.
Continuous Time Frank-Wolfe Does Not Zig-Zag, But Multistep Methods Do Not Accelerate

The Frank-Wolfe algorithm has regained much interest in its use in structurally constrained machine learning applications. However, one major limitation of the Frank-Wolfe algorithm is the slow local convergence property due to the zig-zagging behavior. We observe that this zigzagging phenomenon can be viewed as an artifact of discretization, as when the method is viewed as an Euler discretization of a continuous time flow, that flow does not zig-zag. For this reason, we propose multistep Frank-Wolfe variants based on discretizations of the same flow whose truncation errors decay as O(∆), where p is the method’s order. This strategy “stabilizes” the method, and allows tools like line search and momentum to have more benefit. However, in terms of a convergence rate, our result is ultimately negative, suggesting that no Runge-Kutta-type discretization scheme can achieve a better convergence rate than the vanilla Frank-Wolfe method. We believe that this analysis adds to the growing knowledge of flow analysis for optimization methods, and is a cautionary tale on the ultimate usefulness of multistep methods.
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset

Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking investigations since they can help track down current and future victims who might be taken to the same places. Hotel recognition is a challenging fine grained visual classification task as there can be little similarity between different rooms within the same hotel, and high similarity between rooms from different hotels (especially if they are from the same chain). Hotel recognition to combat human trafficking poses additional challenges as investigative images are often low quality, contain uncommon camera angles and are highly occluded. Here, we present the 2021 Hotel-ID dataset to help raise awareness for this problem and generate novel approaches. The dataset consists of hotel room images that have been crowd-sourced and uploaded through the TraffickCam mobile application. The quality of these images is similar to investigative images and hence models trained on these images have good chances of accurately narrowing down on the correct hotel.
Singularity of the k-core of a random graph

Very sparse random graphs are known to typically be singular (i.e., have singular adjacency matrix), due to the presence of"low-degree dependencies'' such as isolated vertices and pairs of degree-1 vertices with the same neighbourhood. We prove that these kinds of dependencies are in some sense the only causes of singularity: for constants $k\ge 3$ and $\lambda>0$, an Erd\H{o}s--R\'enyi random graph $G\sim\mathbb{G}(n,\lambda/n)$ with $n$ vertices and edge probability $\lambda/n$ typically has the property that its $k$-core (its largest subgraph with minimum degree at least $k$) is nonsingular. This resolves a conjecture of Vu from the 2014 International Congress of Mathematicians, and adds to a short list of known nonsingularity theorems for"extremely sparse'' random matrices with density $O(1/n)$. A key aspect of our proof is a technique to extract high-degree vertices and use them to"boost'' the rank, starting from approximate rank bounds obtainable from (non-quantitative) spectral convergence machinery due to Bordenave, Lelarge and Salez.
Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance

This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments. This is achieved by numerical optimization with constraint learning for instantaneous local control barrier functions (IL-CBFs) and goal-driven control Lyapunov functions (GD-CLFs). In particular, the constraints reflecting safety and task requirements are first online learned from perceptual signals, wherein IL-CBFs are learned to characterize potential collisions, and GD-CLFs are constructed to reflect incrementally discovered subgoals. Then, the learned IL-CBFs are united with GD-CLFs in the context of a quadratic programming optimization, whose feasibility is improved by enlarging the shared control space. Numerical simulations are conducted to reveal the effectiveness of our proposed safe feedback control strategy that could drive the mobile robot to safely reach the destination incrementally in an uncertain environment.
Preparation and photocatalytic activity of TiO_2 thin films

TiO2 thin films were prepared by sol-gel method.The influences of calcining temperature and coating layer and the UV illumination on the photocatalytic activities of the TiO2 thin films were investigated using 10mg/L methyl orange as target degradation substance.The experimental results showed that the TiO2 thin films had the highest photocatalytic activity when calcining temperature was 500℃ and three layers.The photocatalytic activities of the TiO2 thin films were steady and lasting.
The galaxy size–halo mass scaling relations and clustering properties of central and satellite galaxies

In this work, we combine size and stellar mass measurements from the Sloan Digital Sky Server (SDSS) with the group finder algorithm of Rodriguez &Merchán in order to determine the stellar and halo mass – size relations of central and satellite galaxies separately. We show that, while central and satellite galaxies display similar stellar mass – size relations, their halo mass – size relations differ significantly. As expected, more massive haloes tend to host larger central galaxies. However, the size of satellite galaxies depends only slightly on halo virial mass. We show that these results are compatible with a remarkably simple model in which the size of central and satellite galaxies scales as the cubic root of their host halo mass, with the normalization for satellites being ∼ 30 % smaller than that for central galaxies, which can be attributed to tidal stripping. We further check that our measurements are in excellent agreement with predictions from the IllustrisTNG hydrodynamical simulation. In the second part of this paper, we analyse how the clustering properties of central and satellite galaxies depend on their size.We demonstrate that, independently of the stellar mass threshold adopted, smaller galaxies are more tightly clustered than larger galaxies when either the entire sample or only satellites are considered. The opposite trend is observed on large scales when the size split is performed for the central galaxies alone. Our results place significant constraints for halo–galaxy connection models that link galaxy size with the properties of their hosting haloes.
The Microwave Spectrum of 2,6-Lutidine, Analysis of Internal Rotation and 14 N Hyperfine Structure

Abstract The rotational spectrum of 2,6-lutidine, (CH3)2C5H3N, has been recorded between 6 and 26.5 GHz using pulsed molecular beam microwave Fourier transform spectroscopy. The rotational constants are A = 3509.7139(84) MHz, B = 1906.8639(101) MHz, and C = 1254.6215(14) MHz, the barrier to internal rotation of the two methyl groups is V3 = 1.1752 kJ/mol, their moments of inertia were found to be Iα = 3.0808(9) uÅ2 . The nitrogen nuclear quadrupole constants are χaa = +1.600(5) MHz, χbb = -4.572(3) MHz and χcc = +2.972(5) MHz.
Evaluation of the Mineral Contents in Fish Meal by FT NIR using PLS and Kernel PLS

In the present work we study the use of Fourier transform near infrared spectroscopy (FTNIRS) technique to analysis the calcium (Ca), phosphorus (P) and copper (Cu) contents of fish meal. The regression methods employed were partial least squares (PLS) and kernel partial least squares (KPLS). The results showed that the efficiency of KPLS was better than PLS. As a whole, the application of FT-NIRS with PLS and KPLS was as a suitable option for replacing the routine chemical analysis to assess the mineral content in fish meal, allowing immediate control of the fish meal without prior sample treatment or destruction.
Multi-Organ Transcriptome Dynamics in a Mouse Model of Cecal Ligation and Puncture-Induced Polymicrobial Sepsis

Purpose During sepsis, an excessive inflammatory immune reaction contributes to multi-organ dysfunction syndrome (MODS), a critical condition associated with high morbidity and mortality; however, the molecular mechanisms driving MODS remain elusive. Methods We used RNA sequencing to characterize transcriptional changes in the early phase of sepsis, at 6, 12, 24 hour time points in lung, kidney, liver, and heart tissues, in a cecal ligation and puncture (CLP)-induced polymicrobial sepsis murine model. Results The CLP surgery induced significant changes (adj. p-value<0.05) in expression of hundreds of transcripts in the four organs tested, with the highest number exceeding 2,000 differentially expressed genes (DEGs) in all organs at 12 hours post-CLP. Over-representation analysis by functional annotations of DEGs to the Reactome database revealed the immune system, hemostasis, lipid metabolism, signal transduction, and extracellular matrix remodeling biological processes as significantly altered in at least two organs, while metabolism of proteins and RNA were revelaed as being liver tissue specific in the early phase of sepsis. Conclusion RNA sequencing across organs and time-points in the CLP murine model allowed us to study the trajectories of transcriptome changes demonstrating alterations common across multiple organs as well as biological pathways altered in an organ-specific manner. These findings could pave new directions in the research of sepsis-induced MODS and indicate new sepsis treatment strategies.
Physical Inactivity, Non-Communicable Diseases and National Fitness Plan of China for Physical Activity

Background China has the world’s largest population, going under health transition due to industrialization, urbanization, and a sedentary lifestyle. About 82% of China’s disease burden is due to the prevalence of non-communicable diseases (NCDs). Physical activity (active travel) is the best preventive measure against NCDs. The Chinese government has introduced several steps to improve national fitness and overcome NCDs among the aging population. Exercise and sports play a vital role in promoting physical activity and helpful in accomplishing the national fitness level for Healthy China under the national fitness plan (NFP). Objective The current study aimed to explore the role of national fitness plan (NFP) in promoting physical activity and health well-being preventing NCDs under built environment intervention. Methods The study was exploratory, and the mixed-method approach was used to analyze the primary and secondary data. Quantitative content analysis was used to analyze the collected data. A purposive sampling technique was used to collect the primary data from those individuals rich in knowledge about the National Fitness Plan (NFP) of China and its role in promoting physical activity for physical fitness. Results The majority of the participants (32.0%) said that the national fitness plan helps promote sports environment and health. About 28.0% reported that NFP helps promote cycling and walk environments for physical activity and health promotion and preventing non-communicable diseases (NCDs). Conclusion Participation in physical exercise and sports is the preferred approach to prevent different diseases. In the context of rising active living among the Chinese people, the facilities such as built environment and green parks under the national fitness plan (NFP) for sports play a crucial role in the mitigation of NCDs.
miR-340 Promotes Retinoblastoma Cell Proliferation, Migration and Invasion Through Targeting WIF1

Background MicroRNAs (miRNAs) function as important regulators of gene expression involved in tumor pathogenesis, including retinoblastoma. However, the expression profiles and potential roles in retinoblastoma are still largely unclear. Material and Methods Differentially expressed miRNAs (DEmiRs) and genes (DEGs) in retinoblastoma were extracted from Gene Expression Omnibus (GEO) repository. Expression levels of miR-340 and WIF1 were detected in retinoblastoma tissues and cell lines by qRT-PCR. Both gain-of-function and loss-of-function experiments were performed to explore the effects of miR-340 on cell proliferation, migration and invasion. Bioinformatics analysis and luciferase reporter assay were used to explore the interaction between miR-340 and WIF1. Results A total of 11 DEmiRs were identified in retinoblastoma tissue and blood samples. Among them, we validated that miR-340 was the most highly expressed miRNA and correlated with tumor size, ICRB stage and optic nerve invasion. miR-340 was observed to enhance the proliferation, migration and invasion capacity of retinoblastoma cells. We then identified 26 DEGs from 3 retinoblastoma GEO datasets and subsequently constructed a miRNA–mRNA regulatory network. Further analysis revealed that WIF1 was a direct target of miR-340. Moreover, overexpression of WIF1 could repress retinoblastoma progression induced by miR-340 in vitro and in vivo. Conclusion Collectively, miR-340 functioned as an oncomiRNA to promote retinoblastoma cell proliferation, migration and invasion via regulating WIF1. Our data also provided multiple miRNAs and genes that may contribute to a better understanding of retinoblastoma pathogenesis.
Preparation and Characterization of Polyester Staple Yarns Nanowrapped with Polysulfone Amide Fibers

In this study, a novel approach and the related electrospinning equipment were developed to fabricate a new type of polyester staple yarn wrapped with poly(sulfone amide) (PSA) fibers at the nanoscale. As the number of tubes of spinnerets, electrospinning voltage, and rotation speed of the funnel collector have effects on the structure and properties of the nanowrapped yarns, three series of wrapped yarns were prepared using the self-developed electrospinning and wrapping equipment; these wrapped yarns at the nanoscale were characterized systematically by scanning electron microscopy (SEM), thermogravimetry (TG), and infiltration evaluation. The three-dimensional electric field of the electrospinning system used for the nanowrapped yarn was simulated to model the electric fields of different electrospinning spinnerets. Our experimental results showed that the mechanical, thermal, and hygroscopicity properties of the nanowrapped yarns fabricated in this work were clearly improved obviously in comparison wi.
A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database

Background To create an appropriate chronic kidney disease (CKD) management program, we developed a predictive model to identify patients in a large administrative claims database with CKD stages 3 or 4 who were at high risk for progression to kidney failure. Methods The predictive model was developed and validated utilizing a subset of patients with CKD stages 3 or 4 derived from a large Aetna claims database. The study spanned 36 months, comprised of a 12-month (2015) baseline period and a 24-month (2016–2017) prediction period. All patients were ≥18 years of age and continuously enrolled for 36 months. Multivariate logistic regression was used to develop models. Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts. Results Of the 74,114 patients identified as having CKD stages 3 or 4 during the baseline period, 2476 (3.3%) had incident kidney failure during the prediction period. The predictive model included the effect of numerous variables, including age, gender, CKD stage, hypertension (HTN), diabetes mellitus (DM), congestive heart failure, peripheral vascular disease, anemia, hyperkalemia (HK), prospective episode risk group score, and poor adherence to renin-angiotensin-aldosterone system inhibitors. The strongest predictors of progression to kidney failure were CKD stage (4 vs 3), HTN, DM, and HK. The ROC and calibration analyses in the validation sample demonstrated good predictive accuracy (AUROC=0.844) and calibration. The top two prediction deciles identified 70.8% of patients who progressed to kidney failure during the prediction period. Conclusion This novel predictive model had good accuracy for identifying, from a large national database, patients with CKD who were at high risk of progressing to kidney failure within 2 years. Early identification using this model could potentially lead to improved health outcomes and reduced healthcare expenditures in this at-risk population.