Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Differences in Cardiovascular Disease Risk Factors among Korean Adults

Background/Aims: The prevalence of cardiovascular disease among women is lower than that among men in middle age, but increases after middle age. This study was performed to compare the risk factors for cardiovascular disease according to sex in Koreans adults.Methods: Data from the National Health and Nutrition Examination Survey for the 5th, 6th, and 7th years (2010–2017) were analyzed. This study examined the risk factors for cardiovascular disease among adults. From the total of 41,386 patients, 11,529 patients were selected.Results: The overall prevalence of cardiovascular disease in Korean adults was 27.9%, and the prevalence was higher in women than in men (55.3% vs. 44.7%, respectively, p < 0.001). Women with cardiovascular disease were older than men (66.6 ± 9.9 years vs. 63.9 ± 11.1 years, respectively, p < 0.001). The prevalence of cardiovascular disease was higher in women with comorbid depression and rheumatoid arthritis and higher in men with diabetes mellitus. Multivariate regression analysis showed that stress (OR 2.31, 95% CI 1.28–4.17, p = 0.005) and smoking (OR 1.61, 95% CI 1.04–2.48, p = 0.031) were independent risk factors for cardiovascular disease in men, while depression (OR 2.03, 95% CI 1.22–3.39, p = 0.007), low education level, rheumatoid arthritis, and poor subjective health status (OR 1.70, 95% CI 1.20–2.41, p = 0.003) were independent risk factors for cardiovascular disease in women.Conclusions: Differences were detected in cardiovascular risk factors according to sex in Korea. The most important risk factors were smoking and stress in men, and depression, low education level, rheumatoid arthritis, and poor subjective health status in women.
Thermal Degradation Studies of Terpolymer Derived from 2-Aminothiophenol, Hexamethylenediamine, and Formaldehyde

Terpolymer (2-ATPHMDAF-I) has been synthesized by the condensation of 2-aminothiophenol and hexamethylenediamine with formaldehyde in the presence of 2 M hydrochloric acid as a catalyst with 1 : 1 : 2 molar proportion of reacting monomers. The structure of newly synthesized terpolymer has been elucidated and confirmed on the basis of elemental analysis and various spectral techniques, that is, UV-visible, FT-IR, and 1H-NMR spectroscopy. Number average molecular weight () has been determined by conductometric titration in nonaqueous medium. The viscosity measurements in dimethyl sulfoxide (DMSO) have been carried out to ascertain the characteristic functions and constants. The studies have been further extended to nonisothermal thermogravimetric analysis for determination of their mode of decomposition and relative thermal stability. Activation energy , order of reaction , and frequency factor were calculated by Friedman, Chang, Sharp-Wentworth and Freeman-Carroll methods. Activation energy calculated by Friedman and Chang methods are in close agreement with each other while the results obtained from Freeman-Carroll and Sharp-Wentworth’s methods are found to be in a similar order.
Relationship of environment with technological innovation, carbon pricing, renewable energy, and global food production

ABSTRACT The United Nation sustainable development goals are largely emphasized on advancement in cleaner production technologies to take action against climate change and maintain the average global temperature less than 1.5°C. The role of carbon pricing, renewable energy, and sustainable food production would be helpful to achieve sustainable development goals in line with technological innovations. This study works in given factors and evaluated the global pace of economic growth towards sustainable development. The ex-ante and ex-post analysis is carried out on the world aggregated data for a period of 1980–2017. The results show that knowledge spillover, combustible renewables & waste, and carbon pricing substantially decreases carbon emissions, whereas trademark applications have a direct relationship with carbon emissions that exhibit the innovation embodied emissions at a global scale. The results support the inverted U-shaped relationship between carbon emissions and global income with a turning point of US$15,800 and US$11,100 by using FMOLS and DOLS estimators respectively. The ex-ante analysis shows that knowledge spillover, trademark applications, and carbon pricing will largely decrease carbon emissions while carbon pricing, food production index, FDI inflows, and broad money supply will decrease fossil fuel emissions for the next 10 years time period.
Listener-Preferred Headphone FrequencyResponse for Stereo and Spatial Audio Content

on a and at The complete not r This conference paper the corrections, or consideration by the Board. The AES takes no for the contents. This paper in the AES E-Library (http://www.aes.org/e-lib), all reserved. Reproduction of this paper, or any portion thereof, is not permitted without direct permission from the Journal of the Engineering ABSTRACT When spatial audio content is presented over headphones, the audio signal is typically ﬁltered with binaural room impulse responses (BRIRs). An accurate virtual auditory space presentation can be achieved by ﬂattening the headphones’ frequency response. However, when presenting stereo music over headphones, previous studies have shown that listeners prefer headphones with a frequency response that simulates loudspeakers in a listening room. It is as yet unclear if headphones that are calibrated in such a way will be preferred by listeners in the context of spatial audio content as well. This study investigates how listeners’ preferences for headphone frequency response may differ between stereo audio content and spatial audio content, which was rendered by convolving the same stereo content with in-situ-measured BRIRs of loudspeakers in a room.
Comparison of postoperative complications of ileal conduits versus orthotopic neobladders

Background Radical cystectomy (RC) and urinary diversion (UD), with either an ileal conduit (IC) or an orthotopic neobladder (NB), is a complex surgery, in which various complications can occur. In this study, we compared postoperative complication rates after a RC and UD performed for the treatment of muscle-invasive bladder cancer or recurring high-risk non-muscle-invasive bladder cancer in our center. Methods We retrospectively included 604 patients that underwent UDs from December 1996 to August 2015. Complications were classified by type and severity according to the Clavien-Dindo classification (CDC). Univariate and multivariate analyses were performed to identify predictive factors of short-term (≤30 d), intermediate-term (31–90 d), and long-term (>90 d) complications. Results Four hundred and forty-five (74%) and 159 (26%) patients received ICs and NBs, respectively. These groups had significantly different long-term complication rates (IC: 39.7% vs. NB: 49%, P=0.046), but similar short-term (P=0.319) and intermediate-term complication rates (P=0.397). Short-term complications (CDC I–V) were predicted by male gender, age-adjusted Charlson comorbidity index (aCCI) ≥3, and American Society of Anesthesiologists (ASA) score ≥3. Compared to minor short-term complications (CDC I–II), major short-term complications (CDC III–V) were predicted by male gender and a previous abdominal/pelvic surgery, and long-term major complications were predicted by the type of UD (NB). Conclusions The increasing risk of short-term complications with increasing aCCI and ASA score can be used when counseling the patients who are planned to undergo a RC with UD. Patients that receive NBs should be informed of the increased risk of reoperations compared to an IC.
Physical Parameter and Loss Determination of Piezoceramics Using Partial Electrode: k31 and k33 Mode Cases

The standard method to determine physical parameters of piezoceramics, established by IEEE, has been utilized for decades by the number of researchers, yet it omits presence of important loss factors and possesses serious deficits that restricts accurate parameter determination. In order to resolve these issues, the partial electrode (PE) method (mechanical excitation method) was previously proposed. In this study, we aim to propose a modified PE method to enhance the efficiency of parameter determination process, along with a simplified analytical admittance equation for better understanding of the PE configuration. To prove that the PE method is reliable, possible causes of errors were listed, and it was shown that they were either negligibly small or resolved with proper calibration methods. Throughout the paper, it was validated that the PE method not only reduces the errors of several physical parameters by avoiding error propagation, but also enables measurement compatibility with commercially available impedance analyzers.
Assessing The Importance Of Colours For CNNs In Object Recognition

Humans rely heavily on shapes as a primary cue for object recognition. As secondary cues, colours and textures are also beneficial in this regard. Convolutional neural networks (CNNs), an imitation of biological neural networks, have been shown to exhibit conflicting properties. Some studies indicate that CNNs are biased towards textures whereas, another set of studies suggests shape bias for a classification task. However, they do not discuss the role of colours, implying its possible humble role in the task of object recognition. In this paper, we empirically investigate the importance of colours in object recognition for CNNs. We are able to demonstrate that CNNs often rely heavily on colour information while making a prediction. Our results show that the degree of dependency on colours tend to vary from one dataset to another. Moreover, networks tend to rely more on colours if trained from scratch. Pre-training can allow the model to be less colour dependent. To facilitate these findings, we follow the framework often deployed in understanding role of colours in object recognition for humans. We evaluate a model trained with congruent images (images in original colours eg. red strawberries) on congruent, greyscale, and incongruent images (images in unnatural colours eg. blue strawberries). We measure and analyse network's predictive performance (top-1 accuracy) under these different stylisations. We utilise standard datasets of supervised image classification and fine-grained image classification in our experiments.
Robustness Threats of Differential Privacy

Differential privacy is a powerful and gold-standard concept of measuring and guaranteeing privacy in data analysis. It is well-known that differential privacy reduces the model's accuracy. However, it is unclear how it affects security of the model from robustness point of view. In this paper, we empirically observe an interesting trade-off between the differential privacy and the security of neural networks. Standard neural networks are vulnerable to input perturbations, either adversarial attacks or common corruptions. We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions. To explore this, we extensively study different robustness measurements, including FGSM and PGD adversaries, distance to linear decision boundaries, curvature profile, and performance on a corrupted dataset. Finally, we study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect (decrease and increase) the robustness of the model.
Fine-Grained Lineage for Safer Notebook Interactions

Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program state in memory and segmenting units of execution into so-called "cells", notebooks allow users to enjoy particularly tight feedback. However, as cells are added, removed, reordered, and rerun, this hidden intermediate state accumulates, making execution behavior difficult to reason about, and leading to errors and lack of reproducibility. We present nbsafety, a custom Jupyter kernel that uses runtime tracing and static analysis to automatically manage lineage associated with cell execution and global notebook state. nbsafety detects and prevents errors that users make during unaided notebook interactions, all while preserving the flexibility of existing notebook semantics. We evaluate nbsafety's ability to prevent erroneous interactions by replaying and analyzing 666 real notebook sessions. Of these, nbsafety identified 117 sessions with potential safety errors, and in the remaining 549 sessions, the cells that nbsafety identified as resolving safety issues were more than 7X more likely to be selected by users for re-execution compared to a random baseline, even though the users were not using nbsafety and were therefore not influenced by its suggestions.
Reflection Spectra of Accretion Disks Illuminated by Disk-like Coronae

Relativistic reflection features in the X-ray spectra of black hole binaries and active galactic nuclei are thought to be produced through illumination of a cold accretion disk by a hot corona. In this work, we assume that the corona has the shape of an infinitesimally thin disk with its central axis the same as the rotational axis of the black hole. The corona can either be static or corotate with the accretion disk. We calculate the disk’s emissivity profiles and iron line shapes for a set of coronal radii and heights. We incorporate these emissivity profiles into relxill_nk and we simulate some observations of a black hole binary with the Nuclear Spectroscopic Telescope Array to study the impact of a disk-like coronal geometry on the measurement of the properties of the system, and in particular, on the possibility of testing the Kerr nature of the source. We find that, in general, the astrophysical properties of the accretion disk are recovered well even if we fit the data with a model employing a broken power law or a lamppost emissivity profile, while it is more challenging to constrain the geometric properties of the black hole spacetime.
Achieving Adversarial Robustness Requires An Active Teacher

A new understanding of adversarial examples and adversarial robustness is proposed by decoupling the data generator and the label generator (which we call the teacher). In our framework, adversarial robustness is a conditional concept---the student model is not absolutely robust, but robust with respect to the teacher. Based on the new understanding, we claim that adversarial examples exist because the student cannot obtain sufficient information of the teacher from the training data. Various ways of achieving robustness is compared. Theoretical and numerical evidence shows that to efficiently attain robustness, a teacher that actively provides its information to the student may be necessary.
On the Convergence of the Physicochemical Properties of [n]Helicenes

The results of a series of DFT and DFT−D calculations are reported with the aim to predict the physicochemical properties (equilibrium structures, stabilization energies, redox potentials, excitation and CD spectra, electronic conductivity, and elasticity) of elongating helical structures (i.e., [n]helicenes, n = 1−14). It was shown that many of them are converged at [14]helicene with an interpitch distance of Rpitch = 3.75 A, an incremental stabilization energy of ΔGn = 11 kJ mol-1 (thus suggesting the inherent destabilization of the growing helical structures), an S0−S1 energy difference to the estimated band gap of 2.60 eV, and redox potentials, corresponding to the reduction of [n]helicenes, of E0 = −2.2 V. The elasticity was shown to decrease as a linear function of m (i.e., k ≈ m) with k = 0.01056 a.u. for [14]helicene. Moreover, the conductivity properties were discussed in terms of hole and excess electron densities. Possible implications of the calculated data in nanoscience are discussed.
Enterococcus faecium HDRsEf1 Represses CYP3A29 Expression in the Intestine through the TLR1/2-induced A20 to attenuate the NF-κB/RXR-α Signaling

Background CYP3A29 is crucial for drug metabolism in pigs and an important research model of human CYP3A4. Recent studies have shown that some probiotics can regulate the expression of CYP3A in mammals, but the mechanisms underlying their action are unclear. This study explored whether Enterococcus faecium HDRsEf1 could modulate the expression of CYP3A29 in pig intestine and potential mechanisms. Results we found that HDRsEf1 significantly reduced CYP3A29 expression in pig intestinal tissues and epithelial cells, dependent on cell-cell contact. In IPEC-J2 cells, HDRsEf1 treatment decreased the transcription activity of the CYP3A29 promoter, RXR-α expression and abrogated or mitigated the RXR-α or PXR over-expression-increased CYP3A29 expression. Both RXR-α/PXR over-expression synergistically increased CYP3A29 expression while RXR-α or PXR silencing reduced CYP3A29 expression. Co-immunoprecipitation revealed that RXR-α directly interacted with PXR. HDRsEf1, like a NF-kB inhibitor, significantly decreased the NF-kBp65 activation, RXR-α and CYP3A29 expression, which were abrogated by RXR-α silencing. The down-regulated CYP3A29 expression by HDRsEf1 was associated with increased A20 expression, which were abrogated by TLR1/2 silencing. Conclusions HDRsEf1 inhibits the expression of CYP3A29 through the TLR1/2-induced A20 to attenuate the NF-κB/RXR-α signaling in pig intestinal tissues. Our findings may provide new insights into the mechanisms by which probiotics regulate the expression of CYP450s, and suggest potential risks in the clinical application of probiotics
D2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D2IM-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.
Bayes DistNet - A Robust Neural Network for Algorithm Runtime Distribution Predictions

Randomized algorithms are used in many state-of-the-art solvers for constraint satisfaction problems (CSP) and Boolean satisfiability (SAT) problems. For many of these problems, there is no single solver which will dominate others. Having access to the underlying runtime distributions (RTD) of these solvers can allow for better use of algorithm selection, algorithm portfolios, and restart strategies. Previous state-of-the-art methods directly try to predict a fixed parametric distribution that the input instance follows. In this paper, we extend RTD prediction models into the Bayesian setting for the first time. This new model achieves robust predictive performance in the low observation setting, as well as handling censored observations. This technique also allows for richer representations which cannot be achieved by the classical models which restrict their output representations. Our model outperforms the previous state-of-the-art model in settings in which data is scarce, and can make use of censored data such as lower bound time estimates, where that type of data would otherwise be discarded. It can also quantify its uncertainty in its predictions, allowing for algorithm portfolio models to make better informed decisions about which algorithm to run on a particular instance.
Information-Theoretic Segmentation by Inpainting Error Maximization

We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches.1
Distance-wise Graph Contrastive Learning

Contrastive learning (CL) has proven highly effective in graph-based semi-supervised learning (SSL), since it can efficiently supplement the limited task information from the annotated nodes in graph. However, existing graph CL (GCL) studies ignore the uneven distribution of task information across graph caused by the graph topology and the selection of annotated nodes. They apply CL to the whole graph evenly, which results in an incongruous combination of CL and graph learning. To address this issue, we propose to apply CL in the graph learning adaptively by taking the received task information of each node into consideration. Firstly, we introduce Group PageRank to measure the node information gain from graph and find that CL mainly works for nodes that are topologically far away from the labeled nodes. We then propose our Distance-wise Graph Contrastive Learning (DwGCL) method from two views:(1) From the global view of the task information distribution across the graph, we enhance the CL effect on nodes that are topologically far away from labeled nodes; (2) From the personal view of each node's received information, we measure the relative distance between nodes and then we adapt the sampling strategy of GCL accordingly. Extensive experiments on five benchmark graph datasets show that DwGCL can bring a clear improvement over previous GCL methods. Our analysis on eight graph neural network with various types of architecture and three different annotation settings further demonstrates the generalizability of DwGCL.