Miracle-dz/UniFAR-FTUs huggingface dataset:
MVS2D: Efficient Multiview Stereo via Attention-Driven 2D Convolutions

Deep learning has made significant impacts on multiview stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth. While such end-to-end learning of plane-sweeping stereo advances public benchmarks' accuracy, they are typically very slow to compute. We present MVS2D, a highly efficient multi-view stereo algorithm that seamlessly integrates multi-view constraints into single-view net-works via an attention mechanism. Since MVS2D only builds on 2D convolutions, it is at least $2\times faster$ than all the notable counterparts. Moreover, our algorithm produces precise depth estimations and 3D reconstructions, achieving state-of-the-art results on challenging benchmarks ScanNet, SUN3D, RGBD, and the classical DTU dataset. our algorithm also outperforms all other algorithms in the setting of inexact camera poses. Our code is released at https://github.com/zhenpeiyang/MVS2D
Adapting ImageNet-scale models to complex distribution shifts with self-learning

While self-learning methods are an important component in many recent domain adaptation techniques, they are not yet comprehensively evaluated on ImageNet-scale datasets common in robustness research. In extensive experiments on ResNet and EfﬁcientNet models, we ﬁnd that three components are crucial for increasing performance with self-learning: (i) using short update times between the teacher and the student network, (ii) ﬁne-tuning only few afﬁne parameters distributed across the network, and (iii) leveraging methods from robust classiﬁcation to counteract the effect of label noise. We use these insights to obtain drastically improved state-of-the-art results on ImageNet-C (22.0% mCE), ImageNet-R (17.4% error) and ImageNet-A (14.8% error). Our techniques yield further improvements in combination with previously proposed robustiﬁcation methods. Self-learning is able to reduce the top-1 error to a point where no substantial further progress can be expected. We therefore re-purpose the dataset from the Visual Domain Adaptation Challenge 2019 and use a subset of it as a new robustness benchmark (ImageNet-D) which proves to be a more challenging dataset for all current state-of-the-art models (58.2% error) to guide future research efforts at the intersection of robustness and domain adaptation on ImageNet scale.
Experimental determination of bubble sizes in melt refining reactors

An intrusive optical probe for experimental determination of bubble sizes in water models has been designed and tested. The system uses automatic image processing and gives information about bubble size distribution and bubble shapes. Using a standard in-line melt refining rotor the distribution of bubble sizes has been determined. The paper will discuss the experimental findings for this system. The bubbles close to the rotor are very small and grow due to coalescence towards the reactor surface. The effects of rotor frequency, gas purging flowrate and melt flow-rate will be discussed. Removal rates of dissolved oxygen will be presented and theoretical mass transfer theories will be discussed. Finally, the accuracy of this new bubble probe, compared to resistance probes, will be addressed.
Anomalous heating in a colloidal system

Significance A cold system normally takes longer to warm up than a cool system. Yet recent theoretical studies have suggested that the reverse may sometimes be possible. Here, using a colloidal particle in a heat bath, we present experimental evidence for this inverse Mpemba effect. By carefully choosing the energy landscape, we can make the cold system heat up exponentially faster than the heating rate of cool systems. While similar behavior has been seen in systems that cool down—the Mpemba effect—we find that entropic effects generally make anomalous heating harder to observe than anomalous cooling. We report anomalous heating in a colloidal system, an experimental observation of the inverse Mpemba effect, where for two initial temperatures lower than the temperature of the thermal bath, the colder of the two systems heats up faster when coupled to the same thermal bath. For an overdamped, Brownian colloidal particle moving in a tilted double-well potential, we find a nonmonotonic dependence of the heating times on the initial temperature of the system. Entropic effects make the inverse Mpemba effect generically weaker—harder to observe—than the usual Mpemba effect (anomalous cooling). We also observe a strong version of anomalous heating, where a cold system heats up exponentially faster than systems prepared under slightly different conditions.
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation

Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretability, making the behaviors of the deep networks difficult to explain. Hence, their potential applications could be limited in practice. To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP. Given a set of past surrounding-view images, our IVMP first predicts future egocentric semantic maps in bird’s-eye-view space, which are then employed to plan trajectories for self-driving vehicles. The predicted future semantic maps not only provide useful interpretable information, but also allow our motion planning module to handle objects with low probability, thus improving the safety of autonomous driving. Moreover, we also develop an optical flow distillation paradigm, which can effectively enhance the network while still maintaining its real-time performance. Extensive experiments on the nuScenes dataset and closed-loop simulation show that our IVMP significantly outperforms the state-of-the-art approaches in imitating human drivers with a much higher success rate. Our project page is available at https://sites.google. com/view/ivmp.
Deep Learning of the Eddington Tensor in Core-collapse Supernova Simulation

We trained deep neural networks (DNNs) as a function of the neutrino energy density, flux, and the fluid velocity to reproduce the Eddington tensor for neutrinos obtained in our first-principles core-collapse supernova simulation. Although the moment method, which is one of the most popular approximations for neutrino transport, requires a closure relation, none of the analytical closure relations commonly employed in the literature capture all aspects of the neutrino angular distribution in momentum space. In this paper, we develop a closure relation by using DNNs that take the neutrino energy density, flux, and the fluid velocity as the inputs and the Eddington tensor as the output. We consider two kinds of DNNs: a conventional DNN, named a component-wise neural network (CWNN), and a tensor-basis neural network (TBNN). We find that the diagonal component of the Eddington tensor is better reproduced by the DNNs than the M1 closure relation, especially for low to intermediate energies. For the off-diagonal component, the DNNs agree better with the Boltzmann solver than the M1 closure relation at large radii. In the comparison between the two DNNs, the TBNN displays slightly better performance than the CWNN. With these new closure relations at hand, based on DNNs that well reproduce the Eddington tensor at much lower costs, we have opened up a new possibility for the moment method.
Quantifying entanglement in cluster states built with error-prone interactions

Measurement-based quantum computing is an alternative paradigm to the circuit-based model. This approach can be advantageous in certain scenarios, such as when read-out is fast and accurate, but two-qubit gates realized via inter-particle interactions are slow and can be parallelized to efficiently create a cluster state. However, understanding how two-qubit errors impact algorithm accuracy and developing experimentally viable approaches to characterize cluster-state fidelity are outstanding challenges. Here, we consider one-dimensional cluster states built from controlled phase, Ising, and XY interactions with slow two-qubit error in the interaction strength, consistent with error models of interactions found in a variety of qubit architectures. We detail an experimentally viable teleportation fidelity that offers a measure of the impact of these errors on the cluster state. Our fidelity calculations show that the error has a distinctly different impact depending on the underlying interaction used for the two-qubit entangling gate. In particular, the Ising and XY interactions can allow perfect teleportation through the cluster state even with large errors, but the controlled phase interaction does not. Nonetheless, we find that teleportation through cluster state chains of size N has a maximum two-qubit error for teleportation along a quantum channel that decreases as N−1/2. To enable construction of larger cluster states, we design lowest-order refocusing pulses for correcting these slow errors in the interaction strength. Our work generalizes to higher-dimensional cluster states and sets the stage for experiments to monitor the growth of entanglement in cluster states built from error-prone interactions.
End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks

Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based on generative adversarial networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm. Our model consists of an encoder–decoder architecture that receives raw video as input and generates speech, which is then fed to a waveform critic and a power critic. The use of an adversarial loss based on these two critics enables the direct synthesis of the raw audio waveform and ensures its realism. In addition, the use of our three comparative losses helps establish direct correspondence between the generated audio and the input video. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID, and that it is the first end-to-end model to produce intelligible speech for Lip Reading in the Wild (LRW), featuring hundreds of speakers recorded entirely “in the wild. ” We evaluate the generated samples in two different scenarios—seen and unseen speakers—using four objective metrics which measure the quality and intelligibility of artificial speech. We demonstrate that the proposed approach outperforms all previous works in most metrics on GRID and LRW.
Comparison of outcomes for percutaneous coronary intervention in men and women with unprotected left main disease

BACKGROUND Percutaneous coronary intervention (PCI) in patients with unprotected left main coronary artery disease (ULMCAD) is increasing strategy in coronary artery patients. However, there is a lack of knowledge on the impact of sex on outcomes of patients undergoing ULMCAD PCI. METHODS From January 2004 to December 2015, there were 3,960 patients undergoing ULMCAD PCI at our institution, including 3,121 (78.8%) men and 839 (21.2%) women. The clinical outcome included the incidence of major adverse cardiac events (MACE) (the composite of all-cause death, myocardial infarction (MI), and revascularization), all-cause death, MI, revascularization at three years follow-up. RESULTS Compared with men, women had not significantly different MACE (14.7% vs. 14.6%, P = 0.89, all-cause death (3.5% vs. 3.7%, P = 0.76), MI (5.0% vs. 4.3%, P = 0.38), revascularization (9.1% vs. 8.9%, P = 0.86), respectively. After adjustment, rates of MACE (HR = 1.49; 95% CI: 1.24−1.81;P < 0.0001) and all-cause death (HR = 1.65; 95% CI: 1.09−2.48; P = 0.017) occurred more frequently in male patients, as well as revascularization (HR = 1.46; 95% CI: 1.16−1.85;P = 0.001). CONCLUSION In this analysis, compared to men, women undergoing ULMCAD PCI have better outcomes of MACE, all-cause death, and revascularization.
Antioxidants for the Treatment of Retinal Disease: Summary of Recent Evidence

Abstract Retinal tissue is prone to oxidant burden and oxidative stress secondary to the generation of reactive oxygen species from high metabolic demand. The formation of reactive oxygen species occurs primarily from the mitochondrial respiratory chain as well as several enzymatic and oxidation reactions that occur in the neurosensory retina and retinal pigment epithelium. This oxidative stress has been implicated in the pathogenesis of several retinal diseases and the role of antioxidants as a therapeutic treatment shows promise in slowing the progression of certain diseases. The aim of this narrative review is to describe the mechanisms of retinal oxidative stress and summarize the current available evidence for antioxidants as a treatment for vitreoretinal disorders.
Association of Age and Sex with Metabolic Syndrome in Taiwanese Adults

Objective This study aimed to investigate the association of age and sex with metabolic syndrome (MS) in Taiwanese adults. Methods We extracted information of 4307 men and 4783 women aged 30–70 from the Taiwan Biobank. Results The interaction between age and sex on MS was significant (p-value = 0.0001). After stratification by sex, men and women aged 50–70 years (reference: 30≤age<50 years) had a higher risk of MS. The odds ratio (OR), 95% confidence interval (CI) was 2.316, 1.936–2.772 in men and 3.101, 2.561–3.754 in women. After stratification by age, men aged 50–70 years had a lower risk of MS compared to women (OR, 95% CI = 0.713, 0.598–0.851). Conclusion The interaction between age and sex on MS was significant. Sex-wise, both men and women aged 50–70 years had a higher likelihood of MS. Age-wise, men aged 50–70 years had a lower risk of MS compared to women.
A Frailty Assessment Tool to Predict In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Background The exacerbation of chronic obstructive pulmonary disease (AECOPD) is a chronic, frequent, and life-threatening lung disease. In 2014, a frailty index (FI) based on deficits in commonly used laboratory tests (FI-Lab) was suggested to identify older adults at increased risk of death. Objective We aim to study the prognostic value of the FI-Lab in older Chinese patients who were admitted because of AECOPD. Methods We screened 1932 older patients hospitalized with AECOPD from September 2016 to June 2019 at Zhenjiang First People’s Hospital, China. A multivariate logistic regression analysis was used to identify prognostic factors for in-hospital mortality. Results A total of 77 survivors and 77 non-survivors were finally included in the study. Both the mean DECAF (including dyspnea, eosinopenia, consolidation, acidemia, and atrial fibrillation) score and the mean FI-Lab value of non-survivors were statistically higher than those of survivors (4.45 ± 0.80 versus 3.03 ± 0.90, P=0.000; 0.51 ± 0.13 versus 0.29 ± 0.10, P=0.000, respectively). Logistic regression analysis suggested that DECAF Rank and FI-Lab Rank were strongly related factors of death in AECOPD patients. The areas under the receiver-operating characteristic (ROC) curves were 0.906 for FI-Lab and 0.870 for DECAF (P=0.2991). Conclusion FI-Lab is a simple, efficient, and objective tool to stratify the risk of in-hospital mortality of AECOPD.
Long-Term Prognosis in Young Patients with Acute Coronary Syndrome Treated with Percutaneous Coronary Intervention

Background Acute coronary syndrome (ACS) at a young age is uncommon. Limited data regarding the long-term follow-up and prognosis in this population are available. Our objectives were to evaluate the long-term clinical outcomes of patients presenting with ACS at a young age and to assess factors that predict long-term prognosis. Methods A retrospective analysis of consecutive young patients (male below 40 and female below 50 years old) that were admitted with ACS and underwent percutaneous coronary intervention (PCI) between the years 1997 and 2009. Demographics, clinical characteristics, and clinical outcomes including major cardiovascular (CV) events and mortality were analyzed. Multivariable cox proportional hazard model was performed to identify predictors of long-term prognosis. Results One-hundred sixty-five patients were included with a mean follow-up of 9.1±4.6 years. Most patients were men (88%), and mean age (years) was 36.8±4.2. During follow-up, 15 (9.1%) died, 98 (59.4%) patients had at least one major CV event, 22 (13.3%) patients had more than two CV events, and the mean number of recurrent CV events was 1.4±1.48 events per patient. In multivariate analysis, the strongest predictors of major CV events and/or mortality were coronary intervention without stent insertion (HR1.77; 95% CI 1.09–2.9), LAD artery involvement (HR 1.59; 95% CI 1.04–2.44) and hypertension (HR 1.6; 95% CI 1.0–2.6). Conclusion Patients with ACS in young age are at high risk for major CV and/or mortality in long-term follow-up with a high rate of recurrent CV events. Close follow-up and risk factor management for secondary prevention have a major role, particularly in this population.
Emotion regulation and the association between PTSD, diet, and exercise: a longitudinal evaluation among US military veterans

ABSTRACT Background: Posttraumatic stress disorder (PTSD) is associated with premature onset of chronic health conditions such as cardiovascular disease. Diet and exercise are behavioural contributors to physical health, and research suggests they are influenced by psychiatric symptoms, including PTSD. Objectives: The aim of this study was to examine longitudinal associations between PTSD and exercise and diet quality and to test if emotion regulation strategies contribute to the link between PTSD and these health behaviours. Method: A representative sample of US military veterans (n = 860 at Time 1, n = 503 at Time 2, mean age = 63 years, 91.5% male) were assessed twice over the course of approximately three years. Results: Mediation models revealed that the association between baseline PTSD symptom severity and subsequent diet quality was mediated by emotion suppression (measured at Time 2; indirect B = −.03; 95% CI: −.059 to −.002). Trauma exposure also directly predicted diet quality (B = −.31; p = .003). There were no significant direct or indirect associations between PTSD severity or trauma exposure and exercise engagement. Conclusions: These results suggest that PTSD symptoms are associated with worse diet quality and that the consumption of unhealthy food may be driven by efforts to suppress emotion. This carries implications for understanding and treating medical comorbidities among those with traumatic stress.
Heart failure in the elderly

Heart failure (HF) is a clinical syndrome caused by structural and/or functional cardiac abnormalities, resulting in a reduced cardiac output and/or elevated intracardiac filling pressures at rest or during stress. HF is a major public health problem with high prevalence and incidence, involving both high morbidity and mortality, but also high economic costs. The incidence of HF progressively increases with age, reaching around 20% among people over 75 years old. Indeed, HF represents the leading cause of hospitalization in patients older than 65 years in Western countries. Hence, some authors even consider HF a geriatric syndrome, entailing worse prognosis and high residual disability, and often associating some complex comorbidities, common in older population, that may further complicate the course of the disease. On the other hand, however, clinical course and prognosis may be often difficult to predict. In this article, main pathophysiological issues related to the aging heart are addressed, together with key aspects related to both diagnosis and prognosis in elderly patients with HF. Besides, main geriatric conditions, common in the elderly population, are reviewed, highlighting the importance of a comprehensive and multidisciplinary approach.
Phenotype-Genotype Characterization and Antibiotic-Resistance Correlations Among Colonizing and Infectious Methicillin-Resistant Staphylococcus aureus Recovered from Intensive Care Units

Introduction Methicillin-resistant Staphylococcus aureus (MRSA) presents a profound hazard to public health. MRSA colonizing skin, mucous membranes, and the anterior nares without clinical symptoms is termed “colonizing MRSA”. Upon manifestation of clinical symptoms, it is termed “infectious MRSA”. Here, we characterize and differentiate colonizing and infectious MRSA, and analyze the phenotypic-genotypic and antibiotic susceptibility correlations. Methodology Clinical MRSA isolates were recovered from intensive care units (ICUs) of two major Egyptian hospitals and their biofilm formation ability was tested. Antibiograms against 16 antibiotics were determined, in addition to the minimum inhibitory concentrations (MICs) of vancomycin and linezolid. The entire collection was typed by enterobacterial repetitive intergenic consensus (ERIC)-PCR, as well as multi-locus sequence typing (MLST). Representative resistance and virulence genes were detected by PCR amplification. Results Forty-nine isolates were confirmed as MRSA, of which 30 isolates were infectious and 19 were colonizing. Versatile resistance patterns were observed in both groups of isolates. We report a higher tendency for biofilm-formation and borderline minimum inhibitory concentrations among infectious isolates. A Positive antibiotic correlation was observed between susceptibility to protein synthesis inhibitors and cell wall inhibitors. Positive correlations were observed between isolation site and rifampicin resistance: nasal samples were enriched in rifampicin-resistant isolates, while urine and blood samples were enriched in susceptible ones. Furthermore, biofilm formation ability was slightly associated with amikacin resistance, and an association between teicoplanin resistance and the presence of the Panton-Valentine leukocidin gene was the only significant phenotype-genotype correlation observed. Finally, ERIC typing and MLST had congruent results. Conclusion Linezolid and vancomycin are still the most convenient choice for MRSA treatment. ERIC PCR and MLST show promising typing combination that could be easily used periodically for tracking the genotypic changes of MRSA, especially within the healthcare facilities. Several correlations were established between groups of antibiotics and the genotypes/phenotypes of the selected isolates.