Differences in Clinical Outcomes Between Patients With ST-Elevation Versus Non-ST-Elevation Acute Myocardial Infarction in Korea

In Korea, the incidence of acute myocardial infarction has been increasing rapidly. Twelve-month clinical outcomes for 13,133 patients with acute myocardial infarction enrolled in the nationwide prospective Korea Acute Myocardial Infarction Registry study were analyzed according to the presence or absence of ST-segment elevation. Patients with ST-segment elevation myocardial infarction (STEMI) were younger, more likely to be men and smokers, and had poorer left ventricular function with a higher incidence of cardiac death compared to patients with non-ST-segment elevation myocardial infarction (NSTEMI). NSTEMI patients had a higher prevalence of 3-vessel and left main coronary artery disease with complex lesions, and were more likely to have co-morbidities. The in-hospital and 1-month survival rates were higher in NSTEMI patients than in STEMI patients. However, 12-month survival rates was not different between the two groups. In conclusion, NSTEMI patients have worse clinical outcomes than STEMI patients, and therefore should be treated more intensively during clinical follow-up

Digital Rule of Thumb: A Natural Experiment on Autocomplete in Search Engines

Search engines are an essential part of our lives. However, we do not fully understand what affects users\u27 search inputs. One of the most notable features affecting search inputs is autocomplete, an intelligent agent suggesting queries while typing. Understanding the impact of autocomplete helps eCommerce companies retain customers; examining its impact is difficult since all search engines have adopted it, and experiments are risky for firms. We overcome the challenges by leveraging a novel natural experiment of an eCommerce company. Our preliminary results suggest that the deactivation of autocomplete for the incorrect keyword led to a substantial drop in website visits in the PC channel compared to the mobile channel. In addition, website visits substantially shifted from the incorrect keyword to the correct keyword in the mobile channel but not in the PC environment. This short paper is expected to shed new light on our understanding of autocomplete\u27s impact

Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test

Automatic speech recognition systems based on deep learning are mainly trained under empirical risk minimization (ERM). Since ERM utilizes the averaged performance on the data samples regardless of a group such as healthy or dysarthric speakers, ASR systems are unaware of the performance disparities across the groups. This results in biased ASR systems whose performance differences among groups are severe. In this study, we aim to improve the ASR system in terms of group robustness for dysarthric speakers. To achieve our goal, we present a novel approach, sample reweighting with sample affinity test (Re-SAT). Re-SAT systematically measures the debiasing helpfulness of the given data sample and then mitigates the bias by debiasing helpfulness-based sample reweighting. Experimental results demonstrate that Re-SAT contributes to improved ASR performance on dysarthric speech without performance degradation on healthy speech.Comment: Accepted by Interspeech 202

Construction of optimal witness for unknown two-qubit entanglement

Whether entanglement in a state can be detected, distilled, and quantified without full state reconstruction is a fundamental open problem. We demonstrate a new scheme encompassing these three tasks for arbitrary two-qubit entanglement, by constructing the optimal entanglement witness for polarization-entangled mixed-state photon pairs without full state reconstruction. With better efficiency than quantum state tomography, the entanglement is maximally distilled by newly developed tunable polarization filters, and quantified by the expectation value of the witness, which equals the concurrence. This scheme is extendible to multiqubit Greenberger-Horne-Zeilinger entanglement.Comment: Phys. Rev. Lett. 105, 230404 (2010); supplementary information (OWitness_sup.pdf) is included in source zip fil

Muon g−2g-2 and Proton Lifetime in SUSY SU(5) GUTs with Split Superpartners

We consider the interplay of the muon $g-2$ anomaly and the proton decay in the SUSY SU(5) GUTs with generation-independent scalar soft masses. In these scenarios, we introduce a number of $\bf 5+{\bar 5}$ messenger fields with doublet-triplet splitting in general gauge mediation to transmit SUSY breaking to the visible sector by gauge loops. As a result, squarks and sleptons receive generation-independent soft SUSY breaking masses, which are split already at the messenger scale. Taking into account the perturbative unification of gauge couplings as well as the bounds from electroweak precision and vacuum stability bounds, we showed the parameter space in general gauge mediation to explain the muon $g-2$ anomaly with smuon and sneutrino loops while evading the strong bounds on squarks and gluinos from the Large Hadron Collider. We also obtained the dominant Higgsino contributions to the proton decay mode, $p\to K^+{\bar\nu}$, with general generation-independent sparticle masses for squarks and sleptons. Even for split scalar soft masses in our model, however, we found that the bounds from the proton decay are satisfied only if the effective Yukawa couplings of the colored Higgsinos are suppressed further by a factor of order $10^{-4}-10^{-3}$. We illustrated how such a suppression factor is realized in orbifold GUTs in the extra dimension where the colored Higgsinos in the bulk are not coupled to the matter fields localized at the orbifold fixed points at the leading order.Comment: 35 pages, 8 figures, v2: typos fixed and reference updated, v3: version to appear in Phys. Rev.

Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.Comment: Accepted to CVPR 2023. Website: https://kaist-viclab.github.io/old-photo-modernizatio