Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of epicardial adipose tissue with metabolic risk factors on cardiovascular outcomes: serial coronary computed tomography angiography study

Background/Aims Epicardial adipose tissue (EAT) shares pathophysiological properties with other visceral fats and potentially triggers local inflammation. However, the association of EAT with cardiovascular disease (CVD) is still debatable. The study aimed to observe the changes and associations in EAT and risk factors over time, as well as to investigate whether EAT was associated with CVD. Methods A total of 762 participants from Seoul National University Hospital (SNUH) and SNUH Gangnam Center were included in this study. EAT was measured using coronary computed tomography angiography. Results Baseline EAT level was positively associated with body mass index (BMI), calcium score, atherosclerotic cardiovascular disease (ASCVD) 10-year risk score, glucose, triglycerides (TG)/high-density lipoprotein (HDL), but not with total cholesterol, low-density lipoprotein (LDL). At follow-up, EAT levels increased in all groups, with low EAT groups demonstrating a significant increase in EAT per year. Change in EAT was associated with a change in BMI, TG/HDL, and glucose, while changes in LDL, calcium score, and ASCVD 10-year risk score were not associated. Although calcium score and ASCVD 10-year risk score were associated with CVD events, baseline information of EAT, baseline EAT/body surface area, or EAT change was not available. Conclusions Metabolic risks, e.g., BMI, TG/HDL, and glucose, were associated with EAT change per year, whereas classical CVD risks, e.g., LDL, calcium score, and ASCVD 10-year risk score, were not. The actual CVD event was not associated with EAT volume, warranting future studies combining qualitative assessments with quantitative ones.
Bacteriophage and antibiotic combination therapy for recurrent Enterococcus faecium bacteremia

ABSTRACT Enterococcus faecium is a member of the human gastrointestinal (GI) microbiota but can also cause invasive infections, especially in immunocompromised hosts. Enterococci display intrinsic resistance to many antibiotics, and most clinical E. faecium isolates have acquired vancomycin resistance, leaving clinicians with a limited repertoire of effective antibiotics. As such, vancomycin-resistant E. faecium (VREfm) has become an increasingly difficult to treat nosocomial pathogen that is often associated with treatment failure and recurrent infections. We followed a patient with recurrent E. faecium bloodstream infections (BSIs) of increasing severity, which ultimately became unresponsive to antibiotic combination therapy over the course of 7 years. Whole-genome sequencing (WGS) showed that the patient was colonized with closely related E. faecium strains for at least 2 years and that invasive isolates likely emerged from a large E. faecium population in the patient’s gastrointestinal (GI) tract. The addition of bacteriophage (phage) therapy to the patient’s antimicrobial regimen was associated with several months of clinical improvement and reduced intestinal burden of VRE and E. faecium. In vitro analysis showed that antibiotic and phage combination therapy improved bacterial growth suppression compared to therapy with either alone. Eventual E. faecium BSI recurrence was not associated with the development of antibiotic or phage resistance in post-treatment isolates. However, an anti-phage-neutralizing antibody response occurred that coincided with an increased relative abundance of VRE in the GI tract, both of which may have contributed to clinical failure. Taken together, these findings highlight the potential utility and limitations of phage therapy to treat antibiotic-resistant enterococcal infections. IMPORTANCE Phage therapy is an emerging therapeutic approach for treating bacterial infections that do not respond to traditional antibiotics. The addition of phage therapy to systemic antibiotics to treat a patient with recurrent E. faecium infections that were non-responsive to antibiotics alone resulted in fewer hospitalizations and improved the patient's quality of life. Combination phage and antibiotic therapy reduced E. faecium and VRE abundance in the patient's stool. Eventually, an anti-phage antibody response emerged that was able to neutralize phage activity, which may have limited clinical efficacy. This study demonstrates the potential of phages as an additional option in the antimicrobial toolbox for treating invasive enterococcal infections and highlights the need for further investigation to ensure phage therapy can be deployed for maximum clinical benefit.
Vertical and Horizontal Distribution of Bacterial Communities in Alluvial Groundwater of the Nakdong River Bank

ABSTRACT As a collaborative effort to characterize a pilot test site for managed aquifer recharge (MAR), vertical and horizontal distributions of microbial communities in the river bank subsurface were investigated to assess the ecological effects after the operation of the MAR using the river water adjacent to the site. Along with a geochemical analysis, barcoded pyrosequencing was performed using the genomic DNAs extracted from the subsurface groundwater/sediment samples retrieved from three multilevel wells among the installed cluster of 14 boreholes. A total of 9 samples from 3 depths (10, 15–25, and 33 m below the ground surface) of each borehole showed higher bacterial abundance and diversity in the shallow (10 m) depths than in the deep (33 m) groundwater. In addition, there was a slight separation of the microbial communities between the depths based on the nonmetric multidimensional scaling analysis of the Yue and Clayton distance and the distance-weighted UniFrac analysis. The phylum Proteobacteria was dominant in all the samples at the sequence abundance of 64.0–97.8% with the total operational taxonomic units of 3375 at the species level, while among the total 288 genera, the genus Pseudomonas and an unclassified genus from Betaproteobacteria were the most abundant across the samples. The community separation between the shallow and the deep groundwater seemed to be correlated with depth differences, supported by differences in the dissolved oxygen (DO) concentration and oxidation-reduction potential (ORP). In the study site, unusually high values of electrical conductivity (EC) were found in the deep groundwater, but those values were unlikely to contribute to the community separation between the shallow and deep groundwater, unlike the DO and ORP values, which were found to influence the community differences.
Evaluating the Experience of LGBTQ+ People Using Large Language Model Based Chatbots for Mental Health Support

LGBTQ+ individuals are increasingly turning to chatbots powered by large language models (LLMs) to meet their mental health needs. However, little research has explored whether these chatbots can adequately and safely provide tailored support for this demographic. We interviewed 18 LGBTQ+ and 13 non-LGBTQ+ participants about their experiences with LLM-based chatbots for mental health needs. LGBTQ+ participants relied on these chatbots for mental health support, likely due to an absence of support in real life. Notably, while LLMs offer prompt support, they frequently fall short in grasping the nuances of LGBTQ-specific challenges. Although fine-tuning LLMs to address LGBTQ+ needs can be a step in the right direction, it isn't the panacea. The deeper issue is entrenched in societal discrimination. Consequently, we call on future researchers and designers to look beyond mere technical refinements and advocate for holistic strategies that confront and counteract the societal biases burdening the LGBTQ+ community.
The Order Oracle: a New Concept in The Black Box Optimization Problems

Frequently, the burgeoning field of black-box optimization encounters challenges due to a limited understanding of the mechanisms of the objective function. In this paper, we provide the new concept of the"Order Oracle"as a novel approach to solving such problems. The Order Oracle offers a unique perspective, using only access to the order between function values (possibly with some bounded noise), but without assuming access to their values. As theoretical results, we provide estimates of the convergence rates of the algorithms (obtained by integrating the Order Oracle into existing optimization"tools") in the non-convex, convex, and strongly convex settings. Our theoretical results demonstrate the effectiveness of the Order Oracle through numerical experiments. Finally, we show the possibility of accelerating the convergence of such algorithms.
Nonreciprocal collective dynamics in a mixture of phoretic Janus colloids

A multicomponent mixture of Janus colloids with distinct catalytic coats and phoretic mobilities is a promising theoretical system to explore the collective behavior arising from nonreciprocal interactions. An active colloid produces (or consumes) chemicals, self-propels, drifts along chemical gradients, and rotates its intrinsic polarity to align with a gradient. As a result the connection from microscopics to continuum theories through coarse-graining couples densities and polarization fields in unique ways. Focusing on a binary mixture, we show that these couplings render the unpatterned reference state unstable to small perturbations through a variety of instabilities including oscillatory ones which arise on crossing an exceptional point or through a Hopf bifurcation. For fast relaxation of the polar fields, they can be eliminated in favor of the density fields to obtain a microscopic realization of the Nonreciprocal Cahn-Hilliard model for two conserved species with two distinct sources of non-reciprocity, one in the interaction coefficient and the other in the interfacial tension. Our work establishes Janus colloids as a versatile model for a bottom-up approach to both scalar and polar active mixtures.
Investigating Neutron Scattering in a Spherical Proportional Counter: A Tabletop Experiment

In this paper, we report on a tabletop experiment studying neutron scattering in a Spherical Proportional Counter using an Am-Be source. Systematic studies were carried out to investigate the effect of gas mixture, pressure, operating voltage, and sphere size on the drift time-rise time relationship of the signal in a spherical proportional counter. Our experimental results showed good agreement with MagBoltz simulations. These findings are a crucial step towards measuring the quenching factor in gases using a neutron beam for the New Experiments With Spheres-Gas (NEWS-G) experiment and has important implications for the development of neutron detection techniques and their potential applications in nuclear and particle physics.
GrounDial: Human-norm Grounded Safe Dialog Response Generation

Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial costs. To remove the dependency, we propose GrounDial, where response safety is achieved by grounding responses to commonsense social rules without requiring fine-tuning. A hybrid approach of in-context learning and human-norm-guided decoding of GrounDial enables the response to be quantitatively and qualitatively safer even without additional data or tuning.
Deriving the Gibbons-Maldacena-Nunez no-go theorem from the Raychaudhuri equation

In this article, we point out that to solve the null Raychaudhuri equation for higher dimensional spacetime with accelerating FRW solution in external directions and static compact internal directions, it is necessary to violate the Strong Energy condition in higher dimensions. This constraint is well-known in obtaining accelerating cosmological solutions in string compactification, first described by Gibbons-Maldacena-Nunez. In deriving this constraint, we do not make any assumptions regarding the matter content.
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.
Identifying Tractable Quantified Temporal Constraints within Ord-Horn

The constraint satisfaction problem, parameterized by a relational structure, provides a general framework for expressing computational decision problems. Already the restriction to the class of all finite structures forms an interesting microcosm on its own, but to express decision problems in temporal reasoning one has to take a step beyond the finite-domain realm. An important class of templates used in this context are temporal structures, i.e., structures over $\mathbb{Q}$ whose relations are first-order definable using the usual countable dense linear order without endpoints. In the standard setting, which allows only existential quantification over input variables, the complexity of finite and temporal constraints has been fully classified. In the quantified setting, i.e., when one also allows universal quantifiers, there is only a handful of partial classification results and many concrete cases of unknown complexity. This paper presents a significant progress towards understanding the complexity of the quantified constraint satisfaction problem for temporal structures. We provide a complexity dichotomy for quantified constraints over the Ord-Horn fragment, which played an important role in understanding the complexity of constraints both over temporal structures and in Allen's interval algebra. We show that all problems under consideration are in P or coNP-hard. In particular, we determine the complexity of the quantified constraint satisfaction problem for $(\mathbb{Q};x=y\Rightarrow x\geq z)$, hereby settling a question open for more than ten years.
A Dataset for the Detection of Dehumanizing Language

Dehumanization is a mental process that enables the exclusion and ill treatment of a group of people. In this paper, we present two data sets of dehumanizing text, a large, automatically collected corpus and a smaller, manually annotated data set. Both data sets include a combination of political discourse and dialogue from movie subtitles. Our methods give us a broad and varied amount of dehumanization data to work with, enabling further exploratory analysis as well as automatic classification of dehumanization patterns. Both data sets will be publicly released.
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping

CycleGAN has been proven to be an advanced approach for unsupervised image restoration. This framework consists of two generators: a denoising one for inference and an auxiliary one for modeling noise to fulfill cycle-consistency constraints. However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints. This poses a threat to the effectiveness of the cycle-consistency loss, leading to stripe noise residual in the denoised image. To address the above issue, we present a novel framework for single-frame infrared image destriping, named DestripeCycleGAN. In this model, the conventional auxiliary generator is replaced with a priori stripe generation model (SGM) to introduce vertical stripe noise in the clean data, and the gradient map is employed to re-establish cycle-consistency. Meanwhile, a Haar wavelet background guidance module (HBGM) has been designed to minimize the divergence of background details between the different domains. To preserve vertical edges, a multi-level wavelet U-Net (MWUNet) is proposed as the denoising generator, which utilizes the Haar wavelet transform as the sampler to decline directional information loss. Moreover, it incorporates the group fusion block (GFB) into skip connections to fuse the multi-scale features and build the context of long-distance dependencies. Extensive experiments on real and synthetic data demonstrate that our DestripeCycleGAN surpasses the state-of-the-art methods in terms of visual quality and quantitative evaluation. Our code will be made public at https://github.com/0wuji/DestripeCycleGAN.
Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio

We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, we propose to decouple both training processes. This enables us to leverage existing self-supervised autoencoders pre-trained on much larger data (EnCodecMAE), providing representations with better generalization. APNet allows prototypes' reconstruction to waveforms for interpretability relying on the nearest training data samples. In contrast, we explore using a diffusion decoder that allows reconstruction without such dependency. We evaluate our method on datasets for music instrument classification (Medley-Solos-DB) and genre recognition (GTZAN and a larger in-house dataset), the latter being a more challenging task not addressed with prototypical networks before. We find that the prototype-based models preserve most of the performance achieved with the autoencoder embeddings, while the sonification of prototypes benefits understanding the behavior of the classifier.
Topologies of maximally extended non-Hausdorff Misner Space

Misner (1967) space is a portion of 2-dimensional Minkowski spacetime, identified under a boost $\mathcal B$. It is well known that the maximal analytic extension of Misner space that is Hausdorff consists of one half of Minkowski spacetime, identified under $\mathcal B$; and Hawking and Ellis (1973) have shown that the maximal analytic extension that is non-Hausdorff is equal to the full Minkowski spacetime with the point $Q$ at the origin removed, identifed under $\mathcal B$. In this paper I show that, in fact, there is an infinite set of non-Hausdorff maximal analytic extensions, each with a different causal structure. The extension constructed by Hawking and Ellis is the simplest of these. Another extension is obtained by wrapping an infinite number of copies of Minkowski spacetime around the removed $Q$ as a helicoid or Riemann surface and then identifying events under the boost $\mathcal B$. The other extensions are obtained by wrapping some number $n$ of successive copies of Minkowski spacetime around the missing $Q$ as a helicoid, then identifying the end of the $n$'th copy with the beginning of the initial copy, and then identifying events under $\mathcal B$. I discuss the causal structure and covering spaces of each of these extensions.
Low-loss multilevel operation using lossy PCM-integrated silicon photonics

Chalcogenide phase-change materials (PCMs) offer new paradigms for programmable photonic integrated circuits (PICs) thanks to their zero static energy and significant refractive index contrast. However, prototypical PCMs, such as GeSbTe (GST), are lossy in their crystalline phase, albeit transparent in the amorphous state. Moreover, electrically switching PCMs to intermediate states is a stochastic process, limiting programming accuracy. As a result, achieving both low-loss and deterministic multi-level operation with GST remains challenging. Although low-loss PCMs, such as Sb2S3 and Sb2Se3, have been discovered in recent years, they are much less technologically mature. In this work, we propose a design with multiple GST segments to overcome the challenge of deterministic multilevel operation. GST segments are individually controlled by interleaved silicon PIN diode heaters in a binary but reliable fashion, and multiple levels are encoded in their phase sequence. A 1 x 1 programmable unit with two unequal GST segments is experimentally demonstrated, showcasing four distinct operation levels and negligible thermal crosstalk with only one pair of metal contacts. We then extend the design to 1 x 2 and 2 x 2 programmable units. For the 2 x 2 programmable unit design, we propose a phase-detuned three-waveguide directional coupler structure to mitigate the absorption and radiation loss, showing<-1.2 dB loss and three splitting ratios. Our work provides a new path toward low-loss and multi-level optical switches using lossy PCMs.
Context Composing for Full Line Code Completion

Code Completion is one of the most used Integrated Development Environment (IDE) features, which affects the everyday life of a software developer. Modern code completion approaches moved from the composition of several static analysis-based contributors to pipelines that involve neural networks. This change allows the proposal of longer code suggestions while maintaining the relatively short time spent on generation itself. At JetBrains, we put a lot of effort into perfecting the code completion workflow so it can be both helpful and non-distracting for a programmer. We managed to ship the Full Line Code Completion feature to PyCharm Pro IDE and proved its usefulness in A/B testing on hundreds of real Python users. The paper describes our approach to context composing for the Transformer model that is a core of the feature's implementation. In addition to that, we share our next steps to improve the feature and emphasize the importance of several research aspects in the area.
Sobolev Training for Operator Learning

This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance. Our research reveals that integrating derivative information into the loss function enhances the training process, and we propose a novel framework to approximate derivatives on irregular meshes in operator learning. Our findings are supported by both experimental evidence and theoretical analysis. This demonstrates the effectiveness of Sobolev Training in approximating the solution operators between infinite-dimensional spaces.