Miracle-dz/UniFAR-FTUs huggingface dataset:
Dyadic effects of work overcommitment on depression in dual-earner couples: based on the actor–partner interdependence model

ABSTRACT In this study, we aimed to examine the actor and partner effects of overcommitment on depression in dual-earner couples and the potential mediating role of emotional exhaustion at the actor and partner levels. With a cross-sectional design, 172 dual-earner couples in Chongqing, China, completed the measures of overcommitment, emotional exhaustion, and depression. The results showed that (a) overcommitment has a significant actor effect on depression for the husband and wife, and there were gender differences in the partner effects; that is, a wife’s overcommitment has a significant partner effect on the husband’s depression, while a husband’s partner effect was not significant; (b) emotional exhaustion mediated the effects of overcommitment on depression for both husbands and wives at the actor level, while at the partner level, only a wife’s exhaustion mediated the partner effect of a wife’s overcommitment on a husband’s depression. Focusing on couples’ interdependence, these results enriched the understanding of the relationship between overcommitment and depression within dual-earner couples.
Complete Genome Sequence of the Ice-Nucleation-Active Pseudomonas syringae Strain MUP20, Isolated from Frost-Damaged Wheat (Triticum aestivum cv. Scepter) in Western Australia

Pseudomonas syringae MUP20 was isolated from Western Australian frost-damaged wheat. The MUP20 complete genome contained a 6,045,198-bp single circular chromosome with a GC content of 59.03%. IMG/M genome annotation identified 5,245 protein-coding genes, 1 of which encoded an ice nucleation protein containing 20 occurrences of a highly repetitive PF00818 domain. ABSTRACT Pseudomonas syringae MUP20 was isolated from Western Australian frost-damaged wheat. The MUP20 complete genome contained a 6,045,198-bp single circular chromosome with a GC content of 59.03%. IMG/M genome annotation identified 5,245 protein-coding genes, 1 of which encoded an ice nucleation protein containing 20 occurrences of a highly repetitive PF00818 domain.
AB INITIO MOLECULAR DYNAMICS SIMULATIONS ON LOCAL STRUCTURE AND ELECTRONIC PROPERTIES IN LIQUID Sb FROM 913 K TO 1193 K

Ab initio molecular dynamics simulations on liquid Sb have been carried out at five different temperatures from 913 K to 1193 K. We have investigated the temperature dependence of structure properties including structural factor S(Q), pair correlation function g(r), bond-angle distribution function g3(θ), cluster properties and bond order parameter Q4 and Q6. A shoulder was reproduced in the high wave number side of the first peak in the S(Q) implying that the residual structure units of crystalline Sb remain in liquid Sb. There is a noticeable bend at around 1023 K in the temperature dependence of the first-peak height of S(Q), the cluster properties and bond order parameter Q4, respectively, indicating that an abnormal structural change may occur at 973–1023 K.
Prognostic value and adverse events of cytoreductive surgery with hyperthermic intraperitoneal chemotherapy in primary advanced and platinum-sensitive recurrent epithelial ovarian cancer: a systematic review and meta-analysis

Abstract Background The original meta-analysis of hyperthermic intraperitoneal chemotherapy (HIPEC) is already outdated, owing to the latest trial results. This study aimed to clarify the efficacy and adverse events of cytoreductive surgery with HIPEC compared to conventional therapy for advanced and platinum-sensitive recurrent epithelial ovarian cancer (OC). Methods In this meta-analysis, phase II/III controlled trials regarding ‘HIPEC’ and ‘ovarian cancer’ were searched for in electronic databases from inception to March 2022. Results Twenty-one studies were included in the quantitative synthesis. The pooled hazard ratio [HR] in the HIPEC group for progression-free survival (PFS) (HR = 0.61, 95% confidence interval [CI]: 0.45–0.83, p = .002) and overall survival (OS) (HR = 0.65, 95% CI: 0.51–0.82, p < .001) were improved in the HIPEC group compared with the non-HIPEC group. For primary advanced disease, OS and PFS were significantly increased in patients receiving interval debulking surgery + HIPEC, whereas PFS was not significantly different between primary debulking surgery (PDS) + HIPEC and PDS alone. For platinum-sensitive recurrent disease, no correlation was observed for PFS and OS between the HIPEC and non-HIPEC groups (p < .05). The incidence of procedure-related complications was higher in the HIPEC group than in the non-HIPEC group (odds ratio = 1.93, 95% CI: 1.24–3.01, p < .01). The morbidity of leukopenia, neutropenia, nausea, hypoalbuminemia, and grades III–IV electrolyte disturbance was higher in the HIPEC group than in the non-HIPEC group. However, HIPEC administration reduced the risk of intra-abdominal bleeding and constipation. Conclusion HIPEC-based regimens improved the clinical prognosis for primary advanced OC, whereas no significant value was elicited for recurrent OC.
A flexible class of generalized joint frailty models for the analysis of survival endpoints

This article focuses on shared frailty models for correlated failure times, as well as joint frailty models for the simultaneous analysis of recurrent events (eg, appearance of new cancerous lesions or hospital readmissions) and a major terminal event (typically, death). As extensions of the Cox model, these joint models usually assume a frailty proportional hazards model for each of the recurrent and terminal event processes. In order to extend these models beyond the proportional hazards assumption, our proposal is to replace these proportional hazards models with generalized survival models, for which the survival function is modeled as a linear predictor through a link function. Depending on the link function considered, these can be reduced to proportional hazards, proportional odds, additive hazards, or probit models. We first consider a fully parametric framework for the time and covariate effects. For proportional and additive hazards models, our approach also allows the use of smooth functions for baseline hazard functions and time‐varying coefficients. The dependence between recurrent and terminal event processes is modeled by conditioning on a shared frailty acting differently on the two processes. Parameter estimates are provided using the maximum (penalized) likelihood method, implemented in the R package frailtypack (function GenfrailtyPenal). We perform simulation studies to assess the method, which is also illustrated on real datasets.
Genome Sequences of Comamonadaceae Bacteria OS-1 and OS-4, Two Highly H2O2-Sensitive Strains Isolated from Pond Water

The Comamonadaceae bacterial strains OS-1 and OS-4 were isolated from pond water and were found to be highly sensitive to hydrogen peroxide in the agar plates. Here, we report the nearly complete and complete genome sequences, respectively, of these two strains. ABSTRACT The Comamonadaceae bacterial strains OS-1 and OS-4 were isolated from pond water and were found to be highly sensitive to hydrogen peroxide in the agar plates. Here, we report the nearly complete and complete genome sequences, respectively, of these two strains.
Single Particle Analysis of H3N2 Influenza Entry Differentiates the Impact of the Sialic Acids (Neu5Ac and Neu5Gc) on Virus Binding and Membrane Fusion

Influenza A virus (IAV) infections continue to threaten human health, causing over 300,000 deaths yearly. IAV infection is initiated by the binding of influenza glycoprotein hemagglutinin (HA) to host cell sialic acids (Sias) and the subsequent viral-host membrane fusion. ABSTRACT Entry of influenza A viruses (IAVs) into host cells is initiated by binding to sialic acids (Sias), their primary host cell receptor, followed by endocytosis and membrane fusion to release the viral genome into the cytoplasm of the host cell. Host tropism is affected by these entry processes, with a primary factor being receptor specificity. Sias exist in several different chemical forms, including the hydroxylated N-glycolylneuraminic acid (Neu5Gc), which is found in many hosts; however, it has not been clear how modified Sias affect viral binding and entry. Neu5Gc is commonly found in many natural influenza hosts, including pigs and horses, but not in humans or ferrets. Here, we engineered HEK293 cells to express the hydoxylase gene (CMAH) that converts Neu5Ac to Neu5Gc, or knocked out the Sia-CMP transport gene (SLC35A1), resulting in cells that express 95% Neu5Gc or minimal level of Sias, respectively. H3N2 (X-31) showed significantly reduced infectivity in Neu5Gc-rich cells compared to wild-type HEK293 (>95% Neu5Ac). To determine the effects on binding and fusion, we generated supported lipid bilayers (SLBs) derived from the plasma membranes of these cells and carried out single particle microscopy. H3N2 (X-31) exhibited decreased binding to Neu5Gc-containing SLBs, but no significant difference in H3N2 (X-31)’s fusion kinetics to either SLB type, suggesting that reduced receptor binding does not affect subsequent membrane fusion. This finding suggests that for this virus to adapt to host cells rich in Neu5Gc, only receptor affinity changes are required without further adaptation of virus fusion machinery. IMPORTANCE Influenza A virus (IAV) infections continue to threaten human health, causing over 300,000 deaths yearly. IAV infection is initiated by the binding of influenza glycoprotein hemagglutinin (HA) to host cell sialic acids (Sias) and the subsequent viral-host membrane fusion. Generally, human IAVs preferentially bind to the Sia N-acetylneuraminic acid (Neu5Ac). Yet, other mammalian hosts, including pigs, express diverse nonhuman Sias, including N-glycolylneuraminic acid (Neu5Gc). The role of Neu5Gc in human IAV infections in those hosts is not well-understood, and the variant form may play a role in incidents of cross-species transmission and emergence of new epidemic variants. Therefore, it is important to investigate how human IAVs interact with Neu5Ac and Neu5Gc. Here, we use membrane platforms that mimic the host cell surface to examine receptor binding and membrane fusion events of human IAV H3N2. Our findings improve the understanding of viral entry mechanisms that can affect host tropism and virus evolution.
Measurement of Spatial Resolution in Fiber-optic Image Guides

Common methods of determining the spatial resolution of fiber-optic image guides are by measuring the diameter of individual microfibers or by the use of a resolution test target. However these methods cannot provide enough information of spatial resolution in ultrathin fiber-optic image guides. In this study, a simple method to measure the modulation transfer function (MTF) of an mage guide was developed. The MTFs of ultrathin image guides with 3 and 4 ${\mu}{\textrm}{m}$ Um diameter were measured by examining transmitted sharp edge image. This method should be especially useful for measuring spatial resolution of ultrahigh resolution image guides with less than 5 ${\mu}{\textrm}{m}$ diameter microfibers because their spatial resolution cannot be determined by individual microfiber diameter due to crosstalk and leaky ray phenomena.
Design, Optimization, and Reverse Engineering of Laser Systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor and additive manufacturing, communication networks, biology and medicine, defense and national security, and a host of other sectors over the past decades. Accurately and efficiently modeling CPA and NLO-based laser systems is challenging because of the multitude of coupled linear and nonlinear processes and high variability in modeling frameworks. The lack of fully-integrated modeling tools severely hampers further advances in tailoring existing or materializing new CPA+NLO systems. Emerging optimization and inverse design approaches, data-driven with machine learning methods, will require these fully-integrated modeling tools to meet the big data demand. Here, we present a modular start-to-end software model encompassing an array of amplifier designs and nonlinear optics techniques. The model renders time- and frequency-resolved electromagnetic fields alongside essential physical characteristics of energy, fluence, and spectral distribution. To demonstrate its robustness and real-world applicability–specifically, reverse engineering, system optimization, and inverse design–we present a case study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.
Evolution of SLAM: Toward the Robust-Perception of Autonomy

Simultaneous localisation and mapping (SLAM) is the problem of autonomous robots to construct or update a map of an undetermined unstructured environment while simultaneously estimate the pose in it. The current trend towards self-driving vehicles has influenced the development of robust SLAM techniques over the last 30 years. This problem is addressed by using a standard sensor or a sensor array (Ultrasonic sensor, LIDAR, Camera, Kinect RGB-D) with sensor fusion techniques to achieve the perception step. Sensing method is determined by considering the specifications of the environment to extract the features. Then the usage of classical Filter-based approaches, the global optimisation approach which is a popular method for visual-based SLAM and convolutional neural network-based methods such as deep learning-based SLAM are discussed whereas considering how to overcome the localisation and mapping issues. The robustness and scalability in long-term autonomy, performance and other new directions in the algorithms compared with each other to sort out. This paper is looking at the published previous work with a judgemental perspective from sensors to algorithm development while discussing open challenges and new research frontiers.
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

In this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input of case facts and convention articles. This configuration facilitates the model learning some legal reasoning ability in mapping article text to specific case fact text. It also provides an opportunity to evaluate the model’s ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot experiments and apply domain adaptation methods based on domain discrimination and Wasserstein distance. Our results demonstrate that the article-aware architecture outperforms straightforward fact classification. We also find that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training influencing the effect.
Transfer Learning for Bayesian Optimization: A Survey

A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization. Bayesian optimization (BO) is a powerful tool that models and optimizes such expensive"black-box"functions. However, at the beginning of optimization, vanilla Bayesian optimization methods often suffer from slow convergence issue due to inaccurate modeling based on few trials. To address this issue, researchers in the BO community propose to incorporate the spirit of transfer learning to accelerate optimization process, which could borrow strength from the past tasks (source tasks) to accelerate the current optimization problem (target task). This survey paper first summarizes transfer learning methods for Bayesian optimization from four perspectives: initial points design, search space design, surrogate model, and acquisition function. Then it highlights its methodological aspects and technical details for each approach. Finally, it showcases a wide range of applications and proposes promising future directions.
Topology of the landscape and dominant kinetic path for the thermodynamic phase transition of the charged Gauss-Bonnet-AdS black holes

We study the generalized free energy of the five dimensional charged Gauss-Bonnet AdS black holes in the grand canonical ensemble by treating the black hole radius and the charge as the order parameters. On the two dimensional free energy landscape, the lowest points in the basins represent the local stable black holes and the saddle point represents the unstable black hole. We show that black hole is the topological defect of gradient field of the landscape. The black hole stability is determined by the topography of the free energy landscape in terms of the basin depths and the barrier height between the basins and is not by the topology of the gradient field. In addition, we study the stochastic dynamics of the black hole phase transition and obtain the dominant kinetic path for the transition on the free energy landscape. Unlike the one dimensional landscape, the dominant kinetic path between the small and the large black hole state does not necessarily pass through the intermediate black hole state. Furthermore, the inhomogeneity in diffusions can lead to the switching from the coupled cooperative process of black hole phase transition to the decoupled sequential process, giving different kinetic mechanisms.
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation

This paper proposes Contour Context, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban autonomous driving scenario. We interpret the Cartesian bird's eye view (BEV) image projected from 3D LiDAR points as layered distribution of structures. To recover elevation information from BEVs, we slice them at different heights, and connected pixels at each level form contours. Each contour is parameterized by abstract information, e.g., pixel count, center position, covariance, and mean height. The similarity of two BEVs is calculated in sequential discrete and continuous steps. The first step considers the geometric consensus of graph-like constellations formed by contours in particular localities. The second step models the majority of contours as a 2.5D Gaussian mixture model, which is used to calculate correlation and optimize relative transform in continuous space. A retrieval key is designed to accelerate the search of a database indexed by layered KD-trees. We validate the efficacy of our method by comparing it with recent works on public datasets.
Online Recommendations for Agents with Discounted Adaptive Preferences

We consider a bandit recommendations problem in which an agent's preferences (representing selection probabilities over recommended items) evolve as a function of past selections, according to an unknown $\textit{preference model}$. In each round, we show a menu of $k$ items (out of $n$ total) to the agent, who then chooses a single item, and we aim to minimize regret with respect to some $\textit{target set}$ (a subset of the item simplex) for adversarial losses over the agent's choices. Extending the setting from Agarwal and Brown (2022), where uniform-memory agents were considered, here we allow for non-uniform memory in which a discount factor is applied to the agent's memory vector at each subsequent round. In the"long-term memory"regime (when the effective memory horizon scales with $T$ sublinearly), we show that efficient sublinear regret is obtainable with respect to the set of $\textit{everywhere instantaneously realizable distributions}$ (the"EIRD set", as formulated in prior work) for any $\textit{smooth}$ preference model. Further, for preferences which are bounded above and below by linear functions of memory weight (we call these"scale-bounded"preferences) we give an algorithm which obtains efficient sublinear regret with respect to nearly the $\textit{entire}$ item simplex. We show an NP-hardness result for expanding to targets beyond EIRD in general. In the"short-term memory"regime (when the memory horizon is constant), we show that scale-bounded preferences again enable efficient sublinear regret for nearly the entire simplex even without smoothness if losses do not change too frequently, yet we show an information-theoretic barrier for competing against the EIRD set under arbitrary smooth preference models even when losses are constant.
Nonsingular black holes from conformal symmetries

We derive the form of the metric for static, nonsingular black holes with a de Sitter core, representing a deformation of the Schwarzschild solution, by assuming that the gravitational sources describe a flow between two conformal points, at small and great distances. The resulting black-hole metric turns out to be a particular case of the Fan & Wang metric, whose parameters have been recently constrained by using the data of the S2 star orbits around the galactic center SgrA ∗ .
Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, polynomial worst-case complexity makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.
Low-Rank Tensor Completion With Generalized CP Decomposition and Nonnegative Integer Tensor Completion

—Tensor completion is important to many areas such as computer vision, data analysis, and signal processing. Previously, a category of methods known as low-rank tensor completion has been proposed and developed, involving the enforcement of low-rank structures on completed tensors. While such methods have been constantly improved, none considered exploiting the numerical properties of tensor elements. This work attempts to construct a new methodological framework called GCDTC (Generalized CP Decomposition Tensor Completion) based on numerical properties to achieve higher accuracy in tensor completion. In this newly introduced framework, a generalized form of the CP Decomposition is applied to low-rank tensor completion. This paper also proposes an algorithm known as SPTC (Smooth Poisson Tensor Completion) for nonnegative integer tensor completion as an application of the GCDTC framework. Through experimentation with real-life data, it is veriﬁed that this method could produce results superior in completion accuracy to current state-of-the-art methodologies.