Miracle-dz/UniFAR-FTUs huggingface dataset:
Making BEASTies: dynamical formation of planetary systems around massive stars

Exoplanets display incredible diversity, from planetary system architectures around Sun-like stars that are very different from our Solar system, to planets orbiting post-main-sequence stars or stellar remnants. Recently, the B-star Exoplanet Abundance STudy (BEAST) reported the discovery of at least two super-Jovian planets orbiting massive stars in the Sco Cen OB association. Whilst such massive stars do have Keplerian discs, it is hard to envisage gas giant planets being able to form in such hostile environments. We use N-body simulations of star-forming regions to show that these systems can instead form from the capture of a free-floating planet or the direct theft of a planet from one star to another, more massive star. We find that this occurs on average once in the first 10 Myr of an association’s evolution, and that the semimajor axes of the hitherto confirmed BEAST planets (290 and 556 au) are more consistent with capture than theft. Our results lend further credence to the notion that planets on more distant (>100 au) orbits may not be orbiting their parent star.
Rydberg-dressed spin-1/2 Fermi gases in one dimension

The emergent phases of strongly correlated spin-1/2 Fermi gases of Rydberg dressed atoms in a one dimensional optical lattice are theoretically investigated. At weak coupling a bosonization description is used to demonstrate the ability to drive alternating quantum phase transitions between distinct Luttinger liquids. At strong coupling the ground state develops non-trivial phase separation exhibiting Luttinger liquid ''puddles'' separated by magnetic domain walls due to the interplay of the incommensurate filling and the Rydberg core length scale. These phases can be detected in ultracold gases of Rydberg atoms made from $^6$Li.
Mid-Infrared Photothermal–Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput are necessary to answer the question of “who eats what, when, and where” in complex microbial communities. Here, we present a mid-infrared photothermal–fluorescence in situ hybridization (MIP–FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically labeled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labeling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of 13C atoms originating from 13C-labeled glucose can be exploited by MIP–FISH to discriminate and identify 13C-labeled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure–function analyses for microbiology.
VizBelle: A Design Space of Embellishments for Data Visualization

Visual embellishments, as a form of non-linguistic rhetorical figures, are used to help convey abstract concepts or attract readers' attention. Creating data visualizations with appropriate and visually pleasing embellishments is challenging since this process largely depends on the experience and the aesthetic taste of designers. To help facilitate designers in the ideation and creation process, we propose a design space, VizBelle, based on the analysis of 361 classified visualizations from online sources. VizBelle consists of four dimensions, namely, communication goal to fit user intention, object to select the target area, strategy and technique to offer potential approaches. We further provide a website to present detailed explanations and examples of various techniques. We conducted a within-subject study with 20 professional and amateur design enthusiasts to evaluate the effectiveness of our design space. Results show that our design space is illuminating and useful for designers to create data visualizations with embellishments.
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization

The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, does that mean extractive is equal to faithful? Turns out that the answer is no. In this work, we define a typology with five types of broad unfaithfulness problems (including and beyond not-entailment) that can appear in extractive summaries, including incorrect coreference, incomplete coreference, incorrect discourse, incomplete discourse, as well as other misleading information. We ask humans to label these problems out of 1600 English summaries produced by 16 diverse extractive systems. We find that 30% of the summaries have at least one of the five issues. To automatically detect these problems, we find that 5 existing faithfulness evaluation metrics for summarization have poor correlations with human judgment. To remedy this, we propose a new metric, ExtEval, that is designed for detecting unfaithful extractive summaries and is shown to have the best performance. We hope our work can increase the awareness of unfaithfulness problems in extractive summarization and help future work to evaluate and resolve these issues.
A brief introduction to giant magnetoresistance

Giant magnetoresistance (GMR) is a quantum mechanical magnetoresistance effect observed in thin film structures composed of alternating ferromagnetic and nonmagnetic layers. The effect manifests itself as a significant decrease (typically 10-80%) in electrical resistance in the presence of a magnetic field. The effect is exploited commercially by manufacturers of hard disk drives. The 2007 Nobel Prize in physics was awarded to Albert Fert and Peter Grunberg for the discovery of GMR.
Improved Sensor-Based Animal Behavior Classification Performance through Conditional Generative Adversarial Network

Many activity classifications segments data into fixed window size for feature extraction and classification. However, animal behaviors have various durations that do not match the predetermined window size. The dense labeling and dense prediction methods address this limitation by predicting labels for every point. Thus, by tracing the starting and ending points, we could know the time location and duration of all occurring activities. Still, the dense prediction could be noisy with misalignments problems. We modified the U-Net and Conditional Generative Adversarial Network (cGAN) with customized loss functions as a training strategy to reduce fragmentation and other misalignments. In cGAN, the discriminator and generator trained against each other like an adversarial competition. The generator produces dense predictions. The discriminator works as a high-level consistency check, in our case, pushing the generator to predict activities with reasonable duration. The model trained with cGAN shows better or comparable performance in the cow, pig, and UCI HAPT dataset. The cGAN-trained modified U-Net improved from 92.17% to 94.66% for the UCI HAPT dataset and from 90.85% to 93.18% for pig data compared to previous dense prediction work.
A Unified Complexity Metric for Nonconvex Rank-one Generalized Matrix Completion

In this work, we develop a new complexity metric for an important class of low-rank matrix optimization problems in both symmetric and asymmetric cases, where the metric aims to quantify the complexity of the nonconvex optimization landscape of each problem and the success of local search methods in solving the problem. The existing literature has focused on two complexity measures. The RIP constant is commonly used to characterize the complexity of matrix sensing problems. On the other hand, the incoherence and the sampling rate are used when analyzing matrix completion problems. The proposed complexity metric has the potential to unify these two notions and also applies to a much larger class of problems. To mathematically study the properties of this metric, we focus on the rank-1 generalized matrix completion problem and illustrate the usefulness of the new complexity metric on three types of instances, namely, instances with the RIP condition, instances obeying We note that a similar complexity metric based on a special case of instances in Section 3.3 was proposed in our conference paper [61]. However, the complexity metric in this work has a different form and is proved to work on a broader set of applications. In addition, we prove several theoretical properties of the metric in this work, which are not included in [61]. Corresponding Author: Javad Lavaei Affiliation: Department of Industrial Engineering and Operations Research, University of California, Berkeley E-mail: lavaei@berkeley.edu Haixiang Zhang Department of Mathematics, University of California, Berkeley, CA E-mail: haixiang zhang@berkeley.edu Baturalp Yalcin Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA E-mail: baturalp yalcin@berkeley.edu Javad Lavaei Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA E-mail: lavaei@berkeley.edu Somayeh Sojoudi Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA E-mail: sojoudi@berkeley.edu ar X iv :2 20 4. 02 36 4v 2 [ m at h. O C ] 8 S ep 2 02 2 2 Haixiang Zhang et al. the Bernoulli sampling model, and a synthetic example. We show that the complexity metric exhibits a consistent behavior in the three cases, even when other existing conditions fail to provide theoretical guarantees. These observations provide a strong implication that the new complexity metric has the potential to unify various notions of optimization complexity proposed for different applications. Furthermore, we establish theoretical results to provide sufficient conditions and necessary conditions on the existence of spurious solutions in terms of the proposed complexity metric. This contrasts with the RIP and incoherence conditions that fail to provide any necessary condition.
Evaluating Temporal Patterns in Applied Infant Affect Recognition

Agents must monitor their partners' affective states continuously in order to understand and engage in social interactions. However, methods for evaluating affect recognition do not account for changes in classification performance that may occur during occlusions or transitions between affective states. This paper addresses temporal patterns in affect classification performance in the context of an infant-robot interaction, where infants' affective states contribute to their ability to participate in a therapeutic leg movement activity. To support robustness to facial occlusions in video recordings, we trained infant affect recognition classifiers using both facial and body features. Next, we conducted an in-depth analysis of our best-performing models to evaluate how performance changed over time as the models encountered missing data and changing infant affect. During time windows when features were extracted with high confidence, a unimodal model trained on facial features achieved the same optimal performance as multimodal models trained on both facial and body features. However, multimodal models outperformed unimodal models when evaluated on the entire dataset. Additionally, model performance was weakest when predicting an affective state transition and improved after multiple predictions of the same affective state. These findings emphasize the benefits of incorporating body features in continuous affect recognition for infants. Our work highlights the importance of evaluating variability in model performance both over time and in the presence of missing data when applying affect recognition to social interactions.
Data Management Challenges for Internet-scale 3D Search Engines

This paper describes the most significant data-related challenges involved in building internet-scale 3D search engines. The discussion centers on the most pressing data management issues in this domain, including model acquisition, support for multiple file formats, asset versioning, data integrity errors, the data lifecycle, intellectual property, and the legality of web crawling. The paper also discusses numerous issues that fall under the rubric of trustworthy computing, including privacy, security, inappropriate content, and copying/remixing of assets. The goal of the paper is to provide an overview of these general issues, illustrated by empirical data drawn from the internet's largest operational search engine. While numerous works have been published on 3D information retrieval, this paper is the first to discuss the real-world challenges that arise in building practical search engines at scale.
Bullying and Work-Related Stress in the Irish Workplace

Work-related stress is increasing in prevalence, with important consequences for employees, employers, the economy, and wider society. While previous research has identified a link between work-related stress and bullying, gaps remain in our understanding of the nature of the relationship. This article uses ordered logistic regression and nationally representative data on 5110 employees from Ireland to empirically analyse the distribution of subjective work-related stress and its relationship with bullying (self-reported). We also consider the role and importance of gender and the presence of a formal policy on respect and dignity at work, as well as the degree to which relationships between management and staff and between staff themselves are related to work-related stress. Amongst the main findings are that employees who reported that they were bullied were considerably more likely to report that they were often or always stressed, while bad and very bad relationships between management and staff were also significantly associated with greater stress, particularly for female employees. Overall, our findings have a range of implications for employees, employers, and policymakers.
Peer to Peer Learning Platform Optimized With Machine Learning

HELM Learning (Helping Everyone Learn More) is the first online peer-to-peer learning platform which allows students (typically middle-to-high school students) to teach classes and students (typically elementary-to-middle school students) to learn from classes for free. This method of class structure (peer-to-peer learning) has been proven effective for learning, as it promotes teamwork and collaboration, and enables active learning. HELM is a unique platform as it provides an easy process for students to create, teach and learn topics in a structured, peer-to-peer environment. Since HELM was created in April 2020, it has gotten over 4000 student sign ups and 80 teachers, in 4 continents around the world. HELM has grown from a simple website-and-Google-Form platform to having a backend system coded with Python, SQL, JavaScript and HTML, hosted on an AWS service. This not only makes it easier for students to sign up (as the students' information is saved in an SQL database, meaning they can sign up for classes without having to put in their information again, as well as getting automated emails about their classes), but also makes it easier for teachers to teach (as supplemental processes such as creating Zoom links, class recording folders, sending emails to students, etc. are done automatically). In addition, HELM has a recommendation machine learning algorithm which suggests classes and subjects students would enjoy taking, based on the previous classes a student has taken. This has created an easier experience for students to sign up for classes they are interested in.
Relationship between multimorbidity and composite lifestyle status in Shenzhen, China

Objective The study aimed to understand multimorbidity among adults in a high-tech city in southern coastal China that has undergone rapid economic development and to investigate its relationship with lifestyle status. Methods A population-based survey was conducted among 24 community centers in the Nanshan District of Shenzhen from February to December of 2018. Participants were recruited using a stratified random sampling approach. A self-administered questionnaire on typical chronic diseases, lifestyle factors, body composition, and social demographics was used to collect data. Multimorbidity was defined as two or more chronic diseases coexisting in a single person. An algorithm on body mass index, physical activity, drinking, smoking, and sleep quality was used to calculate lifestyle scores (0–9), with higher scores predicting a healthier lifestyle. Results A total of 2,905 participants were included in the analysis, with men accounting for 52.4%, and single for 25%. The prevalence of multimorbidity was 4.8%, and the mean lifestyle score was 4.79 ± 1.55. People who were old, retired, married, and had less education were more likely to have multimorbidity (all P < .05). A higher prevalence of multimorbidity was found among those who were obese, less engaged in physical activity, consumed more alcohol, and had poorer sleep quality (all p < .05). After adjusting for age, employment, education, and marital status, one unit increase in lifestyle score was associated with 0.74 times lower to have multimorbidity (OR: 0.74; 95% CI: 0.63–0.87, p < .05). Conclusion The prevalence of multimorbidity was relatively low in economically developed Shenzhen. Keeping a healthy lifestyle was related to the lower possibility of suffering from multiple chronic diseases.
Electronic features around Fermi level correlating to occurrence of magnetism or superconductivity in Laves-phase intermetallic compounds

Based on density functional calculations, the relationship between magnetism or superconductivity and electronic states around Fermi level were derived, and the location of the Fermi level in nonmagnetic "form" of Laves-phase compounds is very sensitive to determine the presence of nonmagnetism, or ferromagnetism, or antiferromagnetism, or superconductivity. The Fermi level at the nonbonding and antibonding regions corresponds for the nonmagnetism and magnetism, respectively, whereas at bonding region with responsibility for superconductivity. This rules will be very useful and convenient to search the new magnetic and superconductive materials.
Are Self-disorders in Schizophrenia Expressive of a Unifying Disturbance of Subjectivity: A Factor Analytic Approach

Abstract Background and Hypothesis The idea that a disorder of the basic self is a central feature in schizophrenia has recently been corroborated in a meta-analysis and a systematic review. Manifestations of the self-disorder can be systematically explored with the Examination of Anomalous Self-Experience (EASE). In this study, we examined the factorial structure of EASE, and diagnostic efficacy of EASE. We hypothesized that EASE will have a monofactorial structure as an instability of the basic self will result in multiple deformations of self-experience which would be meaningfully interrelated as aspects of a unifying Gestalt. Design EASE data for 226 patients suffering from various mental disorders were analyzed under a confirmatory factor analysis framework (CFA). Area under the receiver operating characteristic curve (AUC) was calculated for the total EASE sums, and sensitivity and specificity values for prediction of schizophrenia spectrum disorders based on different cut-offs were obtained. Results Fit indices for the CFA model: RMSEA = 0.036, SRMR = 0.100, CFI = 0.983, TLI = 0.981. The AUC value was 0.946 (95% confidence interval: 0.919–0.974). Sensitivity as well as specificity for schizophrenia spectrum disorders were high. Conclusion Our results lend support for EASE exhibiting a monofactorial structure and the notion of self-disorders as a central phenotypic feature of schizophrenia spectrum disorders.
Acute effects of euglycemic‐hyperinsulinemia on myocardial contractility in male mice

Type 2 diabetes and obesity are associated with increased risk of cardiovascular disease, including heart failure. A hallmark of these dysmetabolic states is hyperinsulinemia and decreased cardiac reserve. However, the direct effects of hyperinsulinemia on myocardial function are incompletely understood. In this study, using invasive hemodynamics in mice, we studied the effects of short‐term euglycemic hyperinsulinemia on basal myocardial function and subsequent responses of the myocardium to β‐adrenergic stimulation. We found that cardiac function as measured by left ventricular (LV) invasive hemodynamics is not influenced by acute exposure to hyperinsulinemia, induced by an intravenous insulin injection with concurrent inotropic stimulation induced by β‐adrenergic stimulation secondary to isoproterenol administration. When animals were exposed to 120‐min of hyperinsulinemia by euglycemic‐hyperinsulinemic clamps, there was a significant decrease in LV developed pressure, perhaps secondary to the systemic vasodilatory effects of insulin. Despite the baseline reduction, the contractile response to β‐adrenergic stimulation remained intact in animals subject to euglycemic hyperinsulinemic clamps. β‐adrenergic activation of phospholamban phosphorylation was not impaired by hyperinsulinemia. These results suggest that short‐term hyperinsulinemia does not impair cardiac inotropic response to β‐adrenergic stimulation in vivo.
On the Pursuit of Generalizations for the Petrov Classification and the Goldberg-Sachs Theorem

The Petrov classification is an important algebraic classification for the Weyl tensor valid in 4-dimensional space-times. In this thesis such classification is generalized to manifolds of arbitrary dimension and signature. This is accomplished by interpreting the Weyl tensor as a linear operator on the bundle of p-forms, for any p, and computing the Jordan canonical form of this operator. Throughout this work the spaces are assumed to be complexified, so that different signatures correspond to different reality conditions, providing a unified treatment. A higher-dimensional generalization of the so-called self-dual manifolds is also investigated. The most important result related to the Petrov classification is the Goldberg-Sachs theorem. Here are presented two partial generalizations of such theorem valid in even-dimensional manifolds. One of these generalizations states that certain algebraic constraints on the Weyl operator imply the existence of an integrable maximally isotropic distribution. The other version of the generalized Goldberg-Sachs theorem states that these algebraic constraints imply the existence of a null congruence whose optical scalars obey special restrictions. On the pursuit of these results the spinorial formalism in 6 dimensions was developed from the very beginning, using group representation theory. Since the spinors are full of geometric significance and are suitable tools to deal with isotropic structures, it should not come as a surprise that they provide a fruitful framework to investigate the issues treated on this thesis. In particular, the generalizations of the Goldberg-Sachs theorem acquire an elegant form in terms of the pure spinors.