Miracle-dz/UniFAR-FTUs huggingface dataset:
On the extreme value of Nehari manifold for nonlocal singular Schr{\"o}dinger-Kirchhoff equations in $\mathbb{R}^N$

This article investigates the existence, non-existence, and multiplicity of weak solutions for a parameter-dependent nonlocal Schr\"odinger-Kirchhoff type problem on $\mathbb R^N$ involving singular non-linearity. By performing fine analysis based on Nehari submanifolds and fibre maps, our goal is to show the problem has at least two positive solutions even if $\lambda$ lies beyond the extremal parameter $\lambda_\ast$.
Human Resource Strategy: Formulation, Implementation, and Impact

Peter Bamberger and Ilan Meshoulam's new book, “Formulation, Implementation, and Impact,” presents the best attempt available to provide an overarching framework for integrating the large and varied body of research relating to the links between strategy and human resource management (HRM). Their goal, it seems, is not only to review this substantial literature but also to provide a model of HR strategy that enables researchers to see both the consistencies and inconsistencies that exist within this literature. While not perfect, this attempt goes far beyond any other in both the breadth of literature covered and in the authors' ability to integrate what on the surface appears to be a mishmash of empirical and theoretical efforts related only by their use of the term strategic HRM.
On the first Steklov-Dirichlet eigenvalue on eccentric annuli in general dimensions

We consider the Steklov-Dirichlet eigenvalue problem on eccentric annuli in Euclidean space of general dimensions. In recent work by the same authors of this paper [21], a limiting behavior of the first eigenvalue, as the distance between the two boundary circles of an annulus approaches zero, was obtained in two dimensions. We extend this limiting behavior to general dimensions by employing bispherical coordinates and expressing the first eigenfunction as a Fourier-Gegenbauer series.
Optimal left ventricular ejection fraction in risk stratification of patients with cardiac sarcoidosis

Abstract Aims Identifying patients with cardiac sarcoidosis (CS) who are at an increased risk of sudden cardiac death (SCD) poses a clinical challenge. We sought to identify the optimal cutoff for left ventricular ejection fraction (LVEF) in predicting ventricular arrhythmia (VA) and all-cause mortality and to identify clinical and imaging risk factors in patients with known CS. Methods and results This retrospective cohort included 273 patients with well-established CS. The primary endpoint was a composite of VA and all-cause mortality. A modified receiver operating curve analysis was utilized to identify the optimal cutoff for LVEF in predicting the primary composite endpoint. Cox proportional hazard regression analysis was used to identify independent risk factors of the outcomes. At median follow-up of 7.9 years, the rate of the primary endpoint was 38% (83 VAs and 32 all-cause deaths). The 5-year overall survival rate was 97%. The optimal cutoff LVEF for the primary composite endpoint was 42% in the entire cohort and in subjects without a history of VA. Younger age, history of VA, lower LVEF, and any presence of scar by cardiac magnetic resonance (CMR) imaging and/or positron emission tomography (PET) were found to be independent risk factors for the primary endpoint and for VA, whereas lower LVEF, baseline NT-proBNP, and any presence of scar were independent risk factor of all-cause mortality. Conclusion Among patients with CS, a mild reduction in LVEF of 42% was identified as the optimal cutoff for predicting VA and all-cause mortality. Prior VA and scar by CMR or PET are strong risk factors for future VA and all-cause mortality.
Disassembling one-dimensional chains in molybdenum oxides

The dimensionality of quantum materials strongly affects their physical properties. Although many emergent phenomena, such as charge-density wave and Luttinger liquid behavior, are well understood in one-dimensional (1D) systems, the generalization to explore them in higher dimensional systems is still a challenging task. In this study, we aim to bridge this gap by systematically investigating the crystal and electronic structures of molybdenum-oxide family compounds, where the contexture of 1D chains facilitates rich emergent properties. While the quasi-1D chains in these materials share general similarities, such as the motifs made up of MoO6 octahedrons, they exhibit vast complexity and remarkable tunability. We disassemble the 1D chains in molybdenum oxides with different dimensions and construct effective models to excellently fit their low-energy electronic structures obtained by ab initio calculations. Furthermore, we discuss the implications of such chains on other physical properties of the materials and the practical significance of the effective models. Our work establishes the molybdenum oxides as simple and tunable model systems for studying and manipulating the dimensionality in quantum systems.
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition

Generalized zero-shot skeleton-based action recognition (GZSSAR) is a new challenging problem in computer vision community, which requires models to recognize actions without any training samples. Previous studies only utilize the action labels of verb phrases as the semantic prototypes for learning the mapping from skeleton-based actions to a shared semantic space. However, the limited semantic information of action labels restricts the generalization ability of skeleton features for recognizing unseen actions. In order to solve this dilemma, we propose a multi-semantic fusion (MSF) model for improving the performance of GZSSAR, where two kinds of class-level textual descriptions (i.e., action descriptions and motion descriptions), are collected as auxiliary semantic information to enhance the learning efficacy of generalizable skeleton features. Specially, a pre-trained language encoder takes the action descriptions, motion descriptions and original class labels as inputs to obtain rich semantic features for each action class, while a skeleton encoder is implemented to extract skeleton features. Then, a variational autoencoder (VAE) based generative module is performed to learn a cross-modal alignment between skeleton and semantic features. Finally, a classification module is built to recognize the action categories of input samples, where a seen-unseen classification gate is adopted to predict whether the sample comes from seen action classes or not in GZSSAR. The superior performance in comparisons with previous models validates the effectiveness of the proposed MSF model on GZSSAR.
Gradient estimates of the heat kernel for random walks among time-dependent random conductances

In this paper we consider a time-continuous random walk in $\mathbb{Z}^d$ in a dynamical random environment with symmetric jump rates to nearest neighbours. We assume that these random conductances are stationary and ergodic and, moreover, that they are bounded from below but unbounded from above with finite first moment. We derive sharp on-diagonal estimates for the annealed first and second discrete space derivative of the heat kernel which then yield local limit theorems for the corresponding kernels. Assuming weak algebraic off-diagonal estimates, we then extend these results to the annealed Green function and its first and second derivative. Our proof which extends the result of Delmotte and Deuschel (2005) to unbounded conductances with first moment only, is an adaptation of the recent entropy method of Benjamini et. al. (2015).
A Discussion on Generalization in Next-Activity Prediction

Next activity prediction aims to forecast the future behavior of running process instances. Recent publications in this field predominantly employ deep learning techniques and evaluate their prediction performance using publicly available event logs. This paper presents empirical evidence that calls into question the effectiveness of these current evaluation approaches. We show that there is an enormous amount of example leakage in all of the commonly used event logs, so that rather trivial prediction approaches perform almost as well as ones that leverage deep learning. We further argue that designing robust evaluations requires a more profound conceptual engagement with the topic of next-activity prediction, and specifically with the notion of generalization to new data. To this end, we present various prediction scenarios that necessitate different types of generalization to guide future research.
Projection par plasma d'arc et plasma inductif de dépots céramiques : nécanismes de formation de la première couche et relation avec les propriétés mécaniques des dépots

Ce travail est consacre a l'etude de la generation des premieres couches de depots ceramiques realisees par projection plasma sur des substrats en acier. Une synthese bibliographique traite de l'etat actuel des connaissances sur le mode de generation des depots, tant d'un point de vue microscopique que macroscopique. La demarche experimentale consiste a faire varier la temperature du substrat et la vitesse d'impact des particules de facon independante, sans agir sur les autres parametres d'etude. Aussi, un appareillage specifique de projection a du etre developpe afin d'etudier l'ecrasement des particules. L'utilisation d'une torche a plasma inductif et de deux torches a plasma d'arc a permis de faire varier la vitesse des particules de 50 a 250 m/s. Une etude microscopique des particules etalees met clairement en evidence l'influence de la temperature du substrat (t#s) sur l'etalement des particules. Lorsque cette temperature est inferieure a 100c, les particules recueillies sur un substrat lisse sont tres dechiquetees et presentent un contact limite avec le substrat. Par contre, des lors que t#s depasse 150c, les particules deviennent circulaires et adherent fortement au substrat. La transition morphologique, observee lorsque t#s augmente, a pu etre attribuee a un blocage de l'ecoulement de la particule liquide, lorsque sa vitesse de refroidissement est suffisamment rapide, par un debut de solidification a l'interface. L'adherence des depots peut ainsi etre multipliee par plus de quatre par un prechauffage du substrat, confirmant les resultats de l'etude microscopique des lamelles
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification

The domain shift between training and testing data presents a significant challenge for training generalizable deep learning models. As a consequence, the performance of models trained with the independent and identically distributed (i.i.d) assumption deteriorates when deployed in the real world. This problem is exacerbated in the medical imaging context due to variations in data acquisition across clinical centers, medical apparatus, and patients. Domain generalization (DG) aims to address this problem by learning a model that generalizes well to any unseen target domain. Many domain generalization techniques were unsuccessful in learning domain-invariant representations due to the large domain shift. Furthermore, multiple tasks in medical imaging are not yet extensively studied in existing literature when it comes to DG point of view. In this paper, we introduce a DG method that re-establishes the model objective function as a maximization of mutual information with a large pretrained model to the medical imaging field. We re-visit the problem of DG in Diabetic Retinopathy (DR) classification to establish a clear benchmark with a correct model selection strategy and to achieve robust domain-invariant representation for an improved generalization. Moreover, we conduct extensive experiments on public datasets to show that our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation. Source code available at https://github.com/BioMedIA-MBZUAI/DGM-DR
The Rentier State

Introduction Hazem Beblawi and Giacomo Luciani. 1. Income Measurement in Arab States Thomas Stauffer 2. The Rentier State in the Arab World Hazem Beblawi 3. Allocation vs. Production States: A Theoretical Framework Giacomo Luciani 4. The Macro-behaviour of Oil-Rentier States in the Arab Region Mahmoud Abdel-Fadil 5. Policies for Development: Attitudes Toward Industry and Services Michel Chatelus 6. The State, Social Classes and Agricultural Policies in the Arab World Hamid Ait Amara 7. Political Aspects of State Building in Rentier Economies: Algeria and Libya Compared Dirk Vandewalle 8. Fiscal Resources and Budget Financing in the Countries of the Maghreb (Algeria, Morocco, Tunisia, Libya and Mauritania) Fathallah Oulalou and Larbi Jaidi 9. Government Income Sources and the Development of the Taxation System: the Case of Jordan, Egypt and Kuwait Hesham Garaibeh 10. Depoliticisation of a Rentier State: the Case of Pahlavi Iran Afsaneh Najmabadi
Quantum Computed Green's Functions using a Cumulant Expansion of the Lanczos Method

In this paper, we present a quantum computational method to calculate the many-body Green's function matrix in a spin orbital basis. We apply our approach to finite-sized fermionic Hubbard models and related impurity models within Dynamical Mean Field Theory, and demonstrate the calculation of Green's functions on Quantinuum's H1-1 trapped-ion quantum computer. Our approach involves a cumulant expansion of the Lanczos method, using Hamiltonian moments as measurable expectation values. This bypasses the need for a large overhead in the number of measurements due to repeated applications of the variational quantum eigensolver (VQE), and instead measures the expectation value of the moments with one set of measurement circuits. From the measured moments, the tridiagonalised Hamiltonian matrix can be computed, which in turn yields the Green's function via continued fractions. While we use a variational algorithm to prepare the ground state in this work, we note that the modularity of our implementation allows for other (non-variational) approaches to be used for the ground state.
Stationary particle currents in sedimenting active matter wetting a wall.

Recently it was predicted, on the basis of a lattice gas model, that scalar active matter in a gravitational field would rise against gravity up a confining wall or inside a thin capillary-in spite of repulsive particle-wall interactions [Phys. Rev. Lett. 124, 048001 (2020)0031-900710.1103/PhysRevLett.124. 048001]. In this paper we confirm this prediction with sedimenting active Brownian particles (ABPs) in a box numerically and elucidate the mechanism leading to the formation of a meniscus rising above the bulk of the sedimentation region. The height of the meniscus increases with the activity of the system, algebraically with the Péclet number. The formation of the meniscus is determined by a stationary circular particle current, a vortex, centered at the base of the meniscus, whose size and strength increase with the ABP activity. The origin of these vortices can be traced back to the confinement of the ABPs in a box: already the stationary state of ideal (noninteracting) ABPs without gravitation displays circular currents that arrange in a highly symmetric way in the eight octants of the box. Gravitation distorts this vortex configuration downward, leaving two major vortices at the two side walls, with a strong downward flow along the walls. Repulsive interactions between the ABPs change this situation only as soon as motility induced phase separation (MIPS) sets in and forms a dense, sedimented liquid region at the bottom, which pushes the center of the vortex upwards towards the liquid-gas interface. Self-propelled particles therefore represent an impressive realization of scalar active matter that forms stationary particle currents being able to perform visible work against gravity or any other external field, which we predict to be observable experimentally in active colloids under gravitation.
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets

Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior research has independently explored framing within news media and by social media users, there remains a notable gap in our comprehension of the disparities in framing political issues between these two distinct groups. To address this gap, we conduct a comprehensive investigation, focusing on the nuanced distinctions both qualitatively and quantitatively in the framing of social media and traditional media outlets concerning a series of American Supreme Court rulings on affirmative action, student loans, and abortion rights. Our findings reveal that, while some overlap in framing exists between social media and traditional media outlets, substantial differences emerge both across various topics and within specific framing categories. Compared to traditional news media, social media platforms tend to present more polarized stances across all framing categories. Further, we observe significant polarization in the news media’s treatment (i.e., Left vs. Right leaning media) of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus. The disparities in framing between traditional and social media platforms carry significant implications for the formation of public opinion, policy decision-making, and the broader political landscape.
Biosynthesis of neuroprotective melatonin is dysregulated in Huntington's disease

Huntington's disease (HD) is a progressive neurodegenerative brain disorder associated with uncontrolled body movements, cognitive decline, and reduced circulating melatonin levels. Melatonin is a potent antioxidant and exogenous melatonin treatment is neuroprotective in experimental HD models. In neurons, melatonin is exclusively synthesized in the mitochondrial matrix. Thus, we investigated the integrity of melatonin biosynthesis pathways in pineal and extrapineal brain areas in human HD brain samples, in the R6/2 mouse model of HD and in full‐length mutant huntingtin knock‐in cells. Aralkylamine N‐acetyltransferase (AANAT) is the rate‐limiting step enzyme in the melatonin biosynthetic pathway. We found that AANAT expression is significantly decreased in the pineal gland and the striatum of HD patients compared to normal controls. In the R6/2 mouse forebrain, AANAT protein expression was decreased in synaptosomal, but not nonsynaptosomal, mitochondria and was associated with decreased synaptosomal melatonin levels compared to wild type mice. We also demonstrate sequestration of AANAT in mutant‐huntingtin protein aggregates likely resulting in decreased AANAT bioavailability. Paradoxically, AANAT mRNA expression is increased in tissues where AANAT protein expression is decreased, suggesting a potential feedback loop that is, ultimately unsuccessful. In conclusion, we demonstrate that pineal, extrapineal, and synaptosomal melatonin levels are compromised in the brains of HD patients and R6/2 mice due, at least in part, to protein aggregation.
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?

Deep neural networks used in computer vision have been shown to exhibit many social biases such as gender bias. Vision Transformers (ViTs) have become increasingly popular in computer vision applications, outperforming Convolutional Neural Networks (CNNs) in many tasks such as image classification. However, given that research on mitigating bias in computer vision has primarily focused on CNNs, it is important to evaluate the effect of a different network architecture on the potential for bias amplification. In this paper we therefore introduce a novel metric to measure bias in architectures, Accuracy Difference. We examine bias amplification when models belonging to these two architectures are used as a part of large multimodal models, evaluating the different image encoders of Contrastive Language Image Pretraining which is an important model used in many generative models such as DALL-E and Stable Diffusion. Our experiments demonstrate that architecture can play a role in amplifying social biases due to the different techniques employed by the models for feature extraction and embedding as well as their different learning properties. This research found that ViTs amplified gender bias to a greater extent than CNNs
Epidemiology of lean/non-obese nonalcoholic fatty liver disease in China

Objectives: To assess the prevalence and metabolic characteristics of lean/non-obese (L/NO) nonalcoholic fatty liver disease (NAFLD) in China. Methods: The databses, inlcuding PubMed, Web of Science, EMBASE, as well as Cochrane databases, were retrieved for eligible studies. The prevalence together with clinical features of L/NO-NAFLD in China were analyzed using a random/fixed effects model. Lean or nonobese participants were characterized by the cut-offs of body mass index used in original studies. Heterogeneity was identified using meta-regression and subgroup analyses. Results: We included 25 studies for the final analysis comprising 229091 L/NO Chinese adults and 22641 diagnosed with NAFLD, with the NAFLD prevalence of 8.98% (95% confidence interval [CI]: [5.55-13.13] for L-NAFLD Chinese participants and 13.77% (95% CI: [11.13-16.63]) for NO-NAFLD Chinese participants. This prevalence gradually increased during the past few years. The community and health checkup populations presented similar prevalence (14.19% vs. 13.55%). Meanwhile, L/NO patients with NAFLD showed lower blood pressure (128.86/80.48 vs. 136.09/84.98 mmHg), waist circumference (80.63 vs. 92.73 cm), fasting blood glucose (5.53 vs. 5.69 mmol/L), uric acid (339.14 vs. 365.46 μmol/L), triglyceride levels (1.63 vs. 1.94 mmol/L), alanine transaminase (30.28 vs. 33.12 IU/L), and γ-glutamyl transferase (29.9 vs. 43.68 IU/L), but higher levels of high-density lipoprotein cholesterol (1.33 vs. 1.26 mmol/L) compared to overweight/obese (OW/O) patients with NAFLD. Conclusion: Prevalence of NAFLD was slightly lower among the L/NO-NAFLD Chinese population than the global level but has obviously increased recently. In addition, the metabolic profile of L/NO-NAFLD patients was generally better compared to OW/O-NAFLD patients. PROSPERO Reg. No.: CRD42022327240
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents

Resolving the scope of a negation within a sentence is a challenging NLP task. The complexity of legal texts and the lack of annotated in-domain negation corpora pose challenges for state-of-the-art (SotA) models when performing negation scope resolution on multilingual legal data. Our experiments demonstrate that models pre-trained without legal data underperform in the task of negation scope resolution. Our experiments, using language models exclusively fine-tuned on domains like literary texts and medical data, yield inferior results compared to the outcomes documented in prior cross-domain experiments. We release a new set of annotated court decisions in German, French, and Italian and use it to improve negation scope resolution in both zero-shot and multilingual settings. We achieve token-level F1-scores of up to 86.7% in our zero-shot cross-lingual experiments, where the models are trained on two languages of our legal datasets and evaluated on the third. Our multilingual experiments, where the models were trained on all available negation data and evaluated on our legal datasets, resulted in F1-scores of up to 91.1%.