Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential Stochastic Inequality

We develop the concept of exponential stochastic inequality (ESI), a novel notation that simultaneously captures high-probability and in-expectation statements. It is especially well suited to succinctly state, prove, and reason about excess-risk and generalization bounds in statistical learning, specifically, but not restricted to, the PAC-Bayesian type. We show that the ESI satisfies transitivity and other properties which allow us to use it like standard, nonstochastic inequalities. We substantially extend the original definition from Koolen et al. (2016) and show that general ESIs satisfy a host of useful additional properties, including a novel Markov-like inequality. We show how ESIs relate to, and clarify, PAC-Bayesian bounds, subcentered subgamma random variables and *fast-rate conditions* such as the central and Bernstein conditions. We also show how the ideas can be extended to random scaling factors (learning rates).
Quadric Representations for LiDAR Odometry, Mapping and Localization

Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks. However, the space-inefficiency of methods that use point-wise representations limits their development and usage in practical applications. In particular, scan-submap matching and global map representation methods are restricted by the inefficiency of nearest neighbor searching (NNS) for large-volume point clouds. To improve space-time efficiency, we propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects than conventional point clouds. Our method first segments a given point cloud into patches and fits each of them to a quadric implicit function. Each function is then coupled with other geometric descriptors of the patch, such as its center position and covariance matrix. Collectively, these patch representations fully describe a 3D scene, which can be used in place of the original point cloud and employed in LiDAR odometry, mapping and localization algorithms. We further design a novel incremental growing method for quadric representations, which eliminates the need to repeatedly re-fit quadric surfaces from the original point cloud. Extensive odometry, mapping and localization experiments on large-volume point clouds in the KITTI and UrbanLoco datasets demonstrate that our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.
The Green function for p-Laplace operators

On a bounded domain $\Omega \subset \mathbb{R}^N$, $N\geq 2$, we consider existence, uniqueness and"regularity"issues for the Green function $G_\lambda$ of the quasi-linear operator $u \to -\Delta_p u-\lambda |u|^{p-2}u$ with $1<p \leq N$, homogeneous Dirichlet boundary condition and $\lambda<\lambda_1$, where $\lambda_1>0$ is the first eigenvalue of $-\Delta_p$.
SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts

Personalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible al- ternative for predicting the most appropriate therapy for individual patients. However, the need to develop a custom model for every dataset, the lack of interpretation of their results and high computational requirements make many reluctant to use these methods. Aiming to save time and bring light to the way models work internally, SIBILA has been developed. SIBILA is an ensemble of machine learning and deep learning models that applies a range of interpretability algorithms to identify the most relevant input features. Since the interpretability algo- rithms may not be in line with each other, a consensus stage has been imple- mented to estimate the global attribution of each variable to the predictions. SIBILA is containerized to be run on any high-performance computing plat- form. Although conceived as a command-line tool, it is also available to all users free of charge as a web server at https://bio-hpc.ucam. edu/sibila. Thus, even users with few technological skills can take advantage of it. SIBILA has been applied to two medical case studies to show its ability to predict in classification problems. Even though it is a general-purpose tool, it has been developed with the aim of becoming a powerful decision-making tool for clinicians, but can actually be used in many other domains. Thus, other two non-medical examples are supplied as supplementary material to prove that SIBILA still works well with noise and in regression problems.
Model scale versus domain knowledge in statistical forecasting of chaotic systems

Chaos and unpredictability are traditionally synonymous, yet large-scale machine learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually-chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability. Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times, thereby accessing a new long-horizon forecasting regime well beyond classical methods. We find that, in this regime, accuracy decorrelates with classical invariant measures of predictability like the Lyapunov exponent. However, in data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.
HeartLogic™: real-world data—efficiency, resource consumption, and workflow optimization

Abstract Heart failure (HF) is a major and still growing medical problem and is characterized by episodes of acute decompensation that are associated with a negative prognosis and a significant burden on the patients, doctors, and healthcare resources. Early detection of incipient HF may allow outpatient treatment before patients severely decompensate, thus reducing HF hospitalizations and related costs. The HeartLogic™ algorithm is an automatic, remotely managed system combining data directly related to HF pathophysiology into a single score, the HeartLogic™ index. This index proved to be effective in predicting the risk of incipient HF decompensation, allowing to redistribute resources from low-risk to high-risk patients in a timely and cost-saving manner. The alert-based remote management system seems more efficient than the one based on scheduled remote transmission in terms of caregivers’ workload and alert detection timing. The widespread application of the HeartLogic™ algorithm requires the resolution of logistical and financial issues and the adoption of a pre-defined, functional workflow. In this paper, we reviewed general aspects of remote monitoring in HF patients, the functioning and pathophysiological basis of the HeartLogic index, its efficiency in the management of HF patients, and the economic effects and the organizational revolution associated with its use.
Reproductive phases coincide with changes in morphology and photosynthetic physiology in an endangered cycad species

Cycadales is nominally the most endangered plant group on Earth, yet so much of their basic biology remains unknown which limits conservation strategies. We examined how key aspects of plant function may change with reproductive status in a tropical endangered cycad. We found that sterile individuals may have greater light demands relative to fertile individuals, and fertile individuals may have greater nitrogen demands, which may be critical for successful reproductive events in this endangered cycad.
Viral etiology of lower respiratory tract infections in adults in the pre‐COVID‐19 pandemic era: A cross‐sectional study in a single center experience from Cameroon

Respiratory viruses are responsible for the majority of lower respiratory tract infections (LRTIs) worldwide. However, there is a gap on the epidemiology of viral LRTIs in adults in sub‐Saharan African countries. In Cameroon, like in other countries, the role of viral respiratory pathogens in the etiology of LRTIs in adults is helpful for clinical management. This study aimed to determine the viral aetiologies of LRTIs among hospitalized adults in a reference center for respiratory diseases in the town of Yaounde in Cameroon and its surroundings.
Stage-specific therapy for hypertrophic cardiomyopathy

Abstract Hypertrophic cardiomyopathy (HCM) is the most common inherited myocardial disease and is defined by otherwise unexplained left ventricular hypertrophy. The main complications include heart failure and arrhythmias such as atrial fibrillation and ventricular arrhythmias. Current treatment rests on septal reduction therapies, prevention of sudden cardiac death through implantable cardioverter defibrillator, and use of drugs such as beta-blockers, calcium antagonists, or amiodarone. In the last years, new pharmacological agents specifically targeting the pathophysiology of the disease have been developed with encouraging results in terms of functional capacity and symptoms improvement from clinical trials. In this review, we summarize the possible treatment approaches for each phase of the natural history of the disease: pre-phenotype expression, classic phenotype, adverse remodelling, and overt dysfunction.
Rapid shallow megathrust afterslip from the 2021 M8.2 Chignik, Alaska earthquake revealed by seafloor geodesy

The shallower portions of subduction zone megathrust faults host Earth’s most hazardous tsunamigenic earthquakes, yet understanding how and when they slip remains elusive because of challenges making seafloor observations. We performed Global Navigation Satellite System Acoustic seafloor geodetic surveys before and ~2.5 months after the 29 July 2021 Mw (moment magnitude) 8.2 Chignik, Alaska, earthquake and determine ~1.4 meters cumulative co- and post-seismic horizontal displacement ~60 kilometers from the megathrust front. Only for the 2011 Mw 9 Tohoku event have closer subduction zone earthquake displacements been observed. We estimate ~2 to 3 meters of megathrust afterslip shallower than 20 kilometers, a portion of the megathrust on which both inter- and co-seismic slip likely had occurred previously. Our analysis demonstrates that by 2.5 months, shallower and deeper moment had effectively equilibrated on the megathrust, suggesting that its tsunamigenic potential remains no more elevated than before the earthquake.
The NCEP/NCAR 40-Year Reanalysis Project

The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided by differe.
Device-independent randomness expansion secure against quantum adversaries

Measurements on entangled quantum systems necessarily yield outcomes that are intrinsically unpredictable if they violate a Bell inequality. This property is at the basis of device-independent randomness expansion, in which a certified private random string is generated from a shorter one without making assumptions about the internal working of the quantum devices used in the protocol. We show here that the privacy of the output string can be guaranteed even against adversaries that possess quantum side information about the devices. This implies that device independent randomness expansion is universally composable, and opens the possibility for highly efficient concatenated protocols in which a random string produced by a given pair of Bell-violating devices is used as a seed for another pair of devices. In particular, such concatenated protocols can produce an amount of randomness doubly exponential in the number of pairs of devices starting from a finite initial string and can produce an exponential (double exponential) amount of randomness in the length of the initial seed using two (four) pairs of devices, etc.
Understanding Nuance and Ambivalence in Intergenerational Relationships Through Fiction

Abstract Background and Objectives The term “intergenerational relationships” is widely used in gerontological literature and age-related policies. However, discussions of the term often tell us surprisingly little about what it means or why it matters. We suggest that this is due to a reductivism and instrumentalism in 2 main discourses within which intergenerational relationships are usually discussed. First, intergenerational relationships are often conceptualized through a binary “conflict/solidarity” lens, reinforcing an entrenched “generationalism”. Second, they are predominantly constructed as a problem to be addressed within debates on how to tackle intergenerational segregation. Neither of these discourses provides much room for a more nuanced understanding of how intergenerational relationships are experienced or why they are meaningful. In this paper, we discuss how fictional narratives can introduce imagination and a richer vocabulary into discourses concerning how people of different ages relate to each other. Research Design and Methods We present findings from reading groups where adults discussed novels depicting themes of older age, intergenerational relationships, and time. Results In discussing the fictional narratives and characters, participants reflected on the significance and meaning of intergenerational relationships in ways that went beyond dichotomous and instrumentalist discourses. Drawing on the concept of lived ambivalence, we argue that fictional representations of intergenerational themes can elicit more meaningful reflections on the complexities and contradictions of relationships across age groups. Discussion and Implications We conclude that a more nuanced understanding of intergenerational interaction can inform gerontological discourses and policy, but also that gerontological awareness of social challenges concerning age relations can inform interpretations of fictional narratives.
Accessing Apoptosis Induction and Metastasis Inhibition Effect of Magnolol on Triple Negative Breast Cancer In Vitro

Background/Aim: Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer that still requires improvement in treatment. Magnolol extract, derived from the bark of Magnolia officinalis, has traditionally been used in Asia to treat sleeping disorders and anxiety, and as an anti-inflammatory agent. Several reports have indicated that magnolol may have the potential to inhibit the progression of hepatocellular carcinoma and glioblastoma. However, the anti-tumor effect of magnolol on TNBC remains unknown. Materials and Methods: In this study, we used two TNBC cell lines, MDA-MB-231 and 4T1, to examine the cytotoxicity, apoptosis, and metastasis effects of magnolol. These were evaluated using MTT assay, flow cytometry, western blotting, and invasion/migration transwell assay, respectively. Results: Magnolol significantly induced cytotoxicity and extrinsic/intrinsic apoptosis in both TNBC cell lines. It also decreased metastasis and associated protein expression in a dose-dependent manner. Furthermore, the anti-tumor effect was associated with the inactivation of the epidermal growth factor receptor (EGFR)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling pathway. Conclusion: Magnolol may not only induce cell death in TNBC through apoptosis signaling activation but also by down-regulating EGFR/JAK/STAT3 signaling, which mediates TNBC progression.
Metal-nanotube composites as radiation resistant materials

The improvement of radiation resistance in nanocomposite materials is investigated by means of classical reactive molecular dynamics simulations. In particular, we study the influence of carbon nanotubes (CNTs) in an Ni matrix on the trapping and possible outgassing of He. When CNTs are defect-free, He atoms diffuse alongside CNT walls and, although there is He accumulation at the metal-CNT interface, no He trespassing of the CNT wall is observed, which is consistent with the lack of permeability of a perfect graphene sheet. However, when vacancies are introduced to mimic radiation-induced defects, He atoms penetrate CNTs, which play the role of nano-chimneys, allowing He atoms to escape the damaged zone and reduce bubble formation in the matrix. Consequently, composites made of CNTs inside metals are likely to display improved radiation resistance, particularly when radiation damage is related to swelling and He-induced embrittlement.
Transition From School Violence Victimization to Reporting Victimized Experiences: A Latent Transition Analysis

This study aimed to understand the heterogeneity of school violence experiences among South Korean youth, explicitly examining how each type of violence experienced transitions into different reporting behaviors. A latent profile analysis was conducted to classify different types of violence victimization and reporting behaviors, followed by a latent transition analysis, which contributed to an understanding of the relationships between profiles of violence and reporting. The influence of social support on reporting victimization was further examined. The results are as follows. First, school violence victimization experience was divided into five profiles: cyber violence-oriented (7.0%), ostracization-oriented (8.9%), verbal violence-oriented (41.8%), high-level of multiple-violence (2.8%), and medium-level of multiple-violence (39.5%). Second, reporting behavior was divided into four profiles: reporting to family and teachers (14.7%), reporting to family, teachers, and friends (11.0%), actively reporting (1.5%), and passively coping (72.8%). Third, students showed the highest probability of passively reporting, while the probability of actively reporting was low for all victimization profiles. Fourth, support from family and friends positively correlated with reporting violence, while support from teachers did not. The findings confirm that reporting violence may vary depending on the type of school violence victimization, implying that different types of violence need to be mitigated in distinct ways. Additionally, the study’s result regarding the influence of social support suggests that school counselors and practitioners need to develop ways to facilitate violence reporting in schools.
MERS-CoV seroconversion amongst Malaysian Hajj pilgrims returning from the Middle East, 2016–2018: results from the MERCURIAL multiyear prospective cohort study

ABSTRACT Prospective cohort study to investigate the potential exposure to the Middle East Respiratory Syndrome-Coronavirus (MERS-CoV) following Hajj pilgrims is still very limited. Here, we report the antibody seroconversion study results obtained from successive three years cohort studies (2016–2018) involving the Malaysian Hajj pilgrims returning from the Middle East. A cohort study of Hajj pilgrims from Malaysia enrolled 2,863 participants from 2016–2018, all of whom consented to provide paired blood samples for both pre- and post-Hajj travel to the Middle East. ELISAs and micro-neutralization assays were performed to detect the presence of MERS-CoV IgG antibodies. Sociodemographic data, symptoms experienced during Hajj, and history of exposure to camels or camel products were recorded using structured pre- and post-Hajj questionnaires. A 4-fold increase in anti-MERS-CoV IgG between paired pre-Hajj and post-Hajj serum samples in twelve participants was observed. None of the twelve ELISA-positive sera had detectable levels of virus-neutralizing antibodies. All reportedly had mild symptoms of respiratory symptoms at a certain point during the pilgrimage, implying mild or asymptomatic infections. No association between post-Hajj serum positivity and a history of exposure to camels or camel products was obtained. Findings from the study suggest that serologic conversion to MERS-CoV occurred in at least 0.6% of the Hajj pilgrims returning from the Middle East. Since all the seroconvertants had mild to no symptoms during the sampling period, it highlights the likelihood of occurrence of only low infectivity spillover infections among the Hajj pilgrims.
CCD photometry of the galactic star clusters Be 15, Be 71 and King 1

We present UBVRI CCD photometry of three open star clusters Be 15, Be 71 and King 1 for the flrst time. Base on these data, optical colour- magnitude diagrams (CMDs) for the stars in the clusters down to V = 22 mag arestudied. Thereare1408,1887and2485starslocatedintheregionsofBe15, Be 71 and King 1 respectively. We also provide estimates of their fundamental parameters such as radius, reddening, distance and age. The radius values for the clusters Be 15, Be 71 and King 1 are 4.5, 3.0 and 4.0 arcmin respectively. The corresponding distances are 3.0§0:3,3.9§0:4 and 1.9§0:2 kpc respectively while the ages are log (age)=8.5§0.1, 8.8§0.1 and 9.2§0.1 respectively. The red giant clump is clearly visible in the CMDs of cluster King 1.
Temperature-dependent orbital physics in a spin-orbital-lattice-coupled 2p electron Mott system: The case of KO 2

We have investigated the temperature (T )-dependent evolution of orbital states in a typical spin-orbital-latticecoupled 2p electron Mott system KO2, based on the electronic structures obtained by the dynamical mean-field theory as well as the density functional theory. We have shown that KO2 exhibits the orbital fluctuation feature at high T due to degenerate π∗ g orbitals. Upon cooling, the orbital fluctuation is suppressed by the Jahn-Teller-type crystal field that becomes stronger with the lowering of structural symmetry, and then the ferro-orbital (FO) ordering emerges at low T . This FO ordering feature distinguishes KO2 from RbO2 and CsO2 in that the latter two seem to have antiferro-orbital orderings at low T , indicating that the underlying physics is different between them. We propose that the suppression of the orbital fluctuation in KO2 can be observed by thermal-conductivity measurement, as observed in spin-orbital-lattice-coupled 3d transition-metal oxides such as LaVO3.