Miracle-dz/UniFAR-FTUs huggingface dataset:
Pointwise convergence problem of Ostrovsky equation with rough data and random data

In this paper, we consider the pointwise convergence problem of free Ostrovsky equation with rough data and random data. Firstly, we show the almost everywhere pointwise convergence of free Ostrovsky equation in $H^{s}(\mathbb{R})$ with $s\geq \frac{1}{4}$ with rough data. Secondly, we present counterexamples showing that the maximal function estimate related to the free Ostrovsky equation can fail if $s<\frac{1}{4}$. Finally, for every $x\in \mathbb{R}$, we show the almost surely pointwise convergence of free Ostrovsky equation in $L^{2}(\mathbb{R})$ with random data. The main tools are the density theorem, high-low frequency idea, Wiener decomposition and Lemmas 2.1-2.6 as well as the probabilistic estimates of some random series which are just Lemmas 3.2-3.4 in this paper. The main difficulty is that zero is the singular point of the phase functions of free Ostrovsky equation. We use high-low frequency idea to conquer the difficulties.
Identification of a new deadenylation negative feedback loop that regulates meiotic progression

Los oocitos de vertebrados estan parados en la profase I de la primera division meiotica (PI). Durante la oogenesis, los oocitos sintetizan y almacenan grandes cantidades de ARN mensajeros que seran necesarios para la complecion de la meiosis y tambien para las primeras divisiones embrionarias. De este periodo destaca el hecho que transcurren en ausencia de transcripcion, por lo tanto la sintesis de proteinas depende de la activacion traduccional de los ARN mensajeros (ARMm) acumulados durante la oogenesis. La activacion traduccional de los mensajeros esta inducida por la elongacion de la cola de poliadeninas (cola poli (A)), mediada por las secuencias de poliadenilacion citoplasmatica (CPE) presentes en la region 3# no traducida (3#UTR) de los ARNm. El momento y la longitud de la poliadenilacion de los ARNm que contienen los elementos CPE estan estrechamente regulados, de modo que combinado con la degradacion de las proteinas, se establecen patrones especificos de expresion de proteinas que conducen la meiosis (Shmitt et al., 2002; de Moor and Richter, 1997; Ballantyne et al., 1997; Mendez et al., 2002; Charlesworth et al., 2002). Hasta la fecha , no se ha descrito que la deadenilacion (acortamiento de la cola de poli (A) fuera necesaria para la progresion meiotica. En esta tesis se ha descrito e identificado una ruta de retroalimentacion negativa requerida para progresion meiotica despues de la entrada en la primera metafase. Esta nueva ruta, ademas, tiene la particularidad de actuar a nivel traduccional regulando la expresion de las proteinas por deadenilacion. El elemento central de esta ruta es la proteina C3H-4, que interacciona con elementos ARE de las regiones 3#UTR provocando la deadenilacion del ARNm al cual se esta uniendo. Ademas se han identificado dos de sus dianas, Emi1 y Emi2, las dos, reguladoras de la actividad APC/C que es critica para la division celular.
Efficiency of different marker systems for molecular characterization of subtropical carrot germplasm

SUMMARY Genetic variability in carrots is a consequence of allogamy, which leads to a high level of inbreeding depression, affecting the development of new varieties. To understand the extent of genetic variability in 40 elite indigenous breeding lines of subtropical carrots, 48 DNA markers consisting of 16 inter simple sequence repeats (ISSRs), 10 universal rice primers (URPs), 16 random amplification of polymorphic DNA (RAPD) and six simple sequence repeat (SSR) markers were used. These 48 markers amplified a total of 591 bands, of which 569 were polymorphic (0·96). Amplicon size ranged from 200 to 3500 base pairs (bp) in ISSR, RAPD and URPs markers and from 100 to 300 bp in SSR markers. The ISSR marker system was found to be most efficient with (GT)n motifs as the most abundant SSR loci in the carrot genome. The unweighted pair group method with arithmetic mean (UPGMA) analysis of the combined data set of all the DNA markers obtained by four marker systems classified 40 genotypes in two groups with 0·45 genetic similarity with high Mantel matrix correlation (r=0·92). The principal component analysis (PCA) of marker data also explained 0·55 of the variation by first three components. Molecular diversity was very high and non-structured in these open-pollinated genotypes. The study demonstrated for the first time that URPs can be used successfully in genetic diversity analysis of tropical carrots. In addition, an entirely a new set of microsatellite markers, derived from the expressed sequence tags (ESTs) sequences of carrots, has been developed and utilized successfully.
Automatic Recommendation of Strategies for Minimizing Discomfort in Virtual Environments

Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is becoming accessible to the mass market. Virtual reality provides immersive experiences but still does not offer an entirely perfect situation, mainly due to Cybersickness (CS) issues. In this work, we first present a detailed review about possible causes of CS. Following, we propose a novel CS prediction solution. Our system is able to suggest if the user may be entering in the next moments of the application into an illness situation. We use Random Forest classifiers, based on a dataset we have produced. The CSPQ (Cybersickness Profile Questionnaire) is also proposed, which is used to identify the player's susceptibility to CS and the dataset construction. In addition, we designed two immersive environments for empirical studies where participants are asked to complete the questionnaire and describe (orally) the degree of discomfort during their gaming experience. Our data was achieved through 84 individuals on different days, using VR devices. Our proposal also allows us to identify which are the most frequent attributes (causes) in the observed discomfort situations.
EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations

The broad deployment of 802.11 (a.k.a. Wi-Fi) access points and the significant energy-efficiency improvement of 802.11 transceivers have resulted in increasing interest in building 802.11-based Internet of Things (IoT) systems. Unfortunately, the power saving mechanisms of 802.11 fall short when used in IoT applications, especially because they do not take into account the delays caused by various factors, such as buffering, interference, and round-trip delay. In this article, we present edge-assisted predictive sleep scheduling (EAPS) to adjust the sleep duration of stations while they are expecting downlink packets. We first implement a Linux-based access point that enables us to collect parameters affecting communication latency. Using this access point, we build a testbed that, in addition to offering traffic pattern customization, replicates the characteristics of real-world environments. We then use multiple machine learning algorithms to predict downlink packet delivery. Our empirical evaluations confirm that with EAPS, the energy consumption of IoT stations is as low as power save mode, whereas the delay of packet delivery is close to the case where the station is always awake.
Combined Effects of Partial Defoliation and Nutrient Availability on Cloned Betula pendula Saplings I CHANGES IN GROWTH, PARTITIONING AND NITROGEN UPTAKE

The combined effects of partial defoliation and nutrient availability on dry matter accumulation and partitioning, and on nitrogen uptake and partitioning, were studied in cloned Betula pendula Roth saplings. The saplings were randomly assigned to different nutrient levels (5, 1.5 and 0.5 mol N m -3 ) in aerated nutrient culture and to the following defoliation treatments: (1) control (no damage), (2) damage of the developing main stem leaves (half of the leaf lamina removed), and (3) removal of the developing main stem leaves (entire leaf lamina removed). Measured in terms of cumulative whole-plant dry weight (including removed leaf tissue), the birch saplings were unable to compensate for the loss of the developing leaves (treatment 3) during the 14 d study period
APPROXIMATE MULTIPLE STRING MATCHING ALGORITHM

Approximate multiple string matching comprises of finding all appearances of a set of patterns within a larger text with at most k-mismatches or k-differences. It is a process that consumes large amounts of computational resources and has applications in many domains of computer science such as correction of spelling, spam filtering and computational biology to name a few. However, these applications require a fast algorithm for string matching which depends on the construction of an accurate shift table. When a mismatch occurs while searching a pattern, a shift table specifies the maximum number of characters that can be skipped in text while ensuring that a possible match is not overlooked. This study presents a simple and efficient algorithm that is a variant of Tarhio-Ukkonen algorithm generalized to approximate multiple string matching. The algorithm searches for patterns of different lengths with at most k -mismatches by skipping some of the characters, while not missing the occurrences of any pattern. While the space and time complexity for construction of shift table is O(|S|*( 1) ) k c   and O( ) M k c   , the time complexity of search algorithm is ( ) O M n  , where | | S is the number of patterns, c is the number of characters in the alphabet, n is the length of the text string and M is the total length of all patterns. To evaluate the efficacy of the algorithm, experiments are conducted on random text and random patterns of different lengths and different values of 𝑘.
Beneath (or beyond) the surface: Discovering voice-leading patterns with skip-grams

Recurrent voice-leading patterns like the Mi-Re-Do compound cadence (MRDCC) rarely appear on the musical surface in complex polyphonic textures, so finding these patterns using computational methods remains a tremendous challenge. The present study extends the canonical n-gram approach by using skip-grams, which include sub-sequences in an n-gram list if their constituent members occur within a certain number of skips. We compiled four data sets of Western tonal music consisting of symbolic encodings of the notated score and a recorded performance, created a model pipeline for defining, counting, filtering, and ranking skip-grams, and ranked the position of the MRDCC in every possible model configuration. We found that the MRDCC receives a higher rank in the list when the pipeline employs 5 skips, filters the list by excluding n-gram types that do not reflect a genuine harmonic change between adjacent members, and ranks the remaining types using a statistical association measure.
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory

Deep learning has significantly revolutionized the design of numerical algorithms for solving high-dimensional partial differential equations (PDEs). Yet the empirical successes of such approaches remains mysterious in theory. In deep learning-based PDE solvers, solving the original PDE is formulated into an expectation minimization problem with a PDE solution space discretized via deep neural networks. A global minimizer corresponds to a deep neural network that solves the given PDE. Typically, gradient descent-based methods are applied to minimize the expectation. This paper shows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks in the over-parameterization regime (i.e., the network width is sufficiently large). The generalization error of the gradient descent solution does not suffer from the curse of dimensonality if the solution is in a Barron-type space. The theories developed here could form a theoretical foundation of deep learning-based PDE solvers.
Experimental measurements of a joint 5G-VLC communication for future vehicular networks

One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, visible light communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information provided can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm.
A new level set-finite element formulation for anisotropic grain boundary migration

Grain growth in polycrystals is one of the principal mechanisms that take place during heat treatment of metallic components. This work treats an aspect of the anisotropic grain growth problem. By applying the first principles of thermodynamics and mechanics, an expression for the velocity field of a migrating grain boundary with an inclination dependent energy density is expressed. This result is used to generate the first, to the authors' knowledge, analytical solution (for both the form and kinetics) to an anisotropic boundary configuration. This new benchmark is simulated in order to explore the convergence properties of the proposed level set finite element numerical model in an anisotropic setting. Convergence of the method being determined, another configuration, using a more general grain boundary energy density, is investigated in order to show the added value of the new formulation.
Robust Out-of-distribution Detection via Informative Outlier Mining

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle under small adversarial perturbations. In this paper, we propose a simple and effective method, Adversarial Training with informative Outlier Mining (ATOM), to robustify OOD detection. Our key observation is that while unlabeled data can be used as auxiliary OOD training data, the majority of these data points are not informative to improve the decision boundary of the OOD detector. We show that, by carefully choosing which outliers to train on, one can significantly improve the robustness of the OOD detector, and somewhat surprisingly, generalize to some adversarial attacks not seen during training. We provide additionally a unified evaluation framework that allows future research examining the robustness of OOD detection algorithms. ATOM achieves state-of-the-art performance under a broad family of natural and perturbed OOD evaluation tasks, surpassing previous methods by a large margin. Finally, we provide theoretical insights for the benefit of auxiliary unlabeled data and outlier mining.
Higher derivative scalar-tensor theory from the spatially covariant gravity: a linear algebraic analysis

We investigate the ghostfree scalar-tensor theory with a timelike scalar field, with the derivatives of the scalar field up to the third order and with the Riemann curvature tensor up to the quadratic order. There are two kinds of linear space that are isomorphic to each other in the sense of gauge fixing/recovering procedures. One is the set of linearly independent generally covariant scalar-tensor monomials, the other is the set of linearly independent spatially covariant gravity monomials. We concentrate on the subspaces of the spatially covariant gravity, which are spanned by the linearly independent monomials built of the extrinsic curvature, the intrinsic curvature, the lapse function as well as their spatial derivatives. The vectors in these subspaces, i.e., spatially covariant gravity polynomials, automatically propagate at most three degrees of freedom. As a result, their images under the gauge recovering mappings are automatically the subspaces of the generally covariant scalar-tensor theory that propagate up to three degrees of freedom as long as the scalar field is timelike, although the higher derivatives of the scalar field are present generally. We also derive the explicit expressions for the projection matrices, which encode the mappings from the subspaces of the spatially covariant gravity to the subspaces of the generally covariant scalar-tensor theory, up to the fourth order in the total number of derivatives in the spatially covariant gravity. Our formalism and results will be useful in deriving the generally covariant higher derivative scalar-tensor theory without ghost(s).
Search for glitches in gamma-ray pulsars with deep learning

The pulsar glitches are generally assumed to be an apparent manifestation of the superfluid interior of the neutron stars. Most of them were discovered and extensively studied by continuous monitoring in the radio wavelengths. The Fermi-LAT space telescope has made a revolution uncovering a large population of gamma-ray pulsars. In this paper we suggest to employ these observations for the searches of new glitches. We develop the method capable of detecting step-like frequency change associated with glitches in a sparse gamma-ray data. It is based on the calculations of the weighted H-test statistics and glitch identification by a convolutional neural network. The method demonstrates high accuracy on the Monte Carlo set and will be applied for searches of the pulsar glitches in the real gamma-ray data in the future works.
Automated Stitching of Coral Reef Images and Extraction of Features for Damselfish Shoaling Behavior Analysis

Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness, reproductive patterns, group decision-making, and effects in animal epidemiology. In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources. Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information. This limits reef managers in efficiently planning for stress and disaster responses in protecting the reef. Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment. The gathered videos of damselfish by this research exhibit several scene distortions caused by erratic camera motions during acquisition. To effectively analyze shoaling behavior given the issues posed by capturing data in the wild, we propose a pre-processing system that utilizes color correction and image stitching techniques and extracts behavior features for manual analysis.
Molecular characterization of two glutathione peroxidase genes in Mytilus galloprovincialis and their transcriptional responses to sub-chronic arsenate and cadmium exposure

Glutathione peroxidases (GPxs) are key enzymes in the antioxidant defense system of livingorganisms, and protect organisms against oxidative stresses. In this study, the full-length cDNAsequences encoding cytosolic GPx (MgcGPx) and phospholipid-hydroperoxide GPx (MgGPx4) were identified from Mytilus galloprovincialis. The mussels were exposed to 0, 1, 10, and 100 μg/L cadmium and arsenate for 30 days. The mRNA transcripts of these two genes and total GPx activity were examined in the gills and digestive gland after contaminants exposure. The mussels exposed to cadmium and arsenate responded mainly by down-regulating MgcGPx and MgGPx4 mRNA transcription in gills and up-regulating transcription in digestive gland. However, total GPx activities increased following cadmium exposure but decreased after arsenate stress in both tissues. These results suggest that MgcGPx and MgGPx4 perhaps play an important role in maintaining cellular redox homeostasis and protecting M. galloprovincialis against cadmium and arsenate toxicity. It can also be inferred that these genes have the potential to be used as molecular biomarkers for assessing cellular stress and toxicity of contaminants in this mussel.
Effectiveness and Safety of Apixaban for Treatment of Venous Thromboembolism in Daily Practice

Abstract Introduction Phase 3 trials have shown comparable efficacy of direct oral anticoagulants (DOACs) and vitamin K antagonists in patients with acute venous thromboembolism (VTE), with less major bleeding events in patients randomized to DOAC treatment. With DOACs being increasingly used in clinical practice, evaluation of the DOACs in daily practice-based conditions is needed to confirm their safety and effectiveness. The aim of this study is to evaluate the effectiveness and safety of apixaban in VTE patients in daily practice. Methods In this retrospective cohort study, consecutive patients diagnosed with VTE in two Dutch hospitals (Leiden University Medical Center, Leiden and Haga Teaching Hospital, The Hague) were identified based on administrative codes. We assessed recurrent VTE, major bleeding and mortality during a 3-month follow-up period in those treated with apixaban. Results Of 671 consecutive VTE patients treated with apixaban, 371 presented with acute pulmonary embolism (PE) and 300 patients with deep-vein thrombosis. During 3 months treatment, 2 patients had a recurrent VTE (0.3%; 95% confidence interval [CI]: 0.08–1.1), 12 patients had major bleeding (1.8%; 95% CI: 1.0–3.2), and 11 patients died (1.6%; 95% CI: 0.9–2.9), of which one patient with recurrent PE and one because of a intracerebral bleeding. Conclusion In this daily practice-based cohort, apixaban yielded a low incidence of recurrent VTE, comparable to the phase 3 AMPLIFY study patients. The incidence of major bleeding was higher than in the AMPLIFY-study patients, reflecting the importance of daily practice evaluation and the fact that results from phase III clinical studies cannot be directly extrapolated toward daily practice.
Colonic diverticulitis following open-heart surgery: a case report of an unusual postoperative gastrointestinal complication

Abstract Diverticulitis, though a common gastrointestinal disease, is rare following open-heart surgery. There is insufficient data regarding its incidence and management post-cardiac surgery. Especially in patients with atypical presentation, diagnosis and management can be challenging. This case outlines one such atypical diverticulitis case in which a 57-year-old female patient developed perforated diverticulitis with pelvic abscess accumulation following left ventricular aneurysm (LVA) repair. Diagnosis, appropriate management and treatment approaches are discussed. Cardiac surgeons should consider the possibility of diverticulitis in patients reporting nonspecific abdominal pain following cardiac surgery to ensure early diagnosis and institution of appropriate treatment to prevent associated adverse outcomes.