Miracle-dz/UniFAR-FTUs huggingface dataset:
AND COPULAS - A PERSONAL LOOK

The author recalls his initial involvement with the basic notions of probability theory, which began in the late forties in the context of number theory, continued through his work with B. Schweizer on probabilistic metric spaces, and culminated in a correspondence with Frechet that led to the identification and naming of copulas. The author speculates about possible future applications of the theory of distribution functions with given margins: In particular, there is the prospect of productive treatment of situations where, say, no common probability space can be found for a given set of "random variables," but such common probability spaces exist for arbitrary proper subsets of the given set.
SOME RIGIDITY RESULTS ON THE HEMISPHERE

Partial results have been derived in [1, 2, 3]. More generally it would be interesting to formulate a notion for the positive curvature case corresponding to the concept of asymptotically at manifolds for the case of comparison with the Euclidean spaces and prove an analogy of the positive mass theorem ([4]), which would imply Conjecture 0.1. Such a formulation should also enable us to get local comparison results in the positive curvature cases similar to those derived in [5].
A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry

Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof’s suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses.
A Subquadratic Algorithm for Road Coloring

The synchronizing word of a deterministic automaton is a word in the alphabet of colors (considered as letters) of its edges that maps the automaton to a single state. A coloring of edges of a directed graph is synchronizing if the coloring turns the graph into a deterministic finite automaton possessing a synchronizing word. The road coloring problem is the problem of synchronizing coloring of a directed finite strongly connected graph with constant outdegree of all its vertices if the greatest common divisor of lengths of all its cycles is one. The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automata and symbolic dynamics. A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented. The work is based on my recent positive solution of the road coloring problem.
Conhecimento dos profissionais de saúde acerca da transmissão vertical da sífilis em Fortaleza

The aim of this study was to investigate the knowledge of family health strategy professionals regarding the prevention and control of the vertical transmission of syphilis. This cross-sectional study was carried out in Fortaleza, Ceara, Brazil, from August to October 2009. A questionnaire was used, with its correct answers being in line with the recommendations of the Ministry of Health. Univariate analysis was carried out, with frequency distribution, mean and standard deviation. A total of 269 professionals participated, 44.7% had 6 to 10 years of experience in the FHS, and 60% of the questions regarding their knowledge were answered correctly: 75.8% were aware of the request period of the Venereal Disease Research Laboratory (VDRL) test; 78.1% of the alternative drug for the treatment for pregnant women allergic to penicillin, 55.1% of the request period for VDRL for cure and control, and 50.2% of the conduct with a sexual partner. It was concluded that healthcare professionals are unaware of the appropriate actions for the prevention and control of congenital syphilis.
SIMPLE NUMERICAL TASKS 1

Summary . This study examined whether is it easier to reason in terms of frequencies or with percentages for simple numerical tasks. Research on probabilistic reasoning has shown that humans can draw correct inferences when problems are presented in terms of natural frequencies, but not when in percentages . Whether the same effect can be observed in other numerically simple tasks which are not probabilistic was studied with 40 undergraduate students who volunteered for the experiment (13 men, 27 women, M age of 23 yr.). In a simple numerical task involving frequencies or percentages ( N =20), their performance showed representation in frequencies facilitates the task.
Development of an Acoustic Lens for an Imaging Sonar for Autonomous Underwater Vehicle “Urashima” and Experimentation in a Water Tank

Underwater imaging sonar has been developed for obstacle avoidance and navigation of the autonomous underwater vehicle (AUV) “URASHIMA”. The acoustic image, the distance and the azimuth of the target are obtained using an acoustic lens. Tone burst acoustic waves are transmitted and scattered at the target, and a direct image of the target can be created on a two-dimensional receiving array. The merits of the acoustic lens system are the possibility to omit the extensive calculation required for signal processing of received signals at each element to form two-dimensional beams, and reduction of equipment size. In this report, the outline of the imaging sonar with a single spherical lens and the experimental results for the acoustic lens in a water tank are presented. Then, theoretical calculation of the sound field through the lens using the parabolic equation method is introduced as a useful tool for improvement of lens performance to solve problems in resolution and aberration.
OF ORGANIZATIONAL DESIGN AND SUSTAINABILITY: THE MEDIATING ROLE OF LEARNING

This paper builds on prior scholarly works by examining the relationship between organizing paradox (formalization and decentralization), and organizational levels of learning paradoxes, i.e. exploration and exploitation, and firms’ outcomes (organizational creativity, organizational resilience and organizational energy). Using data from 98 executives and 325 senior employees working across a diverse range of firms operating in the Middle East, the findings suggest that organizing paradox (formalization and decentralization) has a positive impact on learning ambidexterity. In addition, we also found that learning ambidexterity has a positive impact on both organizational resilience and organizational energy. Furthermore, the results indicate that learning ambidexterity mediates the relationship between organizing paradox and organizational creativity. These findings provide important insights into the micro-foundation aspects of organizational ambidexterity.
Metal-Based Nanostructured Therapeutic Strategies for Glioblastoma Treatment—An Update

Glioblastoma (GBM) is the most commonly diagnosed and most lethal primary malignant brain tumor in adults. Standard treatments are ineffective, and despite promising results obtained in early phases of experimental clinical trials, the prognosis of GBM remains unfavorable. Therefore, there is need for exploration and development of innovative methods that aim to establish new therapies or increase the effectiveness of existing therapies. One of the most exciting new strategies enabling combinatory treatment is the usage of nanocarriers loaded with chemotherapeutics and/or other anticancer compounds. Nanocarriers exhibit unique properties in antitumor therapy, as they allow highly efficient drug transport into cells and sustained intracellular accumulation of the delivered cargo. They can be infused into and are retained by GBM tumors, and potentially can bypass the blood–brain barrier. One of the most promising and extensively studied groups of nanostructured therapeutics are metal-based nanoparticles. These theranostic nanocarriers demonstrate relatively low toxicity, thus they might be applied for both diagnosis and therapy. In this article, we provide an update on metal-based nanostructured constructs in the treatment of GBM. We focus on the interaction of metal nanoparticles with various forms of electromagnetic radiation for use in photothermal, photodynamic, magnetic hyperthermia and ionizing radiation sensitization applications.
Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico

Background: One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs. Objectives: This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database. Materials and Methods: Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied. Results: Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan–Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database. Conclusions: The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as “pathways in cancer (hsa05200)”, “protein processing in the endoplasmic reticulum (hsa04141)”, “antigen processing and presentation (hsa04612)”, “dopaminergic synapse (hsa04728)”, and “measles (hsa05162)”; in which these pathways, the “protein processing in endoplasmic reticulum (hsa04141)”, was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.
An application of a generalization of Artin's primitive root conjecture in the theory of monoid rings

Using techniques of algebraic and analytic number theory, we resolve a question on monoid rings posed by Kulosman, et. al., under the assumption of the Generalized Riemann Hypothesis (GRH). Speciﬁcally, we show that under an appropriate GRH, for any (rational) prime p the set E ( p ) = { q prime | X q − 1 factors in F p [ X ; M ] } , where M = h 2 , 3 i = N 0 \ { 1 } , contains a subset with positive natural density. In particular E ( p ) 6 = ∅ . This proves that M is not a so-called “Matsuda monoid” of any positive type. For p = 2 , 3 this was observed by Kulosman, who provided factorizations of X 7 − 1 and X 11 − 1 in F 2 [ X ; M ] and F 3 [ X ; M ], respectively. Our results explain and reproduce both of these factorizations, as well.
On joint training with interfaces for spoken language understanding

Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances. SLU systems usually consist of (1) an automatic speech recognition (ASR) module, (2) an interface module that exposes relevant outputs from ASR, and (3) a natural language understanding (NLU) module. Interfaces in SLU systems carry information on text transcriptions or richer information like neural embeddings from ASR to NLU. In this paper, we study how interfaces affect joint-training for spoken language understanding. Most notably, we obtain the state-of-the-art results on the publicly available 50-hr SLURP dataset. We first leverage large-size pretrained ASR and NLU models that are connected by a text interface, and then jointly train both models via a sequence loss function. For scenarios where pretrained models are not utilized, the best results are obtained through a joint sequence loss training using richer neural interfaces. Finally, we show the overall diminishing impact of leveraging pretrained models with increased training data size.
Elementary proofs of several results on false discovery rate

We collect self-contained elementary proofs of four results in the litera-ture on the false discovery rate of the Benjamini-Hochberg (BH) procedure for independent or positive-regression dependent p-values, the Benjamini-Yekutieli correction for arbitrarily dependent p-values, and the e-BH procedure for arbitrarily dependent e-values. As a corollary, the above proofs also lead to some inequalities of Simes and Hommel.
A new resonance-like feature in the outer disc of the Milky Way

Modern astrometric and spectroscopic surveys have revealed a wealth of structure in the phase space of stars in the Milky Way, with evidence of resonance features and non-equilibrium processes. Using Gaia's third data release, we present evidence of a new resonance-like feature in the outer disc of the Milky Way. The feature is most evident in the angular momentum distribution of the young Classical Cepheids, a population for which we can derive accurate distances over much of the Galactic disc. We then search for similar features in the outer disc using a much larger sample of red giant stars, as well as a compiled list of over 31 million stars with spectroscopic line-of-sight velocity measurements. While much less evident in these two older samples, the distribution of stars in action-configuration space suggests that resonance features are present here as well. The position of the feature in action-configuration space suggests that the new feature may be related to the Galactic bar, but other possibilities are discussed.
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

—Environmental perception is an important aspect within the ﬁeld of autonomous vehicles that provides crucial information about the driving domain, including but not limited to identifying clear driving areas and surrounding obstacles. Semantic segmentation is a widely used perception method for self-driving cars that associates each pixel of an image with a predeﬁned class. In this context, several segmentation models are evaluated regarding accuracy and efﬁciency. Experimental results on the generated dataset conﬁrm that the segmentation model FasterSeg is fast enough to be used in realtime on low-power computational (embedded) devices in self-driving cars. A simple method is also introduced to generate synthetic training data for the model. Moreover, the accuracy of the ﬁrst-person perspective and the bird’s eye view perspective are compared. For a 320 × 256 input in the ﬁrst-person perspective, FasterSeg achieves 65 . 44 % mean Intersection over Union (mIoU), and for a 320 × 256 input from the bird’s eye view perspective, FasterSeg achieves 64 . 08 % mIoU. Both perspectives achieve a frame rate of 247 . 11 Frames per Second (FPS) on the NVIDIA Jetson AGX Xavier. Lastly, the frame rate and the accuracy with respect to the arithmetic 16-bit Floating Point (FP16) and 32-bit Floating Point (FP32) of both perspectives are measured and compared on the target hardware.
Needlets Liberated

Spherical needlets were introduced by Narcowich, Petrushev, and Ward to provide a multiresolution sequence of polynomial approximations to functions on the sphere. The needlet construction makes use of integration rules that are exact for polynomials up to a given degree. The aim of the present paper is to relax the exactness of the integration rules by replacing them with QMC designs as introduced by Brauchart, Saff, Sloan, and Womersley (2014). Such integration rules (generalised here by allowing non-equal cubature weights) provide the same asymptotic order of convergence as exact rules for Sobolev spaces $\mathbb{H}^s$, but are easier to obtain numerically. With such rules we construct ``generalised needlets''. The paper provides an error analysis that allows the replacement of the original needlets by generalised needlets, and more generally, analyses a hybrid scheme in which the needlets for the lower levels are of the traditional kind, whereas the new generalised needlets are used for some number of higher levels. Numerical experiments complete the paper.
Semi-supervised 3D Object Detection with Proficient Teachers

Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point cloud is extremely tedious, expensive and time-consuming. To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed. The Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. In this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs. First, to improve the recall of pseudo labels, a Spatialtemporal Ensemble (STE) module is proposed to generate sufficient seed boxes. Second, to improve the precision of recalled boxes, a Clusteringbased Box Voting (CBV) module is designed to get aggregated votes from the clustered seed boxes. This also eliminates the necessity of sophisticated thresholds to select pseudo labels. Furthermore, to reduce the negative influence of wrongly pseudo-labeled samples during the training, a soft supervision signal is proposed by considering Box-wise Contrastive Learning (BCL). The effectiveness of our model is verified on both ONCE and Waymo datasets. For example, on ONCE, our approach significantly improves the baseline by 9.51 mAP. Moreover, with half annotations, our model outperforms the oracle model with full annotations on Waymo.
An elementary alternative to PFH spectral invariants

Inspired by Hutchings' elementary alternative to ECH capacities, we introduce an elementary alternative to spectral invariants defined via periodic Floer homology (PFH). We use these spectral invariants to provide more elementary proofs of a number of results which have recently been obtained using PFH spectral invariants. Among these results are quantitative closing lemmas for area-preserving surface diffeomorphisms and the simplicity conjecture.
Planes in Schatten-$3$

We prove that any two-dimensional real subspace of Schatten-3 can be linearly isometrically embedded into L 3 . This resolves the case p = 3 of Hanner’s inequality for Schatten classes, conjectured by Ball, Carlen and Lieb. We conjecture that similar isometric embedding is possible for any p ≥ 1.
Global Modeling of Nebulae with Particle Growth, Drift, and Evaporation Fronts. III. Redistribution of Refractories and Volatiles

Formation of the first planetesimals remains an unsolved problem. Growth by sticking must initiate the process, but multiple studies have revealed a series of barriers that can slow or stall growth, most of them due to nebula turbulence. In a companion paper, we study the influence of these barriers on models of fractal aggregate and solid, compact particle growth in a viscously evolving solar-like nebula for a range of turbulent intensities α t = 10−5–10−2. Here, we examine how the disk composition in these same models changes with time. We find that advection and diffusion of small grains and vapor, and radial inward drift for larger compact particles and fractal aggregates, naturally lead to diverse outcomes for planetesimal composition. Larger particles can undergo substantial inward radial migration due to gas drag before being collisionally fragmented or partially evaporating at various temperatures. This leads to enhancement of the associated volatile in both vapor inside, and solids outside, their respective evaporation fronts, or snowlines. In cases of lower α t, we see narrow belts of volatile or supervolatile material develop in the outer nebula, which could be connected to the bands of pebbles seen by the Atacama Large Millimeter/submillimeter Array. Volatile bands, which migrate inwards as the disk cools, can persist over long timescales as their gas phase continues to advect or diffuse outward across its evaporation front. These belts could be sites where supervolatile-rich planetesimals form, such as the rare CO-rich and water-poor comets; giant planets formed just outside the H2O snowline may be enhanced in water.
Domain Adaptation under Open Set Label Shift

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p(x|y) are domain-invariant. OSLS subsumes domain adaptation under label shift and Positive-Unlabeled (PU) learning. The learner's goals here are two-fold: (a) estimate the target label distribution, including the novel class; and (b) learn a target classifier. First, we establish necessary and sufficient conditions for identifying these quantities. Second, motivated by advances in label shift and PU learning, we propose practical methods for both tasks that leverage black-box predictors. Unlike typical Open Set Domain Adaptation (OSDA) problems, which tend to be ill-posed and amenable only to heuristics, OSLS offers a well-posed problem amenable to more principled machinery. Experiments across numerous semi-synthetic benchmarks on vision, language, and medical datasets demonstrate that our methods consistently outperform OSDA baselines, achieving 10--25% improvements in target domain accuracy. Finally, we analyze the proposed methods, establishing finite-sample convergence to the true label marginal and convergence to optimal classifier for linear models in a Gaussian setup. Code is available at https://github.com/acmi-lab/Open-Set-Label-Shift.