Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationships between families of thin sets

We show tha t the families of thin sets Vf , pVf ,Bf,Nf,Af, and wVf coincide with corresponding trigonometric families when Z ( / ) is a finite set of rationals. By a theorem of [BUKOVSKA, Z. —BUKOVSKY, L. : Comparing families of thin sets, Real Anal. Exchange 27 (2001/2002), 609-625] this is not true for the families Bof and Mof . We start with some notations. We work on the topological group T -= R/Z often identified with intervals (—1/2,1/2) or (0,1). Throughout the paper, / , g will denote continuous functions satisfying L5:T->(0,+oo), 0 G Z ( / ) , Z ( 5 ) ^ T , (1) where Z(/) = {:reT: f(x) = 0} is the zero set of the function / . The functions / , g are supposed to be periodically extended to the set R. For convenience we define fn( ) = f() for ^In our paper [BZ] we have introduced families of thin sets V,, pVf, B0f, Af0f, Bf, Aff, Af, and wVf in a similar way as one defines the trigonometric families of thin sets X>, pX>, B0, Af0, B, A/", A, and wV in the case 1 f(x) = \\x\\ or equivalently f(x) = |sin(7rx)|. We have shown that those families possess properties similar to those of trigonometric ones. The first important problem is 2000 M a t h e m a t i c s S u b j e c t C l a s s i f i c a t i o n : Pr imary 42A20; Secondary 42A28, 26A99. K e y w o r d s : trigonometric thin set, family of thin sets. The work on this research has been supported by the Grant 1/7555/20 of Slovenska grantova agentiira. 1 | |x | | is the distance of the real x from the nearest integer.
Pronominal and clitic systems in Taqbaylit Berber

This dissertation investigates pronominal and clitic systems in Taqbaylit Berber (Afro-asiatic) from the point of view of their syntactic, semantic and interpretative properties. The thesis's contribution to current research is two-fold. First, a detailed analysis of Taqbaylit as used in spoken discourse is provided along with an exploration of clausal and nominal structures. In particular, the TAM system is explored and arranged within an extended event structure as proposed by Tenny (2000). An in-depth analysis of the various orders in which DP elements are placed and a proposal on the internal structure of the constituent, based on Cinque's universal DP template (1996; 2000; 2005) is also proposed. Secondly, an alternative account of clitic orderings exploiting hierarchical partitions of pronominal forms and a comprehensive and systematic organization of the pronominal system of Taqbaylit Berber focusing on the syntax and semantics/pragmatics of clitics and non-clitic related pro-forms is given. Within the frameworks aforementioned, clitic placements in CP and DP are argued to be derived in two steps. At the syntactic level, clitics are argued to move as phrases to the highest functional projection realized by the lexical head they are associated with. At PF, clitics are argued to incorporate into an adjacent preceding prosodic head or if no such head is available to the following lexical head. Enclitic orders with nominal and verbal heads are further derived by a clitic-host inversion. From the point of view' of typology, it is shown that the pronominal organization of Taqbaylit conforms to independently proposed hierarchical classifications of pronominal forms into different classes or categories (e.g. Cardinaletti & Starke, 1999 and Dechaine & Witlschko, 2002).
Lecture Notes On Symmetries And Curvature Structure In General Relativity

Thank you very much for downloading lecture notes on symmetries and curvature structure in general relativity. Maybe you have knowledge that, people have look numerous times for their chosen novels like this lecture notes on symmetries and curvature structure in general relativity, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their desktop computer.
Electromagnetic simulations for salinity index error estimation

Soil salinity index (SI) is a measure of salt concentration in soil water. The salinity index is calculated as a partial derivative of the soil bulk electrical conductivity (EC) with respect to the bulk dielectric permittivity (DP). The paper focused on the impact of different sensitivity zones for measured both EC and DP on the salinity index determination accuracy. For this purpose, a set of finite difference time domain (FDTD) simulations was prepared. The simulations were carried out on the model of a reflectometric probe consisting of three parallel rods inserted into a modelled material of simulated DP and EC. The combinations of stratified distributions of DP and EC were tested. An experimental verification of the simulation results on selected cases was performed. The results showed that the electromagnetic simulations can provide useful data to improve accuracy of the determination of soil SI.
Resonance Raman of BCC and normal skin

The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
A Molecular Disk Survey of Low-Mass Stars in the TW Hya Association

Abstract We have carried out an ALMA Cycle 2 survey of 15 confirmed or candidate low-mass (<0.2M⊙) members of the TW Hya Association (TWA) with the goal of detecting line emission from CO molecular gas and continuum emission from cold dust. Our targets have spectral types of M4-L0 and hence represent the extreme low end of the TWA's mass function. The survey has yielded a detection of 12CO(2–1) emission around TWA 34. This newly discovered ~10 Myr-old molecular gas disk lies just ~50pc from Earth.
Chebyshev Spectral Analysis For Wave Propagation in Thin Plates

In the present paper we consider a Chebyshev spectral analysis to solve the three dimensional Lamb’s problem in an homogeneous plate. All aspects of the statement and the discretization of the continuous problem related to the wave propagation in the plate are shown. We compare our methodology with previous formulations as the finite element method and finite difference method. The improving in numerical accuracy of the Chebyshev spectral method is presented. The diagonality of the global mass matrix obtained by using Legendre polynomials is an advantage due to the reduction of the calculation time required for marching the time integration scheme. In this contribution the reduction in calculation
Hybrid Machine Learning/Knowledge Base Systems Learning through Natural Language Dialogue with Deep Learning Models

Neurosymbolic approaches to AI typically involve attempts to reincorporate the structure and speed of symbolic reasoning into the flexible representations of deep learning. “Knowledge,” in this understanding, is typically represented in a structured ontology or knowledge base that relies on human expertise and effort to construct. In this paper, we present a vision for “language-endowed intelligent agents,” a type of lifelong learner which begins with a hand-crafted knowledge base and a deep language understander and increases it over the course of its life through dialogue and interaction with both humans and other AI systems—generative large language learning models in particular. We discuss the requirements for such a system, present evidence toward the feasibility of the approach, and conclude with future challenges and research directions.
Smooth maximum based algorithms for classification, regression, and collaborative filtering

Unbalanced classification problems are quite common in the practice of machine learning. Unbalancedness means that the distribution of the class labels is far from uniform. Convex polyhedron classifiers are special binary classifiers that fit well to unbalanced problems. In this paper I propose novel and computationally efficient algorithms for training convex polyhedron classifiers. The proposed algorithms are based on the smooth approximation of the maximum function. I also give the analogous variant of the approach for regression and collaborative filtering. Finally, I demonstrate the usefulness of the approach via experiments on artificial and real datasets.
Robust Time-Optimal Control for the One-Dimensional Optical Lattice for Quantum Computation

Quantum information is a growing field showing exciting possibilities for computational speed-up and communications. For the successful implementation of quantum computers, high-precision control is required to reach fault-tolerant thresholds. Control of quantum systems pertains to the manipulation of states and their evolution. In order to minimize the effects of the environment on the control operations of the qubits, control pulses should be made time-optimal. In addition, control pulses should be made robust to noise in the system, dispersion in energies and coupling elements, and uncertain parameters. In this thesis, we examine a robust time-optimal gradient ascent technique which is used to develop controls of the motional degrees of freedom for an ensemble of neutral atoms in a one-dimensional optical lattice in the high dispersion regime with shallow trapping potentials. As such, the system is analyzed in the delocalized basis. The system is treated as an ensemble of atoms with a range of possible quasimomenta across the first Brillouin zone. This gives the ensemble of Hamiltonians, indexed by the quasimomenta, a distinct spectra in their motional states and highly inhomogeneous control Hamiltonians. Thus, the optical lattice is seen as a model system for robust control. We find optimized control pulses designed using an ensemble modification of gradientascent pulse engineering robust to any range of quasimomentum. We show that it is possible to produce rotation controls with fidelities above 90% for half of the first Brillouin zone with gate times in the order of several free oscillations. This is possible for a spectrum that shows upwards of 75% dispersion in the energies of the band structure. We also show that NOT controls for qubit rotations on the entire Brillouin zone fidelities above 99% were possible for 0.6% dispersion in energies. The gate times were also in the order of several free oscillations. It is shown that these solutions are palindromic in time due to phase differences in some of the energy couplings when comparing one half of the Brillouin zone to another. We explore the limits of discretized sampling of a continuous ensemble for control.
Mining Periodic Patterns from Non-binary Transactions

Pattern with time period is more valuable because it can better describe objective knowledge. Previous studies on periodic patterns from market basket data focus on patterns without considering the items with their purchased quantities. But in real-life transactions, an item could be purchased multiple times in a transaction and different items may have different quantity in the transactions. To solve this problem, we incorporate the concept of transaction frequency (TF) and database frequency (DF) of an item in a time interval. Our algorithm works in two phases. In first phase we mined locally frequent item sets along with the set of intervals and their database frequency range and second phase mines the two types of periodic patterns (cyclic and acyclic) from the list of intervals. Experimental results are provided to validate the study.
Evidence rebuts Chomsky's theory of language learning

The idea that we have brains hardwired with a mental template for learning grammar — famously espoused by Noam Chomsky of the Massachusetts Institute of Technology — has dominated linguistics for almost half a century. Recently, though, cognitive scientists and linguists have abandoned Chomsky’s “universal grammar” theory in droves because of new research examining many different languages — and the way young children learn to understand and speak the tongues of their communities. That work fails to support Chomsky’s assertions.
Leveraging Label Variation in Large Language Models for Zero-Shot Text Classification

The zero-shot learning capabilities of large language models (LLMs) make them ideal for text classiﬁcation without annotation or supervised training. Many studies have shown impressive results across multiple tasks. While tasks, data, and results differ widely, their similarities to human annotation can aid us in tackling new tasks with minimal expenses. We evaluate using 5 state-of-the-art LLMs as “annotators” on 5 different tasks (age, gender, topic, sentiment prediction, and hate speech detection), across 4 languages: English, French, German, and Spanish. No single model excels at all tasks, across languages, or across all labels within a task. However, aggregation techniques designed for human annotators perform substantially better than any one individual model. Overall, though, LLMs do not rival even simple supervised models, so they do not (yet) replace the need for human annotation. We also discuss the trade-offs between speed, accuracy, cost, and bias when it comes to aggregated model labeling versus human annotation.
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation

Reinforcement learning is time-consuming for complex tasks due to the need for large amounts of training data. Recent advances in GPU-based simulation, such as Isaac Gym, have sped up data collection thousands of times on a commodity GPU. Most prior works have used on-policy methods like PPO due to their simplicity and easy-to-scale nature. Off-policy meth-ods are more sample-efficient, but challenging to scale, resulting in a longer wall-clock training time. This paper presents a novel Parallel Q -Learning (PQL) scheme that outperforms PPO in terms of wall-clock time and maintains superior sample efficiency. The driving force lies in the parallelization of data collection, policy function learning, and value function learning. Different from prior works on distributed off-policy learning, such as Apex, our scheme is designed specifically for massively parallel GPU-based simulation and optimized to work on a single workstation. In experiments, we demonstrate the capability of scaling up Q -learning methods to tens of thousands of parallel environments and investigate important factors that can affect learning speed, including the number of parallel environments, exploration strategies, batch size, GPU models, etc. The code is available at https://github.com/Improbable-AI/pql.
Rapidly progressive renal dysfunction in systemic sclerosis Scleroderma renal crisis

Systemic sclerosis is an autoimmune disorder involving skin, gastro-intestinal tract, lungs, kidneys and heart. Rapidly progressive renal dysfunction and accelerated hypertension remains the hallmark for scleroderma renal crisis in patients of Systemic sclerosis. Here we present a case of 38 year old male patient who presented with clinical signs of systemic sclerosis and unexplained renal dysfunction associated with hypertension. A provisional of Scleroderma renal crisis was expected but renal biopsy, surprisingly, revealed features suggestive of Crescentric Glomerulonephritis.
Email and Website-Based Phishing Attack: Examining Online Users Security Behavior in Cyberspace Environment

Despite Emails and websites being widely used for communication, collaboration, and day-to-day activity, not all online users have the same knowledge and skills when determining the credibility of visited websites and email content. As a result, phishing, an identity theft cyber-attack that targets humans rather than computers, was born to harvest internet users' confidential information by taking advantage of human behavior and hurting an organization's continuity, reputation, and credibility. Because the success of phishing attacks depends on human behavior, using the Health-Belief Model, the study's objective is to examine significant factors that influence online users' security behavior in the context of Email and website-based phishing attacks. The model included eight predictor variables and was validated using quantitative data from 138 academic staff. The study findings exhibit that 4 out of 8 predictor variables, namely Perceived-Barriers, Perceived-Susceptibility, Self-efficacy, and Security-Awareness, are statistically significant in determining users' security behavior. The study's outcome is to assist in the appropriate design of both online and offline content for cyber security awareness programs, focusing on Email and website-based phishing attacks
A Modified Stacking Ensemble Machine Learning Algorithm Using A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms Genetic Algorithms

With the massive increase in the data being collected as a result of ubiquitous information gathering devices, and the increased need for doing data mining and analyses, there is a need for scaling up and improving the performance of traditional data mining and learning algorithms. Two related fields of distributed data mining and ensemble learning aim to address this scaling issue. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by combining several classifiers created on the same data and improve their performance. In this paper we use concepts from both of these fields to create a modified and improved version of the standard stacking ensemble learning technique by using a genetic algorithm (GA) for creating the meta-classifier. We use concepts from distributed data mining to study different ways of distributing the data and use the concept of stacking ensemble learning to use different learning algorithms on each sub-set and create a meta-classifier using a genetic algorithm. We test the GA-based stacking algorithm on ten data sets from the UCI Data Repository and show the improvement in performance over the individual learning algorithms as well as over the standard stacking algorithm.
Herbal Medicine in the Treatment of Alzheimer’s Disease and Dementia: Phytoconstituent & Their Possible Pharmacological Activities

Alzheimer’s Disease (AD) is a chronic, progressive neu - rological illness that is linked to ageing and is characterised by memory and cognition problems that progress to death. The pathogenicity is defined by tau protein hyperphosphor - ylation in neurons and the buildup of Amyloid-Beta Plaques (Aβ) and Neurofibrillary Tangles (NFT) in brain tissues, and the main cause is the oxidative stress-induced generation of Reactive Oxygen Species (ROS). There is no cure for Al - zheimer’s disease as of yet, while symptomatic treatment may have some potential for memory enhancement and other concerns associated with the disease, as well as being a preventative for some neurological disorders. Many natu - ral products, such as medicinal plants and herbal remedies, have been used for the treatment of various memory disor - ders such as short-term memory loss, dementia, Alzheim - er’s, and Parkinson’s disease for a long time, and a number of medicinal plants, either in their crude forms or as isolated compounds, have shown to reduce the pathological charac - teristics of the disease, and various studies have depicted the use of therapeutically derived medicinal plants. An at - tempt has been made in this review to clarify the molecular method of action of numerous plant extracts and phyto - chemicals that have been tested against AD.
Structural Changes of Cutaneous Immune Cells in Patients With Type 1 Diabetes and Their Relationship With Diabetic Polyneuropathy

Background and Objectives Diabetic polyneuropathy (DPN) is a complication of diabetes characterized by pain or lack of peripheral sensation, but the underlying mechanisms are not yet fully understood. Recent evidence showed increased cutaneous macrophage infiltration in patients with type 2 diabetes and painful DPN, and this study aimed to understand whether the same applies to type 1 diabetes. Methods The study included 104 participants: 26 healthy controls and 78 participants with type 1 diabetes (participants without DPN [n = 24], participants with painless DPN [n = 29], and participants with painful DPN [n = 25]). Two immune cells, dermal IBA1+ macrophages and epidermal Langerhans cells (LCs, CD207+), were visualized and quantified using immunohistological labeling and stereological counting methods on skin biopsies from the participants. The IBA1+ macrophage infiltration, LC number density, LC soma cross-sectional area, and LC processes were measured in this study. Results Significant difference in IBA1+ macrophage expression was seen between the groups (p = 0.003), with lower expression of IBA1 in participants with DPN. No differences in LC morphologies (LC number density, soma cross-sectional area, and process level) were found between the groups (all p > 0.05). In addition, IBA1+ macrophages, but not LCs, correlated with intraepidermal nerve fiber density, Michigan neuropathy symptom inventory, (questionnaire and total score), severity of neuropathy as assessed by the Toronto clinical neuropathy score, and vibration detection threshold in the whole study cohort. Discussion This study showed expressional differences of cutaneous IBA1+ macrophages but not LC in participants with type 1 diabetes–induced DPN compared with those in controls. The study suggests that a reduction in macrophages may play a role in the development and progression of autoimmune-induced diabetic neuropathy.
Influence of interaural time differences on loudness for low-frequency pure tones at varying signal and noise levels

Directional loudness sensitivity, which is generally accounted for by at-ear pressure modifications because of the perturbation of the sound field by the head, has been reported to occur at 400 Hz where shadowing effects are usually considered small. Then, an effect of the interaural time difference (ITD) on loudness has been observed for pure tones below 500 Hz. The latter was rather small but still significant, contributing to directional loudness sensitivity. In addition, it has been shown that the effect of ITD on loudness was caused by the ITD itself and not by its related localization. As this effect appeared significant at low level only (40 phon), it was hypothesized that ITD could help separate the signal from the internal noise and enhance its loudness. The aim of the present study is to confirm this hypothesis by observing the effect of ITD on the loudness of low-frequency pure tones (100 and 200 Hz) for various signal-to-noise ratios. The signal level was varied from 30 to 90 phon and the nois.