Miracle-dz/UniFAR-FTUs huggingface dataset:
Comments on the “ Core Vector Machines : Fast SVM Training on Very Large Data Sets ”

In a recently published paper in JMLR, Tsang et al. (2005) present an algorithm for SVM called Core Vector Machines (CVM) and illustrate its performances through comparisons with other SVM solvers. After reading the CVM paper we were surprised by some of the reported results. In order to clarify the matter, we decided to reproduce some of the experiments. It turns out that to some extent, our results contradict those reported. Reasons of these different behaviors are given through the analysis of the stopping criterion.
Dense Error Correction via ` 1-Minimization

This paper studies the problem of recovering a sparse signal x ∈ R from highly corrupted linear measurements y = Ax + e ∈ R, where e is an unknown error vector whose nonzero entries may be unbounded. Motivated by an observation from face recognition in computer vision, this paper proves that for highly correlated (and possibly overcomplete) dictionaries A, any sufficiently sparse signal x can be recovered by solving an `-minimization problem: min ‖x‖1 + ‖e‖1 subject to y = Ax + e. More precisely, if the fraction of the support of the error e is bounded away from one and the support of x is a very small fraction of the dimension m, then as m becomes large the above `-minimization succeeds for all signals x and almost all signand-support patterns of e. This result suggests that accurate recovery of sparse signals is possible and computationally feasible even with nearly 100% of the observations corrupted. The proof relies on a careful characterization of the faces of a convex polytope spanned together by the standard crosspolytope and a set of iid Gaussian vectors with nonzero mean and small variance, dubbed the “cross-and-bouquet” model. Simulations and experiments corroborate the findings, and suggest extensions to the result.
Data Integration and Analysis for Medical Systems Biology

Data Integration, Analysis and Logistics (DIAL), Centre for Medical Systems Biology, Leiden, Rotterdam and Amsterdam, The Netherlands*Correspondence to:Johannes H. G. M. van Beek,Vrije Universiteit, Faculty of Earthand Life Sciences, Department ofMolecular Cell Physiology, DeBoelelaan 1085, 1081 HVAmsterdam, The Netherlands.E-mail: hans.van. beek@falw.vu. nl
Development System for Emotion Detection Based on Brain Signals and Facial Images

Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback. Keywords— Multimodal Emotion Detection, EEG, Facial Image, Optical Flow, compass mapping, Brain Wave
RECENT TRENDS IN PARALLEL COMPUTING

In this paper we have studied the recent trends in parallel computation. A big task that cannot be handled by single CPU can be divided into small number of subtasks which can be processed simultaneously by different processor. At the end these subtasks can be combined together. A parallel computer consists of parallel computing hardware, parallel computing model, software support for parallel programming. This paper explores the different types of parallel computation.
USING SQL DEVELOPER DATA MODELER AND MICROSOFT ACCESS FOR TEACHING DATABASE MODELING AND DESIGN

Entity Relationship Diagrams (ERD) seems like a dying art, but many proponents believe that they should be the starting point of all software development projects. A new data modeling tool developed by Oracle could change the way data modeling in taught in today's classrooms. The powerful and easy to use tool that supports ERD allows a student to learn the entire cycle from logical to relational and finally to the physical design of the database. We present the way the tool was introduced in an introductory database management class. It is our belief that students can develop a higher level of expertise on the difficult concepts of database modeling and design with the aid of this tool.
Efficient algorithms for isomorphisms of simple types

The first-order isomorphism problem is to decide whether two non-recursive types using product- and function-type constructors are isomorphic under the axioms of commutative and associative products, and currying and distributivity of functions over products. We show that this problem can be solved in $O(n \log^2 n)$ time and $O(n)$ space, where $n$ is the input size. This result improves upon the $O(n^2 \log n)$ time and $O(n^2)$ space bounds of the best previous algorithm. We also describe an $O(n)$ time algorithm for the linear isomorphism problem, which does not include the distributive axiom, thereby improving upon the $O(n \log n)$ time of the best previous algorithm for this problem.
- 1201 A 3 D Split Manufacturing Approach to Trustworthy System Development

Securing the supply chain of integrated circuits is of the utmost importance to computer security. In addition to counterfeit microelectronics, the theft or malicious modification of designs in the foundry can result in catastrophic damage to critical systems and large projects. In this Technical Report, we describe a 3D architecture that splits a design into two separate tiers: one tier that contains critical security functions is manufactured in a trusted foundry; another tier is manufactured in an unsecured foundry. We argue that a split manufacturing approach to hardware trust based on 3D integration is viable and provides several advantages over other approaches. Index Terms Hardware-Oriented Security and Trust; 3D Integration; Trustworthy System Development; Policy Enforcement; Cryptographic Hardware; Embedded Systems Security; Malicious Hardware; Trusted Foundries The views expressed this this Technical Report do not represent the official policy of the United States Government, the Department of Defense, or the National Science Foundation. A shorter version of this Technical Report was published as a Transactions Brief in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). 2 NPS TECHNICAL REPORT NO. NPS-CS-12-004
Arch ī um Ateneo Arch um Ateneo

A typical learning management system (LMS) provides a tool for teachers to upload and create links to resources, create online assessments and provide immediate evaluation to students. As much as it tries to be student centered, most LMS remains a tool for instruction rather than learning. In a learning generation that is bound by very high online social capital, connectedness to the family weakens. my.Eskwela (My School) redefines LMS to include a parent component to address the need for inclusive participation of parents in the teaching-learning process. Basis for re-design came from the low user acceptance of teachers in using similar system. The study premised that designing an environment th at evokes a ”feeling of socialness” through social widgets provides a perceived presence of a social environment that will increase usage of the system. In a majority of the focus group discussion, results showed a more positive evaluation of the system. Precisely, for perceived usefulness, perceived ease of use, perceived adoption and intent to use, it can be reasoned that the implementations for reducing the total effort to perform a task and the effect of implementing social interaction in the user-interface has high-impact.
Influencia de El Niño-Oscilación del Sur (ENOS) en la ocurrencia de heladas en el Altiplano Peruano

Las fluctuaciones en los patrones de circulacion atmosferica global, asociados a los cambios en la temperatura superficial del mar (TSM) en el oceano Pacifico, son conocidas como eventos. El Nino-Oscilacion del Sur (ENOS). Estos eventos influyen en el comportamiento de la temperatura y precipitacion. El objetivo principal de este trabajo fue determinar la influencia del ENOS en la ocurrencia de heladas en el Altiplano peruano mediante el analisis de la temperatura minima del aire en el departamento de Puno. Para ello, se aplico el analisis de componentes principales (ACP) para obtener los patrones mas relevantes de la variabilidad y se utilizo el analisis de correlaciones entre los datos en formato grilla de la temperatura minima de Puno y la TSM de la region Nino 3.4. Las dos primeras componentes principales explican el 71.9% de la variancia total de los datos (61.9% la primera y 10% la segunda). El primer componente, manifesto un mismo patron en todo el departamento; mientras que el segundo componente, presento un patron de dipolo entre el sur y la parte centro-norte de Puno. El patron espacial del analisis de correlacion, considerando todos los datos del ano, esta relacionado con el segundo componente principal: existen correlaciones positivas en la parte sur y correlaciones negativas en la parte centro-norte del area de estudio. Sin embargo, esta correlacion no presento significancia estadistica al 95%. El analisis de correlacion separando verano e invierno esta relacionado con el primea componente, con valores positivos de correlacion para el verano y negativos para el invierno. Por tanto, la fase positiva (negativa) del ENOS, asociada al aumento (disminucion) de la TSM en la region Nino 3.4, esta relacionada con una menor (mayor) ocurrencia de heladas en Altiplano peruano durante el verano (invierno) debido al aumento (disminucion) de las temperaturas minimas del aire.
A New Approach for the Fingerprint Classification Based on Gray-Level Co-Occurrence Matrix

classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the co-occurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.
Adaptive Adjustment of the “ Sweet Spot ” to the Listener ’ s Position in a Stereophonic Play Back System-Part 1

The spatial reproduction of sound in a conventional stereo system works in a small area which is located on the symmetry axis between the loudspeakers the so called “sweet spot”. Beyond this area, the spatial perception collapses and the stereo image moves to the nearer loudspeaker since the signal arrives both louder and sooner. Finally, the stereo image is completely located in the nearer loudspeaker due to the precedence effect as can be seen in Figure 1. However, in desktop applications or virtual environments, image and sound have to correlate even though the listener is out of the “sweet spot”. Although, stereophony has a long history, the listener has not yet been released from its static hearing position [1, 2].
2 . ZCS : a Zeroth level Learning Classifier System

Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism for effective learning without external reward is examined which enables the simple learning system to build a full map of the task. That is, ZCS is shown to learn under a latent learning scenario using the lookahead scheme. Its ability to form maps in reinforcement learning tasks is then considered. keywords: animat, genetic algorithm, internal models, learning classifier system, multiplexer task.
Your Title

This paper examines event-triggered consensus of multi-agent systems on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the trigger threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed eventtriggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrixweighted networks, respectively. It is shown that that the spectral properties of matrix-valued weights are crucial in event-triggered mechanism design for matrix-weighted networks. Finally, simulation examples are provided to demonstrate the theoretical results.
Adaptive linear models in regression for the modeling of housing market in different U.S. cities

The general setting of regression analysis is to identify a relationship between a response variable $Y$ and one or several explanatory variables $\textbf{X}$ by using a learning sample. In a prediction framework, the main assumption for predicting $Y$ on a new sample of $\textbf{X}$ observations is that the regression model $Y=f(\textbf{X})+\epsilon$ is still valid. Unfortunately, this assumption is not always true in practice. We therefore propose to adapt the original regression model to the new sample by estimating a transformation between the original regression function $f(\textbf{X})$ and the new one $f^{*}(\textbf{X})$. The interest of this work is illustrated by an application to the modeling of housing market in different U.S. cities in which the regression model of a reference city is adapted to another city.
Review of pollutants in urban road dust: Part II. Organic contaminants from vehicles and road management

ABSTRACT Urban road dust is highly enriched with organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), petroleum hydrocarbons, and herbicides that are released primarily from vehicles and/or road management practices. Analysis of sediment cores from urban watersheds clearly demonstrates that increase of pollutant input correlates with traffic volume increase. Pollutants in urban road dust are a significant threat to the health of aquatic organisms. Contaminated urban road dust is mobilized by stormwater runoff and transported into local receiving waterbodies (e.g. streams, rivers, lakes, and estuaries). Concentrations of PAHs in sediments receiving stormwater runoff from densely populated urban areas are high enough to impair the health of aquatic organisms and frequently exceed sediment quality guidelines. To restore streams and rivers impaired by roadway runoff, concentrations of pollutants in road dust need to be reduced through implementing a combination of regulatory policies and management actions. One approach would be to phase out existing pollutant sources such instituting a ban on the use of coal tar-based asphalt road seal coat containing high levels of PAHs. Instituting the use of environment friendly natural herbicides with only spot treatment to target weeds rather than broadcast application and restoration of native vegetation, as integrated roadside vegetation management programs. Adoption of these practices would result in significantly reduced herbicide contamination of roadway stormwater runoff. An alternate option is the removal of contaminants from stormwater runoff before they are delivered to receiving waterbodies using best management practices (BMP) such as retention ponds, detention basins, and grass swales. In densely populated urban areas, however, these BMPs may not be easily adopted due to multiple constraints such as lack of spaces and high land cost. In this case, proprietary BMPs such as media filter, wet vault, and vortex separator, which are also known as manufactured treatment devices, can be considered.
Extending the Range of Distances Accessible by 19F Electron–Nuclear Double Resonance in Proteins Using High-Spin Gd(III) Labels

Fluorine electron-nuclear double resonance (19F ENDOR) has recently emerged as a valuable tool in structural biology for distance determination between F atoms and a paramagnetic center, either intrinsic or conjugated to a biomolecule via spin labeling. Such measurements allow access to distances too short to be measured by double electron–electron resonance (DEER). To further extend the accessible distance range, we exploit the high-spin properties of Gd(III) and focus on transitions other than the central transition (|−1/2⟩ ↔ |+1/2⟩), that become more populated at high magnetic fields and low temperatures. This increases the spectral resolution up to ca. 7 times, thus raising the long-distance limit of 19F ENDOR almost 2-fold. We first demonstrate this on a model fluorine-containing Gd(III) complex with a well-resolved 19F spectrum in conventional central transition measurements and show quantitative agreement between the experimental spectra and theoretical predictions. We then validate our approach on two proteins labeled with 19F and Gd(III), in which the Gd–F distance is too long to produce a well-resolved 19F ENDOR doublet when measured at the central transition. By focusing on the |−5/2⟩ ↔ |−3/2⟩ and |−7/2⟩ ↔ |−5/2⟩ EPR transitions, a resolution enhancement of 4.5- and 7-fold was obtained, respectively. We also present data analysis strategies to handle contributions of different electron spin manifolds to the ENDOR spectrum. Our new extended 19F ENDOR approach may be applicable to Gd–F distances as large as 20 Å, widening the current ENDOR distance window.
Association of non-obstructive dyspnoea with all-cause mortality and incident chronic obstructive pulmonary disease: a systematic literature review and meta-analysis

Background Controversy exists regarding the association between non-obstructive dyspnoea and the future development of chronic obstructive pulmonary disease (COPD) and mortality. Therefore, we aimed to evaluate the association of non-obstructive dyspnoea with mortality and incident COPD in adults. Methods We searched PubMed, Embase, and Web of Science to identify studies published from inception to 13 May 2023. Eligibility screening, data extraction, and quality assessment of the retrieved articles were conducted independently by two reviewers. Studies were included if they were original articles comparing incident COPD and all-cause mortality between individuals with normal lung function with and without dyspnoea. The primary outcomes were incident COPD and all-cause mortality. The secondary outcome was respiratory disease-related mortality. We used the random-effects model to calculate pooled estimates and corresponding 95% confidence interval (CI). Heterogeneity was determined using the I² statistic. Results Of 6486 studies, 8 studies involving 100 758 individuals fulfilled the inclusion and exclusion criteria and were included in the study. Compared with individuals without non-obstructive dyspnoea, individuals with non-obstructive dyspnoea had an increased risk of incident COPD (relative risk: 1.41, 95% CI: 1.08 to 1.83), and moderate heterogeneity was found (p=0.079, I2=52.2%). Individuals with non-obstructive dyspnoea had a higher risk of all-cause mortality (hazard ratio: 1.21, 95% CI: 1.14 to 1.28, I2=0.0%) and respiratory disease-related mortality (hazard ratio: 1.52, 95% CI: 1.14 to 2.02, I2=0.0%) than those without. Conclusions Individuals with non-obstructive dyspnoea are at a higher risk of incident COPD and all-cause mortality than individuals without dyspnoea. Further research should investigate whether these high-risk adults may benefit from risk management and early therapeutic intervention. PROSPERO registration number CRD42023395192.
LRPL-VIO: A Lightweight and Robust Visual–Inertial Odometry with Point and Line Features

Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases. In this paper, we propose a novel lightweight point–line visual–inertial odometry algorithm to solve this problem, called LRPL-VIO. Firstly, a fast line matching method is proposed based on the assumption that the photometric values of endpoints and midpoints are invariant between consecutive frames, which greatly reduces the time consumption of the front end. Then, an efficient filter-based state estimation framework is designed to finish information fusion (point, line, and inertial). Fresh measurements of line features with good tracking quality are selected for state estimation using a unique feature selection scheme, which improves the efficiency of the proposed algorithm. Finally, validation experiments are conducted on public datasets and in real-world tests to evaluate the performance of LRPL-VIO and the results show that we outperform other state-of-the-art algorithms especially in terms of speed and robustness.
Connected Care Reducing Errors Through Automated Vital Signs Data

Technology use has become an important safety strategy for healthcare providers and has transformed their work environment. With landmark reports by the Institute of Medicine, new patient safety standards from the Joint Commission, and the 5 Million Lives Campaign by the Institute for Healthcare Improvement, patient safety in the healthcare industry is at the forefront of importance worldwide. Healthcare organizations are deploying technology systems that assist with positive patient identification and seamlessly integrate clinical information in real time. More than half of US hospitals surveyed by the American Hospital Association have adopted bar-coding technology for at least one purpose, such as matching patients to their laboratory specimens and drugs. Hospitals have taken great strides to reduce preventable medical mistakes from medication errors, wrongful surgery, and other catastrophes. However, there has been little attention focused on an essential component of patient assessment—the documentation of vital signs. Vital signs are perhaps the most fundamental component of patient evaluation. They provide the basis for clinical decision making regarding treatments, interventions, progress, and discharge within the hospital environment. As such, vital sign data should be accurate and quickly accessible to make safe therapeutic decisions. Healthcare personnel spend a considerable portion of their workday obtaining and documenting this clinical information necessary for patient care. Despite the importance placed on vital sign values, a search of the literature failed to produce studies reporting vital sign documentation accuracy for either paper-based or electronic transcription other than the baseline results used for this