Miracle-dz/UniFAR-FTUs huggingface dataset:
Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.
Spatial Hashtags in Tweets

Twitter is an on-line social networking service which enables users to communicate by sending and reading up to 140 characters short text called ”tweets”. Users can attach any hashtag, starting with an ”#”, to tweets to indicate additional or summary information. tweets with explicit or implicit spatial intent can be classified as spatial tweets. Spatial tweets are commonly seen in practice, and it is important if we can discover such tweets and further understand the spatial information behind them. However, recognizing spatial tweets is very challenging since there is usually little spatial information in the short tweet text. In this paper, we design a Bootstrapping framework to automatically mine spatial hashtags from tweets corpus. This framework consists of three key components: PU Learning for classifier training, spatial tweets discovery and heuristic spatial hashtag extraction. Then we will show how to mine spatial hashtags from tweets with this framework and how to recommend spatial hashtags to tweets.
Chapter 9 GENERALIZED METHOD OF MOMENTS

To motivate a GMM estimation, consider the nonlinear Euler equation of Hansen and Singleton (1982), E[βc −α t+1 R t+1 | I t ] c −α t = 1, (9.1) where R t+1 is the gross real return of any asset, c t is real consumption at time t, β is a discount factor, α is a constant relative risk aversion coefficient, and I t is an information set available at time t. This Euler equation can be rearranged as E[β(c t+1 c t) −α R t+1 − 1| I t ] = 0. (9.2) Let z t be a vector of variables whose values are known at time t, then z t ∈ I t and E[z t {β(c t+1 c t) −α R t+1 − 1}| I t ] = 0. By the law of iterative expectations, we obtain the orthogonality conditions to be used in GMM estimation, E[z t {β(c t+1 c t) −α R t+1 − 1}] = 0.
HotLeakage: A Temperature-Aware Model of Subthreshold and Gate Leakage for Architects

This report introduces HotLeakage, an architectural model for subthreshold and gate leakage that we have developed here at the University of Virginia. The most important features of HotLeakage are the explicit inclusion of temperature, voltage, gate leakage, and parameter variations, and the ability to recalculate leakage currents dynamically as temperature and voltage change due to operating conditions, DVS techniques, etc. HotLeakage provides default settings for 180nm through 70nm technologies for modeling cache and register files, and provides a simple interface for selecting alternate parameter values and for modeling alternative microarchitecture structures. It also provides models for several extant cache leakage control techniques, with an interface for adding further techniques. HotLeakage is currently a semi-independent module for use with SimpleScalar, but is sufficiently modular that it should be fairly easy to port to other simulators. Because sub-threshold leakage currents are exponentially dependent on temperature and voltage, because gate leakage is growing so rapidly, and because parameter variations can have a profound effect on simulation accuracy, we hope that HotLeakage will serve as a useful tool for microarchitects to more accurately evaluate issues related leakage power. HotLeakage is available for download athttp://lava.cs. virginia.edu/HotLeakage
Carbapenem-Resistant Hypervirulent Klebsiella pneumoniae of Sequence Type 36

ABSTRACT Sequence type 36 (ST36) Klebsiella pneumoniae is distributed worldwide. We found an ST36 K. pneumoniae clinical isolate that was carbapenem resistant, carried blaKPC-2, had mucoid regulator gene rmpA, and exhibited high virulence. The finding suggests the emergence of carbapenem-resistant hypervirulent K. pneumoniae of ST36, and surveillance of carbapenem-resistant hypervirulent K. pneumoniae is required.
Design and implementation of IMU-based human arm motion capture system

This paper presents the design of a generic inertial measurement unit (IMU) module for motion capture system. The module consists of 32-bit microcontroller with gyroscope, accelerometer, and magnetometer to provide orientation estimation of human limbs. An orientation estimation algorithm that compensates magnetic distortion is implemented. Our main contribution is on the sensor network which is low cost but high speed. We developed a serial-chain network for sensors interconnection in which UART communication is used. We also consider frame alignment issue in developing human arm motion capture to improve tracking accuracy. A simple frame calibration method is implemented and tested.
Distributed under Creative Commons Cc-by 4.0 the Relationship between Mobile Phone Location Sensor Data and Depressive Symptom Severity

Background. Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify depressive symptom severity. Methods. We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. Results. The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from −0.43 to −0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. Discussion. Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of depressive symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
Effects of Atelocollagen Formulation Containing Oligonucleotide on Endothelial Permeability

Atelocollagen is a major animal protein that is used as a highly biocompatible biomaterial. To date, atelocollagen has been used as an effective drug delivery technology to sustain the release of antitumor proteins and to enhance the antitumor activity of oligonucleotides in in vivo models. However, the biological effects of this technology are not fully understood. In the present study, we investigated the effects of atelocollagen on endothelial paracellular barrier function. An atelocollagen formulation containing oligonucleotides specifically increased the permeability of two types of endothelial cells, and the change was dependent on the molecular size, structure of the oligonucleotides used and the concentrations of the oligonucleotide and atelocollagen in the formulation. An immunohistochemical examination revealed that the formulation had effects on the cellular skeleton and intercellular structure although it did not affect the expression of adherens junction or tight junction proteins. These changes were induced through p38 MAP kinase signaling. It is important to elucidate the biological functions of atelocollagen in order to be able to exploit its drug delivery properties.
A pharmacokinetic/pharmacodynamic model developed for the effect of colistin on Pseudomonas aeruginosa in vitro with evaluation of population pharmacokinetic variability on simulated bacterial killing.

OBJECTIVES An optimized dosing regimen of the prodrug of colistin, colistin methanesulphonate (CMS), against resistant Pseudomonas aeruginosa is needed to ensure effective bacterial killing. The objectives of this study were to develop a pharmacokinetic (PK)/pharmacodynamic (PD) model that characterizes the time course of the antibacterial activity of colistin against P. aeruginosa in a static in vitro system and to perform simulations of different dosing regimens and dosing algorithms to evaluate the effect of interindividual variability and interoccasion variability in PK on bacterial killing. METHODS Static in vitro time-kill curve experiments were conducted on two different strains of P. aeruginosa (MIC 1 and 1.5 mg/L). Mechanism-based PK/PD models were fitted in NONMEM7 and the final model was combined with a previously developed population PK model of CMS and colistin to perform simulations of variability based on different dosing algorithms. RESULTS A model with compartments for growing and resting bacteria, with a function allowing the maximal bacterial killing of colistin to reduce upon increasing colistin exposure, characterized both the fast bactericidal effect and the adaptive resistance. The variability in PK was shown to translate into pronounced interoccasion variability in bacterial killing. A flat fixed loading dose was demonstrated to result in less variability than an algorithm based on weight. CONCLUSIONS The developed PK/PD model described the growth, death and resistance development of P. aeruginosa in response to colistin for two different strains. Based on simulations, a flat fixed loading dose followed by an 8 or 12 hourly maintenance dose with an infusion duration of up to 2 h appeared adequate.
Combining Mouse and Keystroke Dynamics Biometrics for Risk-Based Authentication in Web Environments

Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed, and as such, put in question the robustness and reliability of the proposed systems. In this paper, we propose a new online risk-based authentication system that provides more robust user identity information by combining mouse dynamics and keystroke dynamics biometrics in a multimodal framework. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 8.21%, which is promising considering that we are dealing with free text and free mouse movements, and the fact that many web sessions tend to be very short.
Discovering Mammalian Endocytic Pathways with High-Throughput Image-Based siRNA Screens and Network Analysis

Introduction Research on membrane trafficking has generated many key insights into the molecular machinery of individual events. However, comprehensive and quantitative models that describe membrane trafficking in a mammalian cell as one system are lacking. The main workhorses to discover these pathways in this research are mammalian viruses, which have co-evolved with mammalian cells and, therefore, know all the entry routes and organelles constituting the endocytic membrane system. Viruses are easily visualized and followed by means of various light microscopy techniques. In addition, successful infectious entry is an easy and robust readout for high-throughput screens. (Pelkmans et al (2005))
Magnetoresistance of polycrystalline Fe3O4 films prepared by reactive sputtering at room temperature

The magnetic, structural, and transport properties of single-layer magnetite (Fe3O4) films prepared by reactive sputtering were investigated. Magnetoresistance (MR) was measured at various thicknesses and temperatures. The increase in MR with thickness is related to grain crystallinity and size, as confirmed by transmission electron microscopy. MR arises from intergranular tunneling, which is supported by the temperature dependence of resistivity (logρ∼T−1∕2). Field-dependent MR correlates with the M curve. Magnetoresistance versus magnetization curves clearly show that the MR effects come from the surface spin arrangement near the grain boundaries. The dependence of MR on the magnetic field observed in polycrystalline Fe3O4 films can be attributed to a surface magnetization near the grain boundary, which will be discussed.
38 c 0038 The Emotional Systems

reward and punishment representations in the human orbitofrontal cortex, Nature Neurosci 4:95–102, 2001. O’Doherty J, Rolls ET, Francis S, Bowtell R, McGlone F, Kobal G, Renner B, Ahne G: Sensory-specific satiety related olfactory activation of the human orbitofrontal cortex, NeuroReport 11:893–897, 2000. O’Doherty J, Rolls ET, Francis S, Bowtell R, McGlone F: The representation of pleasant and aversive taste in the human brain, J Neurophysiol 85:1315–1321, 2001. O’Doherty JP, Deichmann R, Critchley HD, Dolan RJ: Neural responses during anticipation of a primary taste reward, Neuron 33:815–826, 2002. O’Doherty J, Winston J, Critchley H, Perrett D, Burt DM, Dolan RJ: Beauty in a smile: the role of medial orbitofrontal cortex in facial attractiveness, Neuropsychologia 41:147–155, 2003. Olausson H, Lamarre Y, Backlund H, Morin C, Wallin BG, Starck G, Ekholm S, Strigo I, Worsley K, Vallbo AB, Bushnell MC: Unmyelinated tactile afferents signal touch and project to insular cortex, Nat Neurosci 5:900–904, 2002. Öngür D, Price JL: The organisation of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans, Cereb Cortex 10:206–219, 2000. 38. THE EMOTIONAL SYSTEMS 1334
mSieve: differential behavioral privacy in time series of mobile sensor data

Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.
A COMPARISON OF IMAGE PROCESSING TECHIQUES AND NEURAL NETWORKS FOR AN AUTOMATED VISUAL INSPECTION PROBLEM1

The development of automated visual inspection systems generally involves a time consuming investigation of appropriate preprocessing and ltering techniques, a search for`good' features and selection of a suitable classiier for the feature vectors. An alternative approach which has the potential of avoiding these problems is the classiication of raw pixel data by the use of neural networks. We have compared these two approaches on the problem of detecting bacterial growths on microbiology plates. This problem requires signiicant human expertise and judgement as there is great variability in the sizes, shapes and colours of the growths. We have examined a number of methods from image processing/computer vision | thresholding, 1d entropy, 2d entropy, region growing and template matching | for this problem and compared them with a number of feed forward multilayer neural networks of diierent architectures. We found that the neural networks using raw pixel data were superior for this problem. The best classiiers from each approach gave virtually identical classiication accuracy (over 99%). However the neural net classiiers generally had much faster execution times and required considerably less development time.
Embodied Cognition and the Percept / Concept Distinction

ed perceptual schemata. Through a process of selective attention that breaks down and abstracts perceptual traces into symbols, the schemata can co me t represent abstractions like shapes independent of orientation. Further details on the abstraction mec hanism are not provided. In his description of human conceptual processing, Barsalou invokes the concept of a "simulation": a dynamic reactivation and recombination of perceptua l symbols in working memory. He stresses the active, productive nature of the perceptual system, in cont rast to the "passive recording device" view. Because the schemata are componential and feature-like, they ca n be recombined in an infinite number of ways, accountin g for the effectively infinite human conceptual capacity. In order to include action and affect as elements with which to ground meaning, Barsalou includes proprioception and introspection as "perceptual sys tem ". Introspection provides the "perceptual" medium necessary for generating concepts referring to cogn itive operations (e.g. "comparison") and emotional states . Barsalou’s system is much like a perceptual re-cast of a classical symbol-manipulation approach. The r eal difference, though, is that the symbols remain modal; instead of transducing perceptual input into amodal forms for processing in other parts of the brain, Barsalo u contends that traces merely need to be "filtered do wn" into a schematic form, and processed entirely withi n e sensorimotor system.
Robust A Priori and A Posteriori Error Analysis for the Approximation of Allen-Cahn and Ginzburg-Landau Equations Past Topological Changes

A priori and a posteriori error estimates are derived for the numerical approximation of scalar and complex valued phase field models. Particular attention is devoted to the dependence of the estimates on a small parameter and to the validity of the estimates in the presence of topological changes in the solution that represents singular points in the evolution. For typical singularities the estimates depend on the inverse of the parameter in a polynomial as opposed to exponential dependence of estimates resulting from a straightforward error analysis. The estimates naturally lead to adaptive mesh refinement and coarsening algorithms. Numerical experiments illustrate the reliability and efficiency of this approach for the evolution of interfaces and vortices that undergo topological changes.
A Modified Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.
Fluid approximation and perturbation analysis of a dynamic priority call center

We analyze a call center with multiclass calls and dynamic priority service discipline, in which a lower priority customer becomes high priority when its waiting time exceeds a given service level threshold. For each priority queue, the service discipline is first come, first served. Based on a fluid approximation of the system, we apply infinitesimal perturbation analysis (EPA) to derive estimators for the derivative of the queue lengths with respect to the threshold parameter. We establish unbiasedness of the estimators, and report numerical results via simulation.
Nearest Neighbor-Based Classification of Uncertain Data

This work deals with the problem of classifying uncertain data. With this aim we introduce the Uncertain Nearest Neighbor (UNN) rule, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest neighbor class of a test object is the class that maximizes the probability of providing its nearest neighbor. The evidence is that the former concept is much more powerful than the latter in the presence of uncertainty, in that it correctly models the right semantics of the nearest neighbor decision rule when applied to the uncertain scenario. An effective and efficient algorithm to perform uncertain nearest neighbor classification of a generic (un)certain test object is designed, based on properties that greatly reduce the temporal cost associated with nearest neighbor class probability computation. Experimental results are presented, showing that the UNN rule is effective and efficient in classifying uncertain data.