Miracle-dz/UniFAR-FTUs huggingface dataset:
BARTER MARKETS

We propose a new data collection mechanism (barter markets), as an alternative to conjoint analysis, that allows for information diffusion among respondents, as an accelerated method to capture real life learning and measurement of consumer’s partworths for product features. An empirical study that compares the barter method and choice-based conjoint demonstrates very superior out-of-sample predictive performance, both immediately (as is commonly done) and on a two-week later validation task, based on data collected from a barter market. We also show evidence that respondents indeed learn from those who are familiar with the product suggesting those cases, and for what people, the barter market is likely to be superior to traditional conjoint measurement methods. However, in the spirit of “no free lunch”, as the barter mechanism is “new to the world”, we found that subjects did find the task more taxing (in various ways) suggesting a potential tradeoff between consumer resource allocation (at the time of the task) and (managerial) predictive accuracy.
Product Name Classification for Product Instance Distinction

Product names with a temporal cue in a product review often refer to several product instances purchased at different times. Previous approaches to product entity recognition and temporal information analysis do not take into account such temporal cues and thus fail to distinguish different product instances. We propose to formulate the resolution of such product names as a classification problem by utilizing time expressions, event features and other temporal cues for a classifier in two stages, detecting the existence of such temporal cues and identifying the purchase time. The empirical results show that term-based features and existing event-based features together enhance the performance of product instance distinction.
A Revised Incremental Conductance MPPT Algorithm for Solar PV Generation Systems

A revised Incremental Conductance (IncCond) maximum power point tracking (MPPT) algorithm for PV generation systems is proposed in this paper. The commonly adopted traditional IncCond method uses a constant step size for voltage adjustment and is difficult to achieve both a good tracking performance and quick elimination of the oscillations, especially under the dramatic changes of the environment conditions. For the revised algorithm, the incremental voltage change step size is adaptively adjusted based on the slope of the power-voltage (P-V) curve. An accelerating factor and a decelerating factor are further applied to adjust the voltage step change considering whether the sign of the P-V curve slope remains the same or not in a subsequent tracking step. In addition, the upper bound of the maximum voltage step change is also updated considering the information of sign changes. The revised MPPT algorithm can quickly track the maximum power points (MPPs) and remove the oscillation of the actual operation points around the real MPPs. The effectiveness of the revised algorithm is demonstrated using a simulation.
Toxin Instability and Its Role in Toxin Translocation from the Endoplasmic Reticulum to the Cytosol

AB toxins enter a host cell by receptor-mediated endocytosis. The catalytic A chain then crosses the endosome or endoplasmic reticulum (ER) membrane to reach its cytosolic target. Dissociation of the A chain from the cell-binding B chain occurs before or during translocation to the cytosol, and only the A chain enters the cytosol. In some cases, AB subunit dissociation is facilitated by the unique physiology and function of the ER. The A chains of these ER-translocating toxins are stable within the architecture of the AB holotoxin, but toxin disassembly results in spontaneous or assisted unfolding of the isolated A chain. This unfolding event places the A chain in a translocation-competent conformation that promotes its export to the cytosol through the quality control mechanism of ER-associated degradation. A lack of lysine residues for ubiquitin conjugation protects the exported A chain from degradation by the ubiquitin-proteasome system, and an interaction with host factors allows the cytosolic toxin to regain a folded, active state. The intrinsic instability of the toxin A chain thus influences multiple steps of the intoxication process. This review will focus on the host–toxin interactions involved with A chain unfolding in the ER and A chain refolding in the cytosol.
Evolvable Analogue Electronic Artificial Cochlea using Field Programmable Analogue Array

An evolvable analogue electronic artificial cochlea has become a practical proposition with the recent development of dynamically configurable and reconfigurable Field Programmable Analogue Arrays (FPAA). The authors describe a proof-of-concept system based on the Anadigm® FPAA that implements a four-channel evolvable cochlea on one device. A genetic algorithm (GA) can be used to arrive at an optimal set of filter parameters in a given environment. In this way, the system is able to ada pt the filter bank for different acoustic environments . The system proposed learns to adapt to the acoustic environment and tunes itself to the wearers needs i n that environment. The authors are also investigatin how the system could be further refined to evolve t he configuration of the cochlea through the primary configuration of the FPAA. This might include, for example, varying the number of channels, comparing multi-channel to single channel, pulse (spiking) versus analogue signal waveform or by using more powerful evolutionary algorithms than the GA used such as the harmony search algorithm.
Analysis of Distributed Stochastic Dual Coordinate Ascent

In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates. However, no serious analysis has been provided to understand the updates and therefore the convergence rates. In the paper, we bridge the gap by providing a theoretical analysis of the convergence rates of the practical DisDCA algorithm. Our analysis helped by empirical studies has shown that it could yield an exponential speed-up in the convergence by increasing the number of dual updates at each iteration. This result justifies the superior performances of the practical DisDCA as compared to the naive variant. As a byproduct, our analysis also reveals the convergence behavior of the one-communication DisDCA.
Exploration of the Dietary and Lifestyle Behaviors and Weight Status and Their Self-Perceptions among Health Sciences University Students in North Lebanon

University students may experience significant environmental changes that exert a negative influence on the quality of their diet and lifestyle. There is scarcity of data concerning the dietary and lifestyle behaviors and weight status of students in the health field in North Lebanon. To investigate these data, a cross-sectional survey was conducted including 369 health sciences students aged 18–25 chosen from four public and private universities in North Lebanon. Data were collected using a standardized interview questionnaire to determine sociodemographic, dietary, and lifestyle behaviors, appetite changes, stress related dietary behaviors, and food cravings, as well as self-perceptions of dietary adequacy, physical activity levels, and weight status. Body mass index was assessed. Results had revealed significant differences in some of the dietary consumption patterns and weight status among seniors compared to juniors. However, the overall prevalence of overweight and obesity recorded 32.2% and the dietary consumption patterns fall below recommended levels. Multivariate regression analysis showed that parental obesity, comfort eating, increased appetite, food cravings, and stressful eating were associated with increased risk of obesity while a healthy diet score was associated with decreased risk. The study's findings call for tailoring culture specific intervention programs which enable students to improve their dietary and lifestyle behaviors and control stress.
SCHOOL AND INDIVIDUAL FACTORS THAT CONTRIBUTE TO THE ACHIEVEMENT GAP IN COLLEGE ADMISSIONS TESTS IN CHILE

Title of Document: SCHOOL AND INDIVIDUAL FACTORS THAT CONTRIBUTE TO THE ACHIEVEMENT GAP IN COLLEGE ADMISSIONS TESTS IN CHILE Paulina Perez Mejias, Ph.D., 2012 Directed By: Professor Alberto F. Cabrera, Department of Counseling, Higher Education, and Special Education In Chile, reports and research papers have shown that there is an achievement gap in college admissions tests mostly associated to students’ gender, socioeconomic status and type of school attended. This gap represents a barrier for low-income and female students to access higher education, as well as for graduates of public schools. Prior studies have used descriptive analyses and single-level linear regression to study this gap, which do not take into account the nested structure of the data (students nested within schools). This study uses multilevel linear modeling to concurrently estimate the effect of student and school characteristics on individual performance in admissions tests in Chile. The findings revealed that more than half of the variation in college admissions test scores happens at the school level. This variation between schools is mostly explained by school sector (private, subsidized private, and public) and the average school socioeconomic status. At the individual level, the most influential factor is individual high school GPA. These findings have important implications for policy and practice, as publicly funded universities in Chile rely almost exclusively on test scores to select students and need-based financial aid requires students to score above a minimum threshold. The results of this study suggest that these admission and financial aid policies need to be reconsidered in order to increase opportunity of access to higher education for traditionally excluded students. SCHOOL AND INDIVIDUAL FACTORS THAT CONTRIBUTE TO THE ACHIEVEMENT GAP IN COLLEGE ADMISSIONS TESTS IN CHILE
Register Liveness Analysis of Executable Code

Liveness analysis of variables is a well-understood technique employed by most compilers to guide optimizations such as useless code elimination and register allocation. Liveness analysis can also be performed on object code if we let registers take the place of variables. The increasing interest in systems that modify object code or executables has generated a need for a fast and accurate register liveness analysis. This paper shows how to accurately compute register liveness information in a time and space efficient manner and how to cope with irregular control flow not encountered in high level languages. Tradeoffs between the precision of the analysis and its computation time and space are discussed. In particular, context sensitive and context insensitive register liveness analysis are compared. Experimental results on precision, time and space usage are provided as well as the impact of liveness analysis on optimizations.
Decomposing Dusty Galaxies. I. Multicomponent Spectral Energy Distribution Fitting

We present a new multicomponent SED decomposition method and use it to analyze the UV to millimeter wavelength SEDs of a sample of dusty infrared-luminous galaxies. Each SED is decomposed into emission from populations of stars, an AGN accretion disk, PAHs, atomic and molecular lines, and distributions of graphite and silicate grains. Decompositions of the SEDs of template starburst galaxies and AGNs provide baseline properties to aid in quantifying the strength of star formation and accretion in the composite systems NGC 6240 and Mrk 1014. We find that obscured radiation from stars is capable of powering the total dust emission from NGC 6240. The presence of a small quantity of 1260 K dust in this source suggests a ~2% AGN contribution, although we cannot rule out a larger contribution from a deeply embedded AGN visible only in X-rays. The decomposition of Mrk 1014 is consistent with ~65% of its power emerging from an AGN and ~35% from star formation. We suggest that many of the variations in our template starburst SEDs may be explained in terms of the different mean optical depths through the clouds of dust surrounding the young stars within each galaxy. Prompted by the divergent far-IR properties of our template AGNs, we suggest that variations in the relative orientation of their AGN accretion disks with respect to the disks of the galaxies hosting them may result in different amounts of AGN-heated cold dust emission emerging from their host galaxies. We estimate that 30%-50% of the far-IR and PAH emission from Mrk 1014 may originate from such AGN-heated material in its host galaxy disk.
Late Career Job Loss and the Decision to Retire

This paper provides an empirical analysis of the effect of involuntary job loss on the lifetime income and labor supply of older workers. I develop and estimate a dynamic programming model of retirement with savings, costly job search and exogenous layoffs. The average cost of job loss is equivalent to one year of pre- displacement earnings, 70% of which is due to the wage reduction and 30% to the search frictions. Displaced workers on average retire fourteen months earlier. However, workers who approached retirement during the Great Recession will work approximately five months longer in response to the contemporaneous financial and labor market shocks.
Optical method for measurement of respiration rate

Standard instrumentation for the assessment of the respiration rate is largely used in medicine. Spirometers, textile-based capacitive sensors or photopletismography are all contact devices used for such aim; the main drawback in their use is the necessity to have a direct contact between the instrument and the patient, which in some cases can limit the use of such devices. In this paper, we present an optical, fully no-contact measurement method for monitoring of the respiration rate, based on the assessment of chest wall movements (velocity and displacement) by a laser Doppler vibrometer (LDVi). The proposed method is operated at a distance of 1.5 m, on different point of the patient thoracic and abdominal area. The measurement procedure was already demonstrated to precisely perform for what concern the monitoring of the cardiac activity. In this paper, we have monitored 33 patients with a reference instrument (spirometer) and with the proposed non-contact method. Statistical analysis (Bland-Altman) of the respiration rate measured with both instruments is reported. We also report a study on the optimal position on the thoracic area based on quality of the signal measured on the same population of subject. Results demonstrate the possibility to continuously monitor the respiration rate with a mean difference respect to the spirometer data of 13 m ( relative error: 0.35%).
Design principles and challenges for an autonomous WSN for structural health monitoring in aircrafts

Structural health monitoring is a critical part of the maintenance procedures of an aircraft. Most of the problems that arise from structural elements are detected by trained personnel using visual inspection. Moreover, the places at which these structural elements are located are frequently very difficult to access. This makes inspections time consuming and potentially very costly. In this paper we are presenting the design principles that must be taken into account in order to successfully deploy a wireless sensor network in an aircraft. We will discuss the relations between sampling duration, power consumption, energy harvesting and storage, wireless communication and network architecture in order to successfully deploy the system. In order to consider the trade-offs between different design issues we have designed a sensor node equipped with an accelerometer, a temperature and humidity sensor and a strain gauge sensor. The sensor node has an energy harvester from a thermo-electrical generator and a battery storage element. Wireless communications are based on Zigbee protocol and the sensor node is controlled from a low power microcontroller.
Two-stage sparse graph construction using MinHash on MapReduce

Image graph attracts attention from researchers due to the empirical success of graph based semi-supervised learning (SSL) methods and tasks such as image clustering, image navigation. Despite its simple structure, overwhelming scale of online images makes image graph construction a difficult problem. The challenge lies in time-consuming computation, and the difficulty of storing, processing the resulted graphs of huge size. We propose a novel method of image graph construction on MapReduce for large-scale data. The method consists of two stages: the first stage separates images into overlapping groups called image pools by using hash method, and the second computes pairwise similarities for pairs of images that are grouped into common pools. Both stages are performed on MapReduce. Our experiments on large-scale data show that the proposed method generates more sparse image graphs that reserve same or improved accuracy when comparing with previous method.
Effects of PD control parameter on walking characteristics of a passive dynamic walker with torso

Specific walking velocity, stability and energy consumption (i.e. walking characteristics) can be obtained by adjusting control parameters of complaint actuation which is essential for a passive walking robot to make full use of its own parameters to walk. In this paper, the series elastic actuator is used as the complaint actuator to adjust the walking characteristics. A passive dynamic walking model and a physical prototype with only one hip actuation were built to investigate how the control parameter can affect the walking characteristics. We studied the effects of control parameter on walking characteristics of a passive dynamic walker with torso by both numerical simulations and physical prototype experiments. The results showed that the control parameter kp has marginal effect on walking speed but can affect the walking stability and energy consumption greatly.
The language of emotions: An analysis of a semantic field

Abstract This paper uses a theory of the emotions to motivate a semantic analysis of English words referring to emotions. The theory assumes that emotions have a two-fold communicative function, both externally amongst members of the species, and internally within the brain so as to bypass complex inferences. It implies that there is a small number of basic signals that can set up characteristic emotional modes within the organism, roughly corresponding to happiness, sadness, fear, anger, and disgust. In human beings, these modes can be modulated by the propositional content of the cognitive evaluation that caused the emotion signal, or else, if this content fails to impinge on consciousness, these modes can be experienced as emotions that have occurred for no apparent reason. According to this “communicative” theory, there should be a set of terms that refer to basic emotions, and these terms should have no internal semantics, since they cannot be analysed into anything more basic, such as a prototype or.
Interactive segmentation of EEG synchrony data in time-frequency space by means of region-growing and Bayesian regularization.

In this paper we present a new methodology for the interactive visualization and segmentation of electroencephalographic (EEG) scalp synchrony data. Synchrony measurements are estimated for all electrode pairs and classified as higher, lower, or equal than the baseline average. The classified values are then displayed in the form of Time-Frequency-Topography (TFT) maps, which can be segmented using a seeded region growing algorithm and a Bayesian regularization technique. Finally, we present the synchronization maps that result from the analysis of real EEG data from a figure categorization experiment.
Signal Processing for Time-of-Flight Imaging Sensors: An introduction to inverse problems in computational 3-D imaging

Time-of-flight (ToF) sensors offer a cost-effective and realtime solution to the problem of three-dimensional (3-D) imaging-a theme that has revolutionized our sceneunderstanding capabilities and is a topic of contemporary interest across many areas of science and engineering. The goal of this tutorial-style article is to provide a thorough understanding of ToF imaging systems from a signal processing perspective that is useful to all application areas. Starting with a brief history of the ToF principle, we describe the mathematical basics of the ToF image-formation process, for both time- and frequency-domain, present an overview of important results within the topic, and discuss contemporary challenges where this emerging area can benefit from the signal processing community. In particular, we examine case studies where inverse problems in ToF imaging are coupled with signal processing theory and methods, such as sampling theory, system identification, and spectral estimation, among others. Through this exposition, we hope to establish that ToF sensors are more than just depth sensors; depth information may be used to encode other forms of physical parameters, such as, the fluorescence lifetime of a biosample or the diffusion coefficient of turbid/scattering medium. The MATLAB scripts and ToF sensor data used for reproducing figures in this article are available via the author? s webpage: http://www.mit. edu/~ayush/Code.
Barriers to Community-Based Computing in the Philippines

Community-based computing is a popular trend among the frontiers in social computing opened up by advances in information technology. Also known as Crowdsourcing, derived from the terms "crowd" and "outsourcing", it depends on the collective intellect of a large group of people from which to derive information, experience and, eventually, knowledge. But despite the advances in technology since the birth of the world wide web, and the ubiquitousness of mobile devices in today's modern society, there are still surprisingly high barriers to community-based computing in developing countries such as the Philippines. This paper presents the barriers to community-based computing discovered during the development of use cases for eBayanihan, a multi-platform crowdsourcing system for disaster response being developed by a team from the Ateneo De Manila University sponsored by the Philippine government through the Department of Science and Technology. It is hoped that this will promote greater study and development of solutions to barriers to community-based computing in the future and in other developing countries.
Multiple User Context Inference by Fusing Data Sources

Inference of user context information, including user's gender, age, marital status, location and so on, has been proven to be valuable for building context aware recommender system. However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e.g. Internet browsing logs, or mobile call records), and 2. ignoring the interdependence of multiple user contexts (e.g. interdependence between age and marital status), which have led to poor inference performance. To solve this problem, in this paper, we first exploit tensor outer product to fuse multiple data sources in the feature space to obtain an extensional user feature representation. Following this, by taking this extensional user feature representation as input, we propose a multiple attribute probabilistic model called MulAProM to infer user contexts that can take advantage of the interdependence between them. Our study is based on large telecommunication datasets from the local mobile operator of Shanghai, China, and consists of two data sources, 4.6 million call detail records and 7.5 million data traffic records of 8,000 mobile users, collected in the course of six months. The experimental results show that our model can outperform other models in terms of \emph{recall}, \emph{precision}, and the \emph{F1-measure}.