Miracle-dz/UniFAR-FTUs huggingface dataset:
Primal Dual Gives Optimal Energy Efficient Online Algorithms

We consider the problem of online scheduling of jobs on unrelated machines with dynamic speed scaling to minimize the sum of energy and weighted flow time. We give an algorithm with an almost optimal competitive ratio for arbitrary power functions. (No earlier results handled arbitrary power functions for unrelated machines.) For power functions of the form f(s) = s for some constant α > 1, we get a competitive ratio of O( α logα ), improving upon a previous competitive ratio of O(α) [3], along with a matching lower bound of Ω( α logα ). Further, in the resource augmentation model, with a 1+ speed up, we give a 2( 1 +1) competitive algorithm, with essentially the same techniques, matching the bound of [3] for the special case of fixed speed unrelated machines. Unlike the previous results most of which used an amortized local competitiveness argument or dual fitting methods, we use a primal-dual method, which is useful not only to analyze the algorithms but also to design the algorithm itself.
Speaker recognition by separating phonetic space and speaker space

In speaker recognition, it is a problem that speech feature varies depending on sentences and time di(cid:11)erence. This variation is mainly attributed to the variation of phonetic information and speaker information included in speech data. If these two kinds of information are separated each other, robust speaker recognition will be realized. In this study, we propose a speaker identi(cid:12)ca-tion and speaker veri(cid:12)cation method by separating the phonetic information and speaker information by a sub-space method, under the assumption that a space with large within-speaker variance is a \phonetic space" and a space with small within-speaker variance is a \speaker space". We carried out comparative experiments of the proposed method with a conventional method based on GMM in an observation space as well as in a space transformed by LDA. As a result, we could construct a robust speaker model with a few model parameters using a few training data by the proposed method.
Exogenously induced expression of ethylene biosynthesis, ethylene perception, phospholipase D, and Rboh-oxidase genes in broccoli seedlings

In higher plants, copper ions, hydrogen peroxide, and cycloheximide have been recognized as very effective inducers of the transcriptional activity of genes encoding the enzymes of the ethylene biosynthesis pathway. In this report, the transcriptional patterns of genes encoding the 1-aminocyclopropane-1-carboxylate synthases (ACSs), 1-aminocyclopropane-1-carboxylate oxidases (ACOs), ETR1, ETR2, and ERS1 ethylene receptors, phospholipase D (PLD)-α1, -α2, -γ1, and -δ, and respiratory burst oxidase homologue (Rboh)-NADPH oxidase-D and -F in response to these inducers in Brassica oleracea etiolated seedlings are shown. ACS1, ACO1, ETR2, PLD-γ1, and RbohD represent genes whose expression was considerably affected by all of the inducers used. The investigations were performed on the seedlings with (i) ethylene insensitivity and (ii) a reduced level of the PLD-derived phosphatidic acid (PA). The general conclusion is that the expression of ACS1, -3, -4, -5, -7, and -11, ACO1, ETR1, ERS1, and ETR2, PLD-γ 1, and RbohD and F genes is undoubtedly under the reciprocal cross-talk of the ethylene and PAPLD signalling routes; both signals affect it in concerted or opposite ways depending on the gene or the type of stimuli. The results of these studies on broccoli seedlings are in agreement with the hypothesis that PA may directly affect the ethylene signal transduction pathway via an inhibitory effect on CTR1 (constitutive triple response 1) activity.
Complex c-di-GMP Signaling Networks Mediate Transition between Virulence Properties and Biofilm Formation in Salmonella enterica Serovar Typhimurium

Upon Salmonella enterica serovar Typhimurium infection of the gut, an early line of defense is the gastrointestinal epithelium which senses the pathogen and intrusion along the epithelial barrier is one of the first events towards disease. Recently, we showed that high intracellular amounts of the secondary messenger c-di-GMP in S. typhimurium inhibited invasion and abolished induction of a pro-inflammatory immune response in the colonic epithelial cell line HT-29 suggesting regulation of transition between biofilm formation and virulence by c-di-GMP in the intestine. Here we show that highly complex c-di-GMP signaling networks consisting of distinct groups of c-di-GMP synthesizing and degrading proteins modulate the virulence phenotypes invasion, IL-8 production and in vivo colonization in the streptomycin-treated mouse model implying a spatial and timely modulation of virulence properties in S. typhimurium by c-di-GMP signaling. Inhibition of the invasion and IL-8 induction phenotype by c-di-GMP (partially) requires the major biofilm activator CsgD and/or BcsA, the synthase for the extracellular matrix component cellulose. Inhibition of the invasion phenotype is associated with inhibition of secretion of the type three secretion system effector protein SipA, which requires c-di-GMP metabolizing proteins, but not their catalytic activity. Our findings show that c-di-GMP signaling is at least equally important in the regulation of Salmonella-host interaction as in the regulation of biofilm formation at ambient temperature.
Tourism Recommendation System based in user's profile and functionality levels

This paper describes a proposal to develop a Tourism Recommendation System based in users enhanced profiles (composed by basic user information, relations between user and a set of stereotypes and user functionality levels). The main focus of this work is to evaluate if user's physical and psychological functionality levels considered in user's profiles creation, will produce significant changes in the recommendation results. This work aims also to contribute with a different way to classify points-of-interest (POI) considering their capacity to receive tourists with certain levels of physical and psychological issues that will be described in this paper sections.
LyS at TASS 2013: Analysing Spanish tweets by means of dependency parsing, semantic-oriented lexicons and psychometric word-properties

espanolEste articulo describe el enfoque utilizado por nuestro grupo para resolver las tareas de analisis global del sentimiento, identificacion de topicos y clasi- ficacion de la tendencia politica sobre tuits en espanol; propuestas en el Taller de Analisis del Sentimiento en la SEPLN (TASS 2013). Como paso previo, se realiza un preprocesado ad-hoc para normalizar los tuits. A continuacion, se lleva a cabo un analisis morfologico de los tuits para luego obtener su estructura sintactica aplicando algoritmos de analisis de dependencias. Nuestra propuesta tambien emplea recursos psicologicos, que permiten explotar las caracteristicas psicometricas del lenguaje humano. Los resultados experimentales confirman la robustez de la propuesta, que en terminos generales ha obtenido un buen rendimiento, alcanzando el primer puesto en la tarea de clasificacion de topicos. EnglishThis article describes the approach developed by our group in order to resolve the sentiment analysis at a global level, topic identification and political tendency classification tasks on Spanish tweets; proposed at the Workshop of Sentiment Analysis at sepln (tass 2013). As a preliminary step, we carry out an ad-hoc preprocessing in order to normalise the tweets. We then apply part-of-speech tagging and dependency parsing algorithms to the tweets to obtain their syntactic structure. Our proposal also employs psychological resources in order to exploit the psychometric properties of human language. The experimental results confirm the robustness of the proposal, which has achieved good performance in general, being the best-performing approach in the topic classification task.
Evaluation Framework for Ontology Modularization Approaches

In this research, we devise a novel evaluation framework for ontology modularization to measure both the quality of ontology modules and the logical consistency during the modularization process. To measure the quality of ontology modules, we introduce module size, module cohesion, and module coupling metrics derived from the theory of software engineering. In addition, the evaluating modularization process to guarantee the logical consistency of entities and axioms during modularization is also included in the proposed framework. Empirical experiments are performed, and the experimental results indicate that our evaluation framework for ontology modularization is valid and works well. The ultimate goal of this research is to build an ontology modularization evaluation framework, enabling ontology engineers to compare and apply easily the different modularization approaches to produce qualifying ontology modules.
Towards Proof-Based Real-Time Distribution Middleware

Distribution middleware is now the back-bone of many critic al, realtime embedded applications. New developments strive for en suri g and preserving system properties as part of the engineering pro cess. Thus, middleware must demonstrate its properties, such as depend ability, reliability or real-time constraints. This paper discusses mid dleware architecture geared towards verification and validation of its pr ope ties. Our contribution presents the “schizophrenic” middleware arc hitecture, and how we adapt and deploy this architecture to support the many requirements of critical systems, and to enable the verification of i ts properties.
Evaluation of 3D feature descriptors for classification of surface geometries in point clouds

This paper investigates existing methods for 3D point feature description with a special emphasis on their expressiveness of the local surface geometry. We choose three promising descriptors, namely Radius-Based Surface Descriptor (RSD), Principal Curvatures (PC) and Fast Point Feature Histograms (FPFH), and present an approach for each of them to show how they can be used to classify primitive local surfaces such as cylinders, edges or corners in point clouds. Furthermore these descriptor-classifier combinations have to hold an in-depth evaluation to show their discriminative power and robustness in real world scenarios. Our analysis incorporates detailed accuracy measurements on sparse and noisy point clouds representing typical indoor setups for mobile robot tasks and considers the resource consumption to assure real-time processing.
The Effects of Human Variation in DUC Summarization Evaluation

There is a long history of research in automatic text summarization systems by both the text retrieval and the natural language processing communities, but evaluation of such systems’ output has always presented problems. One critical problem remains how to handle the unavoidable variability in human judgments at the core of all the evaluations. Sponsored by the DARPA TIDES project, NIST launched a new text summarization evaluation effort, called DUC, in 2001 with follow-on workshops in 2002 and 2003. Human judgments provided the foundation for all three evaluations and this paper examines how the variation in those judgments does and does not affect the results and their interpretation.
Physical-layer network coding: A random coding error exponent perspective

In this work, we derive the random coding error exponent for the uplink phase of a two-way relay system where physical layer network coding (PNC) is employed. The error exponent is derived for the practical (yet sub-optimum) XOR channel decoding setting. We show that the random coding error exponent under optimum (i.e., maximum likelihood) PNC channel decoding can be achieved even under the sub-optimal XOR channel decoding. The derived achievability bounds provide us with valuable insight and can be used as a benchmark for the performance of practical channel-coded PNC systems employing low complexity decoders when finite-length codewords are used.
Ieee Transactions on Communications, Accepted for Publication Uplink Resource Management for Multiuser Ofdm Video Transmission Systems: Analysis and Algorithm Design

—We consider a multiuser OFDM system in which users want to transmit videos via a base station. The base station knows the channel state information (CSI) as well as the rate distortion (RD) information of the video streams and tries to allocate power and spectrum resources to the users according to both physical layer CSI and application layer RD information. We derive and analyze a condition for the optimal resource allocation solution in a continuous frequency response setting. The optimality condition for this cross layer optimization scenario is similar to the equal slope condition for conventional video multiplexing resource allocation. Based on our analysis, we design an iterative subcarrier assignment and power allocation algorithm for an uplink system, and provide numerical performance analysis with different numbers of users. Comparing to systems with either only physical layer or only application layer information available at the base station, our results show that the user capacity and the video PSNR performance can be increased significantly by using cross layer design. Bit-level simulations which take into account the imperfection of the video coding rate control, the variation of RD curve fitting, as well as channel errors, are presented.
Experience Report: White Box Test Case Generation for Automotive Embedded Software

Testing software based on symbolic execution is a promising and well-researched approach. Recently, various dynamic symbolic execution approaches have been proposed that are of interest for industrial practice. However, the corresponding tools are typically not directly usable on model-based languages that are predominantly used in the automotive sector. To this end, we use an internal software analysis framework for model-based, embedded software that allows us to easily integrate various approaches leveraging static and dynamic symbolic execution. In our case study, we describe idiosyncrasies of automotive embedded software and show the benefits of leveraging symbolic information to generate test cases. We study and compare different techniques for generating test cases. We evaluate all techniques empirically on relevant, real-world programs from engine control.
CLUSTERING OF RED GALAXIES AROUND THE z = 1.53 QUASAR 3C 270.1

In the paradigm of hierarchical galaxy formation, luminous radio galaxies mark mass assembly peaks that should contain clusters of galaxies. Observations of the z = 1.53 quasar 3C 270.1 with the Spitzer Space Telescope at 3.6–24 μm and with the 6.5 m MMT in the z′ and Y bands allow the detection of potential cluster members via photometric redshifts. Compared with nearby control fields, there is an excess of ∼11 extremely red objects (EROs) at 1.33 ⩽ zphot ⩽ 1.73, consistent with a protocluster around the quasar. The spectral energy distributions of 3/4 of the EROs are better fitted with passive elliptical galaxies than with dust-reddened starbursts, and of four sources well detected on an archival Hubble Space Telescope (HST) snapshot image, all have undisturbed morphologies. However, one ERO, not covered by the HST image, is a double source with 0. ″8 separation on the z′ image and a marginal (2σ) 24 μm detection indicating a dust-enshrouded starburst. The EROs are more luminous than L⋆ (H = −23.6 AB mag at z ≈ 1.5).
Four Classes of Intercellular Channels between Glial Cells in the CNS

Astrocytes form extensive gap junctions with other astrocytes and with oligodendrocytes. Junctional communication between CNS glia is likely of critical importance because loss of the gap junction channel-forming proteins, connexins Cx32 and Cx47, result in severe demyelination. However, CNS glia express at least six connexins, and the cellular origins and relationships of these proteins have not been determined. We produced a Cx29 reporter mouse in which the connexin coding sequence was replaced with a histological marker, which was used to demonstrate that Cx29, Cx32, and Cx47 are expressed specifically in oligodendrocytes. To determine the relationships between astrocyte and oligodendrocyte connexins, we used double- and triple-immunofluorescence microscopy using semithin sections (<1 μm) of adult mouse spinal cord. Astrocytes form two distinct classes of gap junctions with each other; those composed of Cx26 and those composed of Cx43 and Cx30. In addition, astrocytes establish two classes of intercellular channels with oligodendrocytes, heterotypic Cx26–Cx32 channels and heterotypic Cx30/Cx43–Cx47 channels that may also be heteromeric. In contrast, Cx29 does not colocalize with any of the other five connexins. The data provide the first in vivo demonstration of heterotypic intercellular channels and reveal an unexpected complexity in the composition of glial gap junctions.
Learning to Control a 6-Degree-of-Freedom Walking Robot

We analyze the issue of optimizing a control policy for a complex system in a simulated trial-and-error learning process. The approach to this problem we consider is reinforcement learning (RL). Stationary policies, applied by most RL methods, may be improper in control applications, since for time discretization fine enough they do not exhibit exploration capabilities and define policy gradient estimators of very large variance. As a remedy to those difficulties, we proposed earlier the use of piecewise non-Markov policies. In the experimental study presented here we apply our approach to a 6-degree-of-freedom walking robot and obtain an efficient policy for this object.
Set-membership filtering using random samples

Set-membership filtering aims to determine the set of feasible states when the system dynamics and received measurements are corrupted by bounded noise having unknown statistics. In this paper, randomly generated particles are used to approximate the feasible state set, and the theory of random sets in continuous spaces is used to prove stochastic, set-theoretic convergence as the number of particles increases. Two novel modifications are proposed, which generate random samples that are more evenly distributed over the feasible state set compared to the naive application of the particle filter. The key insight behind both modifications is that for set-membership problems, the particle weights do not need to be selected according to the probability law of the underlying system, but should be designed to achieve even coverage over the entire feasible set. Simulation results in a nonlinear batch reaction problem confirm the superior performance of the proposed modifications.
Speed control of brushless DC motors using emotional intelligent controller

This paper presents an improved emotional controller for brushless DC motor (BLDC) drive. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of the new controller is based on the emotion processing mechanism in brain. This intelligent control is inspired by the limbic system of mammalian brain, especially amygdala. The controller is successfully implemented in simulation using MATLAB software, brushless dc drive with trapezoidal back-emf. In this work, a novel and simple implementation of BLDC motor drive system is achieved by using the intelligent controller, which controls the motor speed accurately. This emotional intelligent controller has simple structure with high auto learning feature. Simulation results show that both accurate steady state and fast transient speed responses can be achieved in wide range of speed from 20 to 300 [rpm]. Moreover, for evaluate this emotional controller and hence to assess the effectiveness and control capability of the proposed BELBIC scheme, the performances of proposed control scheme are compared with a conventional PID controller for the BLDC drive control, in simulation different conditions. This is shown proper operating than the PID controller. And also shows excellent promise for industrial scale utilization.
Quantitative Fitness Analysis Identifies exo1∆ and Other Suppressors or Enhancers of Telomere Defects in Schizosaccharomyces pombe

Synthetic genetic array (SGA) has been successfully used to identify genetic interactions in S. cerevisiae and S. pombe. In S. pombe, SGA methods use either cycloheximide (C) or heat shock (HS) to select double mutants before measuring colony size as a surrogate for fitness. Quantitative Fitness Analysis (QFA) is a different method for determining fitness of microbial strains. In QFA, liquid cultures are spotted onto solid agar and growth curves determined for each spot by photography and model fitting. Here, we compared the two S. pombe SGA methods and found that the HS method was more reproducible for us. We also developed a QFA procedure for S. pombe. We used QFA to identify genetic interactions affecting two temperature sensitive, telomere associated query mutations (taz1Δ and pot1-1). We identify exo1∆ and other gene deletions as suppressors or enhancers of S. pombe telomere defects. Our study identifies known and novel gene deletions affecting the fitness of strains with telomere defects. The interactions we identify may be relevant in human cells.
Design and implementation of an intelligent tutoring system for English instruction

This emotion based intelligent tutoring system for English instruction is designed to make up the lack of emotional factors in cognitive ability of traditional intelligent tutoring systems. Microsoft Agent is used as interactive platform in the system. Audio and image information is used as interactive methods. Result of emotional face image recognition is used as one of evaluation factors, to guide teaching strategies adjustment. During the teaching process, it could provide adaptable teaching strategy not only according to the test score but also current emotional study status of students.
SALEEMA AMERSHI RESEARCH STATEMENT

The unprecedented opportunity for big data to enhance our capabilities and improve our lives is limited by our ability to use that data. Machine learning can give us this ability by transforming raw data into the building blocks necessary for configuring automated behaviors. However, the complexity of machine learning has largely restricted its use to experts and skilled developers. For example, trained developers can employ machine learning to automatically detect objects, organize information, and understand and predict behaviors. In contrast, ill-equipped end-users are limited to using machine learning for simple personalization based on developer-conceived notions of interest or similarity. Even within personalization systems, end-user control over the machine learning is typically and intentionally reduced to simple object labeling. As a human-computer interaction researcher, I aim to put the full potential of machine learning in the hands of everyday people.