Miracle-dz/UniFAR-FTUs huggingface dataset:
Conceptualization : A Generative + Descriptive Modeling Approach

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.
Relationship of Visual Cortex Function and Visual Acuity in Anisometropic Amblyopic Children

Purpose: To detect the functional deficit of the visual cortex in anisometropic amblyopia children using functional magnetic resonance imaging (fMRI) technique, and investigate the relationship between visual acuity and visual cortex function. Methods: Blood oxygenation level-dependent fMRI (BOLD-fMRI) was performed in ten monocular anisometropic amblyopia children and ten normal controls. fMRI images were acquired in two runs with visual stimulation delivered separately through the sound and amblyopic eyes. Measurements were performed in cortical activation of striate and extrastriate areas at the occipital lobe. The relationship between cortex function and visual acuity was analyzed by Pearson partial analysis. Results: The activation areas of both the striate and extrastriate cortices in the amblyopic eyes were significantly lower than that of the sound fellow eyes. No relationship was found between the striate and extrastriate cortex activation. No relationship was found between the visual cortical activation of striate, extrastriate areas and visual acuity of anisometropic amblyopes. Conclusions: BOLD-fMRI revealed the independent striate and extrastriate cortical deficits in anisometropic amblyopes. In addition, the visual acuity lesion and the striate and extrastriate cortical deficits were not parallel, and results of fMRI examination have much potential value in the evaluation of amblyopia.
(μ-Naphthalene-1,5-disulfonato-κ2 O 1:O 5)bis[triaqua(glycinato-κ2 N,O)copper(II)]

In the title compound, [Cu2(C2H4NO2)2(C10H6O6S2)(H2O)6], the naphthalenedisulfonate group lies on a center of inversion and bridges two glycinate-chelated CuII atoms. The CuII atom exists in a CuNO4 square-pyramidal geometry that is distorted towards an octahedron owing to a long Cu—Osulfonate bond [2.636 (2) Å]. In the crystal, extensive N—H⋯O and O—H⋯O hydrogen bonds link adjacent molecules into a three-dimensional network
Utilization of Coal Combustion Fly Ash in Terracotta Bodies

In this present work, coal combustion fly ash from a power plant in Turkey was used in combination with a traditional raw material in terracotta production with the aim of having a product with improved physico-mechanical properties and lower production cost. Several compositions were prepared by adding different amounts of fly ash (ranging from 0 to 20 %) in a yellow firing terracotta formulation and shaped by wet pressing. Following firing at a suitable temperature, some of the physical properties of the resultant tiles were determined as a function of the fly ash content. A combination of XRD, SEM and EDX techniques were also employed to correlate the properties with the phase composition. The results indicated that fly ash could be utilized easily in certain amounts in such an application.
A2Q: An Agent-based Architecure for Multilingual Q&A

In this paper we describe the agent based architecture and extensively report the design of the shallow processing model in it. We present the general model describing the data flow and the expected activities that have to be carried out. The notion of question session will be introduced as a means to control the communication among the different agents. We then present a shallow model mainly based on an IR engine and a passage re-ranking that uses the notion of expanded query. We will report the pilot investigation on the performances of
Every Breath You Take - Every Dollar You&Apos;Ll Make: The Long-Term Consequences of the Clean Air Act of 1970

This paper examines the long-term impacts of early childhood exposure to air pollution on adult outcomes using US administrative data. We exploit changes in air pollution driven by the 1970 Clean Air Act to analyze the difference in outcomes between cohorts born in counties before and after large improvements in air pollution relative to those same cohorts born in counties that had no improvements. We find a significant relationship between pollution exposure in the year of birth and later-life outcomes. A higher pollution level in the year of birth is associated with lower labor force participation and lower earnings at age 30.
High-Efficiency InGaN/GaN Dot-in-a-Wire Red Light-Emitting Diodes

We report on the achievement of high-performance InGaN/GaN dot-in-a-wire red light-emitting diodes on Si(111) substrates. Owing to the superior 3-D carrier confinement offered by the self-organized dot-in-a-wire heterostructures, the devices exhibit relatively high (~18%-32%) internal quantum efficiency at room temperature. Moreover, no efficiency droop was observed for injection current up to ~480A/cm2 under pulsed biasing conditions. We have also demonstrated that, by controlling the inhomogeneous broadening of the dot-in-a-wire heterostructures, the devices can exhibit relatively stable emission characteristics with increasing current.
Conceptual Weighing Query Expansion based on User Profiles

Proper query terms significantly affect the performance of information retrieval systems. In this paper, a conceptual weighting method for query expansion is proposed with the help of user profile. Here, the users‘ initial queries and the retrieved documents based on the user‘s query (top n relevant documents) are analyzed and then the relevant terms from the documents retrieved are weighted. The terms having higher weight and the terms from the previous searches with a greater threshold weight will be selected and are used to derive the concepts in the concept network which are then connected to the phrases. Based on the matching of those phrases with that of the query phrases, additional query terms are selected and based on those additional query terms, the user‘s original query is expanded and the search is enhanced.
Efficient data possession–checking protocol with deduplication in cloud

Deduplication has been widely applied to save storage overhead in the cloud server. Data integrity verification with deduplication can not only save space of the cloud server but also ensure security of the stored data. In the existing integrity verification scheme, deduplications are implemented by the cloud server. The signatures of all data blocks are generated and sent to the cloud server. Once receiving the data blocks and signatures, the cloud server compares the received signatures with the stored signatures. If there is a signature that has the same value as some stored signature, the received signature and data block will not be stored by the cloud server. Otherwise, the cloud server stores all received signatures and data blocks. In fact, these operations bring a lot of computational costs. To solve this problem, we propose a data integrity verification scheme with deduplication. In this scheme, the deduplication is performed by the cloud users, which can avoid additional communicational and computational costs. The experiment evaluation indicates that our scheme is practical for real application scenario. We demonstrate that the proposed scheme satisfies signature unforgeability, and the malicious users cannot obtain any legitimate file from the cloud server in the form of deception.
Psoriasis Detection Using Skin Color and Texture Features

Problem statement: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Approach: Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. We used feed forward neural networks to classify input images to be psoriases infected or non psoriasis infected. Results: The system gave very encouraging results during the neural network training and generalization face. Conclusion: The aim of this worked to evaluate the ability of the proposed skin texture recognition algorithm to discriminate between healthy and infected skins and we took the psoriasis disease as example.
Performance Comparison Analysis between IEEE 802.11a/b/g/n Standards

Now days, in practical application scenario, with increasing demand of wireless LANs, higher data rates are required. Wireless Local Area Network (WLAN) is based on IEEE 802.11 standard and is also popular by the name as Wireless Fidelity (Wi-Fi). The task groups within the 802.11 working group introduced few extensions to the original specifications. The well known extensions of 802.11 specifications are 802.11b, 802.11a, 802.11g and 802.11n. This paper provides the major differences between various IEEE 802.11 standards, their operation, interoperability and deployment constraints. In this paper, performances of IEEE 802.11a/b/g/n standards are explained.
Data streaming algorithms for efficient and accurate estimation of flow size distribution

Knowing the distribution of the sizes of traffic flows passing through a network link helps a network operator to characterize network resource usage, infer traffic demands, detect traffic anomalies, and accommodate new traffic demands through better traffic engineering. Previous work on estimating the flow size distribution has been focused on making inferences from sampled network traffic. Its accuracy is limited by the (typically) low sampling rate required to make the sampling operation affordable. In this paper we present a novel data streaming algorithm to provide much more accurate estimates of flow distribution, using a "lossy data structure" which consists of an array of counters fitted well into SRAM. For each incoming packet, our algorithm only needs to increment one underlying counter, making the algorithm fast enough even for 40 Gbps (OC-768) links. The data structure is lossy in the sense that sizes of multiple flows may collide into the same counter. Our algorithm uses Bayesian statistical methods such as Expectation Maximization to infer the most likely flow size distribution that results in the observed counter values after collision. Evaluations of this algorithm on large Internet traces obtained from several sources (including a tier-1 ISP) demonstrate that it has very high measurement accuracy (within 2%). Our algorithm not only dramatically improves the accuracy of flow distribution measurement, but also contributes to the field of data streaming by formalizing an existing methodology and applying it to the context of estimating the flow-distribution.
Mining patterns in long sequential data with noise

Pattern discovery in time series data has been a problem of great importance in many fields, e.g., computational biology, performance analysis, consumer behavior, etc. Recently, considerable amount of research has been carried out in this area. The facts that the input data is typically very large and noises may present in various formats pose great challenge to the mining process. Recently, we have made several new research advances in this area. In this paper, we present some of them. We will survey new models proposed to address different types of noises as well as scalable algorithms developed for efficiently mining patterns under each model.
Multi‐Scale Kernels Using Random Walks

We introduce novel multi‐scale kernels using the random walk framework and derive corresponding embeddings and pairwise distances. The fractional moments of the rate of continuous time random walk (equivalently diffusion rate) are used to discover higher order kernels (or similarities) between pair of points. The formulated kernels are isometry, scale and tessellation invariant, can be made globally or locally shape aware and are insensitive to partial objects and noise based on the moment and influence parameters. In addition, the corresponding kernel distances and embeddings are convergent and efficiently computable. We introduce dual Green's mean signatures based on the kernels and discuss the applicability of the multi‐scale distance and embedding. Collectively, we present a unified view of popular embeddings and distance metrics while recovering intuitive probabilistic interpretations on discrete surface meshes.
Activity Recognition Based on Semi-supervised Learning

Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Since labeling samples requires human's efforts, most existing research in activity recognition focus on refining learning techniques to utilize the costly labeled samples as effectively as possible. However, few of them consider using the costless unlabeled samples to boost learning performance. In this work, we propose a novel semi-supervised learning algorithm named En-Co-training to make use of the unlabeled samples. Our algorithm extends the co- training paradigm by using ensemble method. Experimental results show that En-Co-training is able to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.
Tele-Impedance based stiffness and motion augmentation for a knee exoskeleton device

In this paper, a knee exoskeleton device and its Tele-Impedance based assistive control scheme is presented. The exoskeleton device is an inherently compliant actuated system that was implemented based on the series elastic actuation (SEA) to provide improved and intrinsically soft interaction behaviour. Details of the exoskeleton design are presented. A detailed musculoskeletal model was developed and experimentally identified in order to map electromyographic signals to the antagonistic muscle torques, acting on the human knee joint. The estimated muscle torques are used in order to determine the user's intent and joint stiffness trend. These reference signals are exploited by a novel Tele-Impedance controller which is applied to a knee exoskeleton device to provide assistance and stiffness augmentation to the user's knee joint. Experimental trials of a standing-up motion task were carried out for evaluation of the proposed control strategy. The results indicate that the proposed knee exoskeleton device and control scheme can effectively generate assistive actions that are intrinsically and naturally controlled by the user muscle activity.
A Robust Detection Method of Control Points for Calibration and Measurement With Defocused Images

This paper presents a detection method of control points for the camera calibration and measurement applications, which is robust to defocus. Our method is based on a ground truth, which we call ridge invariance. That is, the positions of broad-brush lines’ ridge lines are invariant to defocus blur. First, the ridge invariance is deduced in theory. Then, the methods for ridge point’s detection including defocus degree estimation and salience enhancement are deduced. In calibration and measurement experiments, new marks are designed and control points are determined at intersects of the ridge curves. Experiments show that our method can obtain precise calibration and measurement results with images in a large defocus amount range. In the effective defocus amount range, the accuracy of the proposed method almost keeps unchanged to the best values. The proposed method has approximated the same performance as conventional methods at good focus values.
Different Catalytic Mechanisms in Mammalian Selenocysteine- and Cysteine-Containing Methionine-R-Sulfoxide Reductases

Selenocysteine (Sec) is found in active sites of several oxidoreductases in which this residue is essential for catalytic activity. However, many selenoproteins have fully functional orthologs, wherein cysteine (Cys) occupies the position of Sec. The reason why some enzymes evolve into selenoproteins if the Cys versions may be sufficient is not understood. Among three mammalian methionine-R-sulfoxide reductases (MsrBs), MsrB1 is a Sec-containing protein, whereas MsrB2 and MsrB3 contain Cys in the active site, making these enzymes an excellent system for addressing the question of why Sec is used in biological systems. In this study, we found that residues, which are uniquely conserved in Cys-containing MsrBs and which are critical for enzyme activity in MsrB2 and MsrB3, were not required for MsrB1, but increased the activity of its Cys mutant. Conversely, selenoprotein MsrB1 had a unique resolving Cys reversibly engaged in the selenenylsulfide bond. However, this Cys was not necessary for activities of either MsrB2, MsrB3, or the Cys mutant of MsrB1. We prepared Sec-containing forms of MsrB2 and MsrB3 and found that they were more than 100-fold more active than the natural Cys forms. However, these selenoproteins could not be reduced by the physiological electron donor, thioredoxin. Yet, insertion of the resolving Cys, which was conserved in MsrB1, into the selenoprotein form of MsrB3 restored the thioredoxin-dependent activity of this enzyme. These data revealed differences in catalytic mechanisms between selenoprotein MsrB1 and non-selenoproteins MsrB2 and MsrB3, and identified catalytic advantages and disadvantages of Sec- and Cys-containing proteins. The data also suggested that Sec- and Cys-containing oxidoreductases require distinct sets of active-site features that maximize their catalytic efficiencies and provide strategies for protein design with improved catalytic properties.
Extracting Lines in Noisy Image Using Directional Information

Detection of lines in noisy image is not easy. When using Hough transform, multiple false peaks may be generated from collinear noisy edge points, which in turn may create false line segments. To overcome this problem, we introduce a method for detecting lines in noise based on directional information. Orientations are generated by Gabor filters to guide an anisotropic Gaussian filtering process, and they are also used in the peak selection in Hough transform. The experimental results show the effectiveness of this method
Automated Discovery of Functional Generality of Human Gene Expression Programs

An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-κB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal “cross-talk,” and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data.
Patterning, electroplating and removal of SU-8 moulds by excimer laser micromachining

The ablation characteristics of the SU-8 photoresist (spun on Si wafers) under 248 KrF excimer pulsed laser radiation have been studied. The variation of etch rate with fluence has been investigated in the range 0.05-3.01 J cm-2. The threshold fluence for ablation of SU-8 is measured to be about 0.05 J cm-2. The etch rate of SU-8 is found to be higher than that of polyimide (previously reported) under similar conditions. We have investigated the effects of different prebake temperatures (90, 110, 120 and 200 °C) on ablation characteristics, which are found to be similar for all temperatures. The effect of increasing the number of laser shots (from 10 to 10 000) has been examined at different fluences in order to understand the etch-rate variation near the `end of film' stage of ablation. The results of our analysis using scanning electron microscopy, profilometry and optical microscopy reveal the very smooth morphology of the etched surfaces with no significant debris, no noticeable damage to underlying silicon and the gradual build-up of a carbonaceous film outside and around the etch pits. We find SU-8 very suitable for excimer ablation lithography and have demonstrated this by patterning a gear structure in an SU-8 resist layer with an aspect ratio of 4.5. For the first time, we have shown that the laser micromachining technique has the potential to cleanly remove SU-8 after electroplating a microstructure with copper.
WOLoc: WiFi-only outdoor localization using crowdsensed hotspot labels

Given the ever-expanding scale of WiFi deployments in metropolitan areas, we have reached the point where accurate GPS-free outdoor localization becomes possible by relying solely on the WiFi infrastructure. Nevertheless, the existing industrial practices do not seem to have the right implementation to achieve an adequate accuracy, while the academic researches that are mostly attracted by indoor localization have largely neglected this outdoor aspect. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a solution that offers meter-level accuracy, by holistically treating the large number of WiFi hotspot labels gather by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, we accommodate all the labeled and unlabeled data for a given area using a semi-supervised manifold learning technique, and the output concerning the unlabeled part will become the estimated locations for both users and WiFi hotspots. We conduct extensive experiments with WOLoc in several outdoor areas, and the results have strongly indicated the efficacy of our solution.