Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Properties of Soft Nanomaterials: Thermal Measurement Design

Soft materials like hydrogels have multiple tunable material properties because of their unique structures. Due to the ability to respond to stimuli like temperature or chemical environment, they have numerous applications in different fields like delivering drugs inside the human body and other medical uses. Details of the thermal transport mechanisms, as well as the overall thermal properties, are critical for a variety of applications. Multiproperty measurements elucidate the underlying transport mechanisms in the soft materials. This research demonstrates a new methodology of measuring thermal properties of soft materials. This work uses the 3ω method [1,2] for measuring the thermal conductivity of soft hydrogels. In the 3ω method, heat is generated in thin heater line excited by a sinusoidal current at a frequency of ω. We measure the voltage response at the third harmonic of the input frequency (3ω). This response is related to the temperature rise and thus the thermal properties. Once the measurement technique is optimized, PVA-PVP hydrogels are tested. Using a 2D mathematical model of the heat transfer, the thermal conductivities of soft material are calculated from the experimental data. Based on the methodology built during this research, this technique is available now for other soft materials beyond hydrogels and the precision of this methodology can be improved by further studies. KEYWORDS Soft Material, Hydrogels, Heat Transfer, 3 Omega Method, Thermal Conductivity, Thermal Measurement, Nano Technology REFERENCES [1] Cahill, D. , & Pohl, R. (1987). Thermal conductivity of amorphous solids above the plateau. Physical Review B, 35(8), 7. Retrieved from http://prb.aps. org/abstract/PRB/v35/i8/p4067_1 [2] Dames, C. (2013). CHAPTER 2 MEASURING THE THERMAL CONDUCTIVITY OF THIN FILMS : 3 OMEGA AND RELATED ELECTROTHERMAL METHODS. In G. Chen, V. Prasad, & Y. Jolutia (Eds.), Annual Review of Heat Transfer (XVI., pp. 7–49). Begell home, Inc. [3]Endo, T. , Ikeda, R. , Yanagida, Y. , & Hatsuzawa, T. (2008). Stimuli-responsive hydrogel-silver nanoparticles composite for development of localized surface plasmon resonance-based optical biosensor. Analytica Chimica Acta, 611(2), 205–11. doi:10.1016/j.aca. 2008.01. 078 [4]Gharagozloo, P. E. , Eaton, J. K. , & Goodson, K. E. (2008). Diffusion, aggregation, and the thermal conductivity of nanofluids. Applied Physics Letters, 93(10), 103110. doi:10.1063/1.2977868
RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more

This article summarizes our progress with RegulonDB (http://regulondb.ccg.unam.mx/) during the past 2 years. We have kept up-to-date the knowledge from the published literature regarding transcriptional regulation in Escherichia coli K-12. We have maintained and expanded our curation efforts to improve the breadth and quality of the encoded experimental knowledge, and we have implemented criteria for the quality of our computational predictions. Regulatory phrases now provide high-level descriptions of regulatory regions. We expanded the assignment of quality to various sources of evidence, particularly for knowledge generated through high-throughput (HT) technology. Based on our analysis of most relevant methods, we defined rules for determining the quality of evidence when multiple independent sources support an entry. With this latest release of RegulonDB, we present a new highly reliable larger collection of transcription start sites, a result of our experimental HT genome-wide efforts. These improvements, together with several novel enhancements (the tracks display, uploading format and curational guidelines), address the challenges of incorporating HT-generated knowledge into RegulonDB. Information on the evolutionary conservation of regulatory elements is also available now. Altogether, RegulonDB version 8.0 is a much better home for integrating knowledge on gene regulation from the sources of information currently available.
Cognitive function and symptoms in adults and adolescents in relation to rf radiation from UMTS base stations

There is widespread public concern about the potential adverse health effects of mobile phones in general and their associated base stations in particular. This study was designed to investigate the acute effects of radio frequency (RF) electromagnetic fields (EMF) emitted by the Universal Mobile Telecommunication System (UMTS) mobile phone base stations on human cognitive function and symptoms. Forty adolescents (15–16 years) and 40 adults (25–40 years) were exposed to four conditions: (1) sham, (2) a Continuous Wave (CW) at 2140 MHz, (3) a signal at 2140 MHz modulated as UMTS and (4) UMTS at 2140 MHz including all control features in a randomized, double blinded cross‐over design. Each exposure lasted 45 min. During exposure the participants performed different cognitive tasks with the Trail Making B (TMB) test as the main outcome and completed a questionnaire measuring self reported subjective symptoms. No statistically significant differences between the UMTS and sham conditions were found for performance on TMB. For the adults, the estimated difference between UMTS and sham was −3.2% (−9.2%; 2.9%) and for the adolescents 5.5% (−1.1%; 12.2%). No significant changes were found in any of the cognitive tasks. An increase in ‘headache rating’ was observed when data from the adolescents and adults were combined (P = 0.027), an effect that may be due to differences at baseline. In conclusion, the primary hypothesis that UMTS radiation reduces general performance in the TMB test was not confirmed. However, we suggest that the hypothesis of subjective symptoms and EMF exposure needs further research. Bioelectromagnetics 29:257–267, 2008. © 2007 Wiley‐Liss, Inc.
Fine Grained Authorization Through Predicated Grants

Authorization in SQL is currently at the level of tables or columns. Many applications need a finer level of control. We propose a model for fine-grained authorization based on adding predicates to authorization grants. Our model supports predicated authorization to specific columns, cell-level authorization with nullification, authorization for function/procedure execution, and grants with grant option. Our model also incorporates other novel features, such as query defined user groups, and authorization groups, which are designed to simplify administration of authorizations. Our model is designed to be a strict generalization of the current SQL authorization mechanism.
Supervised Learning from Incomplete Data using Expectation Maximization ( EM ) Algorithm EE 645 Fall 99 Final Project Report

In this project, we look at the problem of learning from incomplete data in which the labels of the data are missing. The idea is basically based on the statistical perspectives of maximum likelihood and Bayes' theorem. Instead of neural networks, we want to look at a different approach to missing data based on statistical analysis. Some algorithms derived from the likelihood-based framework will be described and applied to clustering and classification problems on incomplete data. These algorithms are based on mixture modeling and we will focus on the Expectation-Maximization (EM) algorithm [1, 2](Dempster et al., 1977), which is an iterative algorithm both for the estimation of mixture components and for coping with the missing data.
Rapid speaker adaptation using regression-tree based spectral peak alignment

In this paper, regression-tree based spectral peak alignment is proposed for rapid speaker adaptation using the linearization of VTLN. Two different regression classes are investigated: phonetic classes (using combined knowledge and data-driven techniques) and mixture classes. Compared to MLLR and VTLN, improved performance can be obtained for both supervised and unsupervised adaptations on both medium vocabulary and connected digits recognition tasks. To further improve the performance, MLLR was integrated into this regression-tree based peak alignment. Experimental results show that the performance improvements can be achieved even with limited adaptation data.
Tracking and Predictive Display for a Remote Operated Robot using Uncalibrated Video

Delays in the visual feedback can seriously impact operator performance in telerobotics. In predictive display synthesized visual feedback is rendered immediately in response to operator motions. In this paper we present a system using a geometric and appearance model that is captured using structure-from-motion by an uncalibrated camera. The geometric model is integrated into a registration based tracking algorithm that allows stable tracking of full 3D pose of the robot. Experimentally we show that predictive scene views can be rendered with both high visual fidelity and metric accuracy.
Indecomposable prefix codes and prime trees

In this paper we introduce a notion of divisibility and primality on k-ary trees and we nd a relation between indecomposable preex code and prime trees. This relation allows to work on trees instead than directly on preex codes. In this way we can prove a density theorem for indecomposable preex codes and we can give an algorithm to test the indecomposability of a maximal preex code.
“Not a nice experience, not at all”: Underprivileged women's experiences of being confronted with cervical cancer

Abstract Objective: Research on women's experiences of cervical cancer as a health problem focuses primarily on cervical screening, while their experiences of cervical cancer as a disease seem to be underresearched. This study addresses this understudied area and aims to yield insight into the experiences of underprivileged women confronted with this disease. Method: Our study was qualitative, exploratory, and contextual with descriptive and interpretive elements. The context of the study was a radiation oncology department at an academic hospital in the Gauteng Province of South Africa. The target population was all patients diagnosed with cervical cancer referred for treatment at the specific hospital. Convenience sampling selected participants typical of the population. Nineteen open-ended unstructured interviews were conducted after demographic data were collected. One question was posed: “Please tell me what made you think there was something wrong with you? ” Thematic analyses were used to analyze the data. Results: The average age of participants was 47.2 years, with a youngest participant of 29 and oldest of 70. Three themes arose from the data: experiencing the symptoms of cervical cancer, hearing the bad news, and what remained unsaid. Significance of Results: Being confronted with cervical cancer is a traumatic experience. Women suffer unexplained, severe, humiliating vaginal bleeding and unrelieved pain. They have to face a dual healthcare system: on the one hand, unaffordable effective private healthcare and, on the other, the free-of-cost public healthcare that failed them. The bad news of their cervical cancer is most commonly broken in a kind and sympathetic manner, yet misunderstandings and a need for additional information prevail after the bad news conversation. Having to inform their families of their cancer adds to their suffering.
Parameter Server for Distributed Machine Learning

We propose a parameter server framework to solve distributed machine learning problems. Both data and workload are distributed into client nodes, while server nodes maintain globally shared parameters, which are represented as sparse vectors and matrices. The framework manages asynchronous data communications between clients and servers. Flexible consistency models, elastic scalability and fault tolerance are supported by this framework. We present algorithms and theoretical analysis for challenging nonconvex and nonsmooth problems. To demonstrate the scalability of the proposed framework, we show experimental results on real data with billions of parameters.
General Parameterized Time-Frequency Transform

Interest in parameterized time-frequency analysis for non-stationary signal processing is increasing steadily. An important advantage of such analysis is to provide highly concentrated time-frequency representation with signal-dependent resolution. In this paper, a general scheme, named as general parameterized time-frequency transform (GPTF transform), is proposed for carrying out parameterized time-frequency analysis. The GPTF transform is defined by applying generalized kernel based rotation operator and shift operator. It provides the availability of a single generalized time-frequency transform for applications on signals of different natures. Furthermore, by replacing kernel function, it facilitates the implementation of various parameterized time - frequency transforms from the same standpoint. The desirable properties and the dual definition in the frequency domain of GPTF transform are also described in this paper. One of the advantages of the GPTF transform is that the generalized kernel can be customized to characterize the time - frequency signature of non-stationary signal. As different kernel formulation has bias toward the signal to be analyzed, a proper kernel is vital to the GPTF. Thus, several potential kernels are provided and discussed in this paper to develop the desired parameterized time - frequency transforms. In real applications, it is desired to identify proper kernel with respect to the considered signal. This motivates us to propose an effective method to identify the kernel for the GPTF.
Improving spam filtering by combining Naive Bayes with simple k-nearest neighbor searches

Using naive Bayes for email classification has become very popular within the last few months. They are quite easy to implement and very efficient. In this paper we want to present empirical results of email classification using a combination of naive Bayes and k-nearest neighbor searches. Using this technique we show that the accuracy of a Bayes filter can be improved slightly for a high number of features and significantly for a small number of features.
X – RAY DIFFRACTION ANALYSIS OF SLUDGE AND DUST PRODUCED IN STEELWORKS

Before handling, storage, disposal, these wastes need to be analyzed. In many cases, it is not enough to know only their chemical composition, but it is necessary to determine their mineralogical composition, or to know exactly what is their crystallographic structure. This can be well done using X – ray diffraction device. Examples of sludge from Steelshop and Tin Mill, and dust from Sintering and Steelshop demonstrate current opportunities of X – ray diffraction. The analysis was done using programs of "search - match" type with diffraction database PDF2 – 2004 and crystallographic COD database. Diffraction records were refined by Rietveld method in the TOPAS program.
Heap Analysis Design: An Empirical Approach

Despite extensive research the construction of a precise and scalable static heap analysis for object-oriented programs remains an open problem. This paper argues that much of the difficulty is the result of empirically unvalidated and inappropriate design decisions. We examine three of them and determine that in practice: (1) strong updates are not necessary for obtaining precise results (2) fixed naming schemes for defining abstract memory locations are fundamentally limiting and are not required for efficiency, and (3) shape/sharing in the heap is generally simple and can be described using a simple abstract heap model. Using these results we construct a new heap analysis and experimentally demonstrate that it is both precise, capable of supporting program optimizations/tools which require sophisticated information on the structure of the heap, and scalable, allowing the analysis to be run on real world programs such as luindex/lusearch from DaCapo.
Audio modeling based on delayed sinusoids

In this work, we present an evolution of the Damped and Delayed Sinusoidal (DDS) model introduced within the framework of the general signal modeling. This model, named the Partial Damped and Delayed Sinusoidal (PDDS) model, takes into account a single time delay parameter for a set (sum) of damped sinusoids. The proposed modification is more consistent with the transient audio modeling problem. The validity of the approach is shown by its comparison with the well-known Exponentially Damped Sinusoids (EDS) approach. Finally, the performances of the three models based high-resolution parameter estimation algorithms are compared on synthetic fast time-varying signals, and on two typical audio transients.
A hierarchical dynamic programming approach to fixed-rate, entropy-coded quantization

In quantization of any source with a nonuniform probability density function, the entropy coding of the quantizer output can result in a substantial decrease in bit rate. A straightforward entropy coding scheme faces us with the problem of variable data rate. A solution in a space of dimensionality N is to select an appropriate subset of elements in the N-fold Cartesian product of a scalar quantizer and represent its elements with codewords of the same length. The drawback is that the search/addressing of this scheme can no longer be achieved independently along the one-dimensional subspaces. A reasonable rule is to select the N-fold symbols of the highest probability. For a memoryless source, this is equivalent to selecting the N-fold symbols with the lowest additive self-information. In this case, due to the additivity property of the self-information, the selected subset has a high degree of structure which can be used to substantially decrease the search/addressing complexity. A dynamic programming approach is used to exploit this structure. We build our recursive structure required for the dynamic programming in a hierarchy of levels. This results in several benefits over the conventional trellis-based approaches. Using this structure, we develop efficient rules (based on aggregating the states) to substantially reduce the search/addressing complexities while keeping the degradation in performance negligible.
TSPAN1 protein expression: a significant prognostic indicator for patients with colorectal adenocarcinoma.

AIM To determine if TSPAN1 overexpression is associated with clinicopathological and prognostic factors in human colorectal adenocarcinoma. METHODS Total RNA was extracted in 20 human adenocarcinoma tissues for TSPAN1 mRNA assay by RT-PCR. Eighty-eight specimens of human colorectal adenocarcinoma were surgically removed. TSPAN1 protein levels in cancer tissues were determined by immunohistochemistry using a polyclonal antibody against self-prepared TSPAN1. The correlation between TSPAN1 expression and the clinicopathological factors and the overall survival rate was analyzed by univariate and multivariate assay. RESULTS TSPAN1 mRNA was detected in 90.0% (18/20) of cancerous tissues. The light density of TSPAN1 mRNA expression levels was 0.89 +/- 0.30 in adenocarcinoma by gel-image system. TSPAN1 protein expression was detected in 78.41% (69/88) and weakly expressed in 40% normal colorectal tissues. There were significant differences between colorectal adenocarcinoma and normal control epithelium (P < 0.05). TSPAN1 protein expression in colorectal cancerous tissue was significantly correlated with the histological grade, cell expression PCNA, lymph nodal metastasis and TNM staging of the disease. Patients with TSPAN1 protein overexpression had a significantly shorter survival period than that in patients with TSPAN1 protein negative or weak expression, respectively (P < 0.05). Furthermore, by multivariate analysis, TSPAN1 protein expression demonstrated an independent prognostic factor for human colorectal cancers (P < 0.05, relative risk 0.755; 95% confidence interval 0.302-1.208). CONCLUSION The expression of TSPAN1 gene is increased in colorectal carcinoma, suggesting that TSPAN1 might serve as an independent prognostic factor for the colorectal adenocarcinoma patients.
Data-driven structural priors for shape completion

Acquiring 3D geometry of an object is a tedious and time-consuming task, typically requiring scanning the surface from multiple viewpoints. In this work we focus on reconstructing complete geometry from a single scan acquired with a low-quality consumer-level scanning device. Our method uses a collection of example 3D shapes to build structural part-based priors that are necessary to complete the shape. In our representation, we associate a local coordinate system to each part and learn the distribution of positions and orientations of all the other parts from the database, which implicitly also defines positions of symmetry planes and symmetry axes. At the inference stage, this knowledge enables us to analyze incomplete point clouds with substantial occlusions, because observing only a few regions is still sufficient to infer the global structure. Once the parts and the symmetries are estimated, both data sources, symmetry and database, are fused to complete the point cloud. We evaluate our technique on a synthetic dataset containing 481 shapes, and on real scans acquired with a Kinect scanner. Our method demonstrates high accuracy for the estimated part structure and detected symmetries, enabling higher quality shape completions in comparison to alternative techniques.
Adaptive Power Management in Energy Harvesting Systems

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and time-varying amount of available energy. Instead of solving the optimization problem on-line which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal on-line controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor node
Ontological based webpage segmentation

In World Wide Web, images come together with valuable surrounding contextual information such as texts. There are very few efforts performed to address problems in mining of the images with their surrounding contextual information; although this information has long been extracted for wide range of uses such as image annotation, clustering of images, inference of image semantic content and image indexing. In this proposal, a genuine ontology based webpage segmentation algorithm is developed for extracting web images with its associated contextual information according to its semantic characteristics (e.g. synonymous words) as they appear on webpages.