Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural performance of nano concrete-steel sandwich wall

Concrete is extensively used in the construction of infrastructures, however formation and development of cracks undermines the integrity of the structure. Thus, both improving the mechanical properties of this construction material as well as structural health monitoring of structures are essential tasks to be tackled. The research covered in this thesis is concerned with mechanical properties of carbon nanofiber reinforced concrete and the effect of this type of concrete on structural performance of the composite steel-concrete sandwich (SC) system. The use of nanofibers such as Carbon Nanofiber (CNF) within cementitious materials is found to be effective in enhancing the mechanical properties of the cementitious material as well as enhancing the sensing ability of the cementitious composites. Despite the abundant experimental work on nanofiber reinforced cementitious composites by researchers, their use within concrete has not been fully addressed. Therefore, the significance of this research is to assess mechanical properties of nanoreinforced concrete along with its sensing capability. The steel-concrete sandwich system consists of thick concrete core with exterior steel faceplates acting as reinforcement. The steel faceplates are anchored to the concrete core with shear connectors. This study presents the structural performance of the SC element with fiber reinforced concrete (FRC) core using both single fiber and hybrid fiber (i.e. consisting of two types of fibers) in the core. For this study carbon nanofiber and steel fiber, which is conventionally used in practice, are used for the FRC. Static tests were conducted on eight SC beams with different concrete types. In addition to studying the structural performance of the SC element with fiber reinforced concrete, the self-sensing capability of beams with CNF reinforced concrete core were assessed. Furthermore, finite element analysis was conducted to evaluate the effect of some design parameters on the behaviour of SC element. The outcome of this thesis enhances the current knowledge on the use of nanofibers in civil engineering industry as nano reinforcement and nanofilaments within cementitious materials, typically concrete and it will contribute to the understanding of the effect of CNF on concrete mechanical properties. This research laid the groundwork for additional in-depth study on using carbon nanofiber reinforced concrete within structural members and determination of their effect as nanofilaments on the self-sensing capability of the structural element.
Optimizing particle collection for enhanced surface-based biosensors

In this article, electrode structures that combine dielectrophoretic effects with electrohydrodynamic fluid flow to concentrate particles on active sensor surfaces were presented. To optimize the collection effect on a surface, a novel electrode configuration called zipper electrodes has been developed. The local enrichment effect of these electrodes is such that particles at local concentration of 5/spl times/10/sup 3/ spores/mL can be collected using a single electrode pad. The fluid flow induced in the bulk flow is an efficient mechanism especially for very small particles, since the dielectrophoretic forces working on these particles are normally very small and do not penetrate the liquid as far as the vortex induced by combined dielectrophoresis/fluid flow does. The bulk flow makes it also a very versatile method that can extract a wide range of particles out of the liquid.
Tiled Virtual Memory for UNIX

M.Ly computer applications require the manipulation of large data arrays. These applÍcations can behave b-adly under a paged virtual memory (vM) system, due to poor memory access _p¿tterns. One solution to this problem is tiling, a technique in which the arays are partitioned into sub-arrays that map one-to-one with VM -pages. Software implementations of tiling have been described in the literature, but none pioviAe the speed and application transparency of a hardware solution. We have implemented a hardware based, tiled VM within a version of the UNIX operating system. Based on a novel memory management unit and supporting kemel software, this tiled VM has proven to be an efficient environment for manipulaiing 2-dimensional arrays of data In this paper we discuss the kemel changes required to implement our tiled VM. We then compare tiled and paged versions of our VM system, and show that tiling results in a 5Q-fold reduction in working set size for a common class of image processing algorithms.
Spherical Classification of Remote Sensing Data

Real data are often characterized by high dimensional feature vectors. However, such data contain redundant information that may not be beneficial for analysis algorithms. As such, feature transformation arises in related fields of study, including geoscientific applications, as a means to capture the few characteristics that are useful for pattern analysis algorithms. In this study, we investigate the transformation of remote sensing images to a coordinate system that preserves local pixel relationships on a constant curvature space. The transformation is performed using the spherical embedding method. Based on the properties of spherical surfaces and their relationship with local tangent spaces, we further propose two geometrical spherical nearest neighbor metrics for classification. As part of experimental validation, results on modeling multi-class multispectral and hyperspectral data using the proposed spherical Mahalanobis nearest neighbor rule and the spherical discriminant adaptive nearest neighbor rule are presented. The results indicate that the proposed metrics yield better classification accuracies on lower dimensional spherical surfaces. This promising outcome serves as a motivation for further development of new models to analyze remote sensing images in spherical manifolds.
Active and unsupervised learning for automatic speech recognition

State-of-the-art speech recognition systems are trained us-ing human transcriptions of speech utterances. In this paper, we describe a method to combine active and unsupervised learning for automatic speech recognition (ASR). The goal is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function. For unsupervised learning, we utilize the remaining untranscribed data by using their ASR output and word conﬁdence scores. Our experiments show that the amount of labeled data needed for a given word accuracy can be reduced by 75% by combining active and unsupervised learning.
Automated Tiling of Unstructured Mesh Computations with Application to Seismological Modeling

Sparse tiling is a technique to fuse loops that access common data, thus increasing data locality. Unlike traditional loop fusion or blocking, the loops may have different iteration spaces and access shared datasets through indirect memory accesses, such as A[map[i]]—hence the name “sparse. ” One notable example of such loops arises in discontinuous-Galerkin finite element methods, because of the computation of numerical integrals over different domains (e.g., cells, facets). The major challenge with sparse tiling is implementation—not only is it cumbersome to understand and synthesize, but it is also onerous to maintain and generalize, as it requires a complete rewrite of the bulk of the numerical computation. In this article, we propose an approach to extend the applicability of sparse tiling based on raising the level of abstraction. Through a sequence of compiler passes, the mathematical specification of a problem is progressively lowered, and eventually sparse-tiled C for-loops are generated. Besides automation, we advance the state-of-the-art by introducing a revisited, more efficient sparse tiling algorithm; support for distributed-memory parallelism; a range of fine-grained optimizations for increased runtime performance; implementation in a publicly available library, SLOPE; and an in-depth study of the performance impact in Seigen, a real-world elastic wave equation solver for seismological problems, which shows speed-ups up to 1.28× on a platform consisting of 896 Intel Broadwell cores.
Feature-based models for improving the quality of noisy training data for relation extraction

Supervised relation extraction from text relies on annotated data. Distant supervision is a scheme to obtain noisy training data by using a knowledge base of relational tuples as the ground truth and finding entity pair matches in a text corpus. We propose and evaluate two feature-based models for increasing the quality of distant supervision extraction patterns. The first model is an extension of a hierarchical topic model that induces background, relation specific and argument-pair specific feature distributions. The second model is a perceptron, trained to match an objective function that enforces two constraints: 1) an at-least-one semantics, i.e. at least one training example per relational tuple is assumed to be correct; 2) high scores for a dedicated NIL label that accounts for the noise in the training data. For both algorithms, neither explicit negative data nor the ratio of negatives has to be provided. Both algorithms give improvements over a maximum likelihood baseline as well as over a previous topic model without features, evaluated on TAC KBP data.
Engaging young adult clients of community pharmacies for HIV screening in Coastal Kenya: a cross-sectional study

Background Adults in developing countries frequently use community pharmacies as the first and often only source of care. The objective of this study was to assess the success of pharmacy referrals and uptake of HIV testing by young adult clients of community pharmacies in the context of a screening programme for acute HIV-1 infection (AHI). Methods We requested five pharmacies to refer clients meeting predefined criteria (ie, 18–29 years of age and requesting treatment for fever, diarrhoea, sexually transmitted infection (STI) symptoms or body pains) for HIV-1 testing and AHI screening at selected clinics. Using multivariable logistical regression, we determined client characteristics associated with HIV-1 test uptake. Results From February through July 2013, 1490 pharmacy clients met targeting criteria (range of weekly averages across pharmacies: 4–35). Of these, 1074 (72%) accepted a referral coupon, 377 (25%) reported at a study clinic, 353 (24%) were HIV-1 tested and 127 (9%) met criteria for the AHI study. Of those tested, 14 (4.0%) were HIV-1 infected. Test uptake varied significantly by referring pharmacy and was higher for clients who presented at the pharmacy without a prescription versus those with a prescription, and for clients who sought care for STI symptoms. Conclusions About a quarter of targeted pharmacy clients took up HIV-1 testing. Clients seeking care directly at the pharmacy (ie, without a prescription) and those with STI symptoms were more likely to take up HIV-1 testing. Engagement of adult pharmacy clients for HIV-1 screening may identify undiagnosed individuals and offers opportunities for HIV-1 prevention research.
TypeDevil: Dynamic Type Inconsistency Analysis for JavaScript

Dynamic languages, such as JavaScript, give programmers the freedom to ignore types, and enable them to write concise code in short time. Despite this freedom, many programs follow implicit type rules, for example, that a function has a particular signature or that a property has a particular type. Violations of such implicit type rules often correlate with problems in the program. This paper presents Type Devil, a mostly dynamic analysis that warns developers about inconsistent types. The key idea is to assign a set of observed types to each variable, property, and function, to merge types based in their structure, and to warn developers about variables, properties, and functions that have inconsistent types. To deal with the pervasiveness of polymorphic behavior in real-world JavaScript programs, we present a set of techniques to remove spurious warnings and to merge related warnings. Applying Type Devil to widely used benchmark suites and real-world web applications reveals 15 problematic type inconsistencies, including correctness problems, performance problems, and dangerous coding practices.
Cyclic pure greedy algorithms for recovering compressively sampled sparse signals

The pure greedy algorithms matching pursuit (MP) and complementary MP (CompMP) are extremely computationally simple, but can perform poorly in solving the linear inverse problems posed by the recovery of compressively sampled sparse signals. We show that by applying a cyclic minimization principle, the performance of both are significantly improved while remaining computationally simple. Our simulations show that while MP and CompMP may not be competitive with state-of-the-art recovery algorithms, their cyclic variations are. We discuss ways in which their complexity can be further reduced, but our simulations show these can hurt recovery performance. Finally, we derive the exact recovery condition of CompMP and both cyclic algorithms.
Reducing Offset and Bias Instability in Lorentz Force Magnetic Sensors Through Bias Chopping

In this paper, we describe a method for reducing offset and offset-related 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> (flicker) noise in micromechanical resonator-based Lorentz force magnetometers by chopping the dc bias voltage applied to the resonator. This 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise is shown to determine the stability of the sensor’s output at long averaging times. The magnetometer is operated at a 40-Hz frequency offset from its 62.88-kHz resonant frequency. At 4.6-mA bias current, the input referred noise is 70 nT/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz limited by white electronic noise. Using the proposed bias chopping method, the sensor’s offset is reduced from 310 to <inline-formula> <tex-math notation="LaTeX">$15~\mu \text{T}$ </tex-math></inline-formula>, and the bias instability is reduced from 27 to 7 nT. The averaging time to reach the bias instability increases from 18 to 290 s as a result of chopping. The root cause of 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise is demonstrated to be 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise on the ac and dc bias voltages applied to the sensor. While the sensor’s readout is modulated at the 62.92-kHz excitation frequency and could therefore be expected to be free from 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise, the baseband 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise on these voltage sources is shown to be up-converted to the excitation frequency. Up-conversion occurs through the existence of fabrication imperfections that lead to mismatches in the sensing capacitances, resistances, and parasitic feedthrough capacitances of the sensor. By chopping the bias voltage, the Lorentz force signal is separated from the offset and 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise produced by these error sources. This technique is a generic method to reduce 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise and bias instability in sensors based on micromechanical resonators. [2016-0184]
Verification and Implementation of Language-Based Deception Indicators in Civil and Criminal Narratives

Our goal is to use natural language processing to identify deceptive and non-deceptive passages in transcribed narratives. We begin by motivating an analysis of language-based deception that relies on specific linguistic indicators to discover deceptive statements. The indicator tags are assigned to a document using a mix of automated and manual methods. Once the tags are assigned, an interpreter automatically discriminates between deceptive and truthful statements based on tag densities. The texts used in our study come entirely from "real world" sources---criminal statements, police interrogations and legal testimony. The corpus was hand-tagged for the truth value of all propositions that could be externally verified as true or false. Classification and Regression Tree techniques suggest that the approach is feasible, with the model able to identify 74.9% of the T/F propositions correctly. Implementation of an automatic tagger with a large subset of tags performed well on test data, producing an average score of 68.6% recall and 85.3% precision when compared to the performance of human taggers on the same subset.
DualNet: Domain-invariant network for visual question answering

Visual question answering (VQA) tasks use two types of images: abstract (illustrations) and real. Domain-specific differences exist between the two types of images with respect to “objectness,” “texture,” and “color. ” Therefore, achieving similar performance by applying methods developed for real images to abstract images, and vice versa, is difficult. This is a critical problem in VQA, because image features are crucial clues for correctly answering the questions about the images. However, an effective, domain-invariant method can provide insight into the high-level reasoning required for VQA. We thus propose a method called DualNet that demonstrates performance that is invariant to the differences in real and abstract scene domains. Experimental results show that DualNet outperforms state-of-the-art methods, especially for the abstract images category.
On sequential strategies for loss functions with memory

The sequential decision problem is studied for loss functions with memory and finite action spaces. Based on the theory of Markov decision processes (MDP's), off-line reference strategies are characterized. An infinite horizon on-line strategy, with corresponding normalized "regret" which is upper-bounded by an O(n/sup -1/3/) term for an arbitrary individual sequence of observations of length n, is derived.
Analysis of protein chameleon sequence characteristics

Conversion of local structural state of a protein from an α-helix to a β-strand is usually associated with a major change in the tertiary structure. Similar changes were observed during the self assembly of amyloidogenic proteins to form fibrils, which are implicated in severe diseases conditions, e.g., Alzheimer disease. Studies have emphasized that certain protein sequence fragments known as chameleon sequences do not have a strong preference for either helical or the extended conformations. Surprisingly, the information on the local sequence neighborhood can be used to predict their secondary at a high accuracy level. Here we report a large scale-analysis of chameleon sequences to estimate their propensities to be associated with different local structural states such as α -helices, β-strands and coils. With the help of the propensity information derived from the amino acid composition, we underline their complexity, as more than one quarter of them prefers coil state over to the regular secondary structures. About half of them show preference for both α-helix and β-sheet conformations and either of these two states is favored by the rest.
Evaluating the usability and security of a graphical one-time PIN system

Traditional Personal Identification Numbers (PINs) are widely used, but the attacks in which they are captured have been increasing. One-time PINs offer better security, but potentially create greater workload for users. In this paper, we present an independent evaluation of a commercial system that makes PINs more resistant to observation attacks by using graphical passwords on a grid to generate a one-time PIN. 83 participants were asked to register with the system and log in at varying intervals. The successful login rate was approximately 91% after 3--4 days, and 97% after 9--10 days. Twenty five participants were retested after two years, and 27% of those were able to recall their pattern. We recorded 17 instances of failed attempts, and found that even though participants recalled the general shape of the pass-pattern in 13 of these instances, they could not recall its detailed location or sequence of cells. We conclude that GrIDsure is usable if people have one pass-pattern, but the level of security will depend on the context of use (it will work best in scenarios where repeated observations of transactions are unlikely), and the instructions given to users (without guidance, they are likely to chose from a small subset of the possible patterns which are easily guessed).
Flow of polymer melt blends through porous media

i The flow of molten polystyrene, poly(methyl meth­ acrylate) and three blends of these polymers has been studied in a rheogoniometer and in packed beds of uncon­ solidated spherical glass beads. One primary purpose of the study was to determine the behavior of polymer melt blends in porous media flow. The second major purpose of the work was to develop and test a new and powerful model for packed bed flow based upon the Huang generalized rheological constitutive equations. Rheological characterization of the pure polymers and blends was obtained using a Roberts-Weissenberg rheogoniometer. Shear and normal stresses were measured at shear rates from . 002 to 20 sec~* and temperatures from 180 to 220 °C. Both the four parameter Huang and three parameter Ellis equations of state provided ex­ cellent representations of the viscometric data. The Huang rheological equation was combined with an hydraulic radius capillary model for a porous medium leading to a generalized Darcy's law. This expression defined an effective non-Newtonian viscosity applicable to packed bed fluid flow. A packed bed friction factor ii and Reynolds number were developed to correlate ex­ perimental data. Pressure drop of the pure polymers and blends was measured over a range of flow rates through packed beds of various bead sizes and packing depths. Temperature was maintained at 20̂ -° G. Data for each pure polymer and blend was success­ fully correlated using a friction factor-Reynolds num­ ber relationship, which specifies that the product of these dimensionless quantities is constant. The value of this constant for pure poly(methyl methacrylate) was approximately one half the value for pure polystyrene and the three blends. No significant differences in the behavior of the pure polymers and blends was observed. The Huang equa­ tion of state provided excellent representation of molten polymer viscometric data and successfully cor­ related packed bed flow data. The difference in packed bed flow behavior of poly(methyl methacrylate) and the other materials was tentatively ascribed to unidenti­ fied surface interaction effects between the fluids and the packed beds. Using the defined expressions, pres­ sure drop for packed bed flow can be estimated from the rheological properties of the fluid and the physical properties of the packed bed. APPROVAL OF DISSERTATION FLOW OF POLYMER MELT BLENDS THROUGH POROUS MEDIA