Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compositional Look at the Human Gastrointestinal Microbiome and Immune Activation Parameters in HIV Infected Subjects

HIV progression is characterized by immune activation and microbial translocation. One factor that may be contributing to HIV progression could be a dysbiotic microbiome. We therefore hypothesized that the GI mucosal microbiome is altered in HIV patients and this alteration correlates with immune activation in HIV. 121 specimens were collected from 21 HIV positive and 22 control human subjects during colonoscopy. The composition of the lower gastrointestinal tract mucosal and luminal bacterial microbiome was characterized using 16S rDNA pyrosequencing and was correlated to clinical parameters as well as immune activation and circulating bacterial products in HIV patients on ART. The composition of the HIV microbiome was significantly different than that of controls; it was less diverse in the right colon and terminal ileum, and was characterized by loss of bacterial taxa that are typically considered commensals. In HIV samples, there was a gain of some pathogenic bacterial taxa. This is the first report characterizing the terminal ileal and colonic mucosal microbiome in HIV patients with next generation sequencing. Limitations include use of HIV-infected subjects on HAART therapy.
Region segmentation based radiographic detection of defects for gas turbine blades

Nondestructive testing (NDT) of mechanical structures is essential in a wide range of industries to ensure that the quality meets the design and operation requirements for safety and reliability. Since discovered by Röntgen in 1895, X-rays can be used to identify inner structures not only in medical imaging for human beings, but also in NDT for materials or objects. In the study of this paper, a direct digital radiography (DR) method is used to detect the defects in gas turbine blades. Aiming at the difficulty caused by the complex shape and uneven thickness, considering the limited size of flat-panel detectors of DR system, a region segmentation based method is presented in this paper. Using the proposed method, the radiographic sub-images can be obtained. By the reduction of scattering noise, the contrast of obtained radiographic image is enhanced and the defects are recognized. Then, quantitative and qualitative analyses are made for the detected defects. Finally, the locations of detected defects in reference to gas turbine blade are determined by the splicing of sub-images.
Hyper-Sharpening Based on Spectral Modulation

Hyperspectral (HS) image sharpening (namely hyper sharpening) with an auxiliary sensor, such as multispectral (MS) or panchromatic sensor, has attracted a great deal of attention for the past decade. A number of hyper-sharpening techniques, aiming at enhancing the spatial resolution of HS images, have been developed and demonstrated their effectiveness especially on synthetic or simulated data. Nevertheless, since the differences between different remote sensing systems or imaging conditions are complicate, it results in a serious spectral distortion when applying on real remote sensing data acquired by different sensors under different acquisition times or conditions. Unfortunately, very few works have considered this issue. In this paper, a new hyper-sharpening framework based on spectral modulation is proposed to better preserve spectral information when fusing with MS data acquired by a different sensor. The goal of this paper is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Two approaches, originating in MS pan-sharpening field, are introduced as examples under this framework, namely high-pass details injection model and band-dependent spatial-detail model. Experiments on three HS and MS datasets acquired by different platforms demonstrate that the proposed framework is beneficial to the spectral fidelity of the fused image compared with some state-of-the-art hyper-sharpening techniques. Meanwhile, it is also easy to implement and has a certain advantage in enhancing the spatial details.
Extreme wave runup on a vertical cliff

Wave impact and runup onto vertical obstacles are among the most important phenomena which must be taken into account in the design of coastal structures. From linear wave theory, we know that the wave amplitude on a vertical wall is twice the incident wave amplitude with weakly nonlinear theories bringing small corrections to this result. In this present study, however, we show that certain simple wave groups may produce much higher runups than previously predicted, with particular incident wave frequencies resulting in runup heights exceeding the initial wave amplitude by a factor of 5, suggesting that the notion of the design wave used in coastal structure design may need to be revisited. The results presented in this study can be considered as a note of caution for practitioners, on one side, and as a challenging novel material for theoreticians who work in the field of extreme wave‐coastal structure interaction.
Efficient feature extraction framework for EEG signals classification

Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.
Performance comparison of different codes in an ultrasonic positioning system using DS-CDMA

In this work, several Code Division Multiple Access encoding schemes are evaluated for their application in ultrasonic sensory systems based on the determination of times-of-flight. Proper encoding improves the performance of such systems in terms of noise immunity, capability of simultaneous measurements and precision in the distance measurements. Important applications that include encoded ultrasonic signals are obstacle detection, local positioning in ubiquitous computing or non-destructive testing. Here, Kasami, Golay and Loosey Synchronous codes are experimentally tested in a privacy-oriented ultrasonic Local Positioning System based on hyperbolic trilateration.
Modelling ecological communities as if they were DNA

Ecologists are interested in understanding and predicting how ecological communities change through time. While it might seem natural to measure this through changes in species' abundances, computational limitations mean transitions between community types are often modelled instead. We present an approach inspired by DNA substitution models that attempts to estimate historic interactions between species, and thus estimate turnover rates in ecological communities. Although our simulations show that the method has some limitations, our application to butterfly community data shows the method can detect signal in real data. Open source C++ code implementing the method is available at this http URL
Effects of Hippotherapy on Postural Stability, in Persons with Multiple Sclerosis: A Pilot Study

Purpose: The purpose of this pilot study was to examine the effectiveness of hippotherapy as an intervention for the treatment of postural instability in individuals with multiple sclerosis (MS). Subjects: A sample of convenience of 15 individuals with MS (24–72 years) were recruited from support groups and assessed for balance deficits. Methods: This study was a nonequivalent pretest-posttest comparison group design. Nine individuals (4 males, 5 females) received weekly hippotherapy intervention for 14 weeks. The other 6 individuals (2 males, 4 females) served as a comparison group. All participants were assessed with the Berg Balance Scale (BBS) and Tinetti Performance Oriented Mobility Assessment (POMA) at 0, 7, and 14 weeks. Results: The group receiving hippotherapy showed statistically significant improvement from pretest (0 week) to posttest (14 week) on the BBS (mean increase 9.15 points (x2 (2) = 8.82, p = 0.012)) and POMA scores (mean increase 5.13 (x2 (2) = 10.38, p = 0.006)). The comparison group had no significant changes on the BBS (mean increase 0.73 (x2 (2) = 0.40, p = 0.819)) or POMA (mean decrease 0.13 (x2 (2) = 1.41, p = 0.494)). A statistically significant difference was also found between the groups’ final BBS scores (treatment group median = 55.0, comparison group median 41.0), U = 7, r = −0.49. Discussion: Hippotherapy shows promise for the treatment of balance disorders in persons with MS. Further research is needed to refine protocols and selection criteria.
Patent Classification Using Ontology-Based Patent Network Analysis

Patent management is increasingly important for organizations to sustain their competitive advantage. The classification of patents is essential for patent management and industrial analysis. In this study, we propose a novel patent network-based classification method to analyze query patents and predict their classes. The proposed patent network, which contains various types of nodes that represent different features extracted from patent documents, is constructed based on the relationship metrics derived from patent metadata. The novel approach analyzes reachable nodes in the patent ontology network to calculate their relevance to query patents, after which it uses the modified k-nearest neighbor classifier to classify query patents. We evaluate the performance of the proposed approach on a test dataset of patent documents obtained from the United States Patent and Trademark Office (USPTO), and compare it with the performance of the three conventional methods. The results demonstrate that the proposed patent network-based approach outperforms the conventional approaches.
Assessing the Interestingness of Discovered Knowledge Using a Principled Objective Approach

When mining a large database, the number of patterns discovered can easily exceed the capabilities of a human user to identify interesting results. To address this problem, various techniques have been suggested to reduce and/or order the patterns prior to presenting them to the user. In this paper, our focus is on ranking summaries generated from a single dataset, where attributes can be generalized in many different ways and to many levels of granularity according to taxonomic hierarchies. We theoretically and empirically evaluate twelve diversity measures used as heuristic measures of interestingness for ranking summaries generated from databases. The twelve diversity measures have previously been utilized in various disciplines, such as information theory, statistics, ecology, and economics. We describe five principles that any measure must satisfy to be considered useful for ranking summaries. Theoretical results show that the proposed principles define a partial order on the ranked summaries in most cases, and in some cases, define a total order. Theoretical results also show that seven of the twelve diversity measures satisfy all of the five principles. We empirically analyze the rank order of the summaries as determined by each of the twelve measures. These empirical results show that the measures tend to rank the less complex summaries as most interesting. Finally, we demonstrate a technique, based upon our principles, for visualizing the relative interestingness of summaries.
Struggling and juggling: a comparison of student assessment loads across research and teaching-intensive universities

Abstract In spite of the rising tide of metrics in UK higher education, there has been scant attention paid to assessment loads, when evidence demonstrates that heavy demands lead to surface learning. Our study seeks to redress the situation by defining assessment loads and comparing them across research and teaching intensive universities. We clarify the concept of ‘assessment load’ in response to findings about high volumes of summative assessment on modular degrees. We define assessment load across whole undergraduate degrees, according to four measures: the volume of summative assessment; volume of formative assessment; proportion of examinations to coursework; number of different varieties of assessment. All four factors contribute to the weight of an assessment load, and influence students’ approaches to learning. Our research compares programme assessment data from 73 programmes in 14 UK universities, across two institutional categories. Research-intensives have higher summative assessment loads and a greater proportion of examinations; teaching-intensives have higher varieties of assessment. Formative assessment does not differ significantly across both university groups. These findings pose particular challenges for students in different parts of the sector. Our study questions the wisdom that ‘more’ is always better, proposing that lighter assessment loads may make room for ‘slow’ and deep learning.
Boolean decomposition in combinational logic synthesis

Today’s digital designs have reached a magnitude making it intractable for humans to develop designs on a gate level. Large designs are developed with help of hardware description languages like VHDL, or with other methods using high level of abstraction. Computer based synthesis tools are applied to transfer those descriptions to layouts for implementation in programmable circuits like Field Programmable Gate Array (FPGAs) or for implementation in full custom integrated circuits. In order to obtain a good implementation, it is important to perform optimization of the design during the transformation from high level description to layout. This thesis is in the area of Boolean decomposition. Decomposition is a technique used to increase the number of levels in a Boolean formula. Decomposition aims at representing a logic function by a multi-level expression with the least number of literals. A multi-level expression is usually simpler than a sum-of-product expression representing the same function. In many design styles, the implementation of a function is related to its multi-level expression. For instance, in complex gate CMOS, the number of transistors in the circuit is roughly proportional to the number of literals in its multi-level expression and therefore can be used as a measure of circuit area. One contribution of this thesis is a heuristic algorithm for disjoint decomposition of a Boolean function based on its representation as a Reduced Ordered Binary Decision Diagram (ROBDD). Two distinct features make the algorithm feasible for large functions. First, for an n-variable function, it checks only O(n) candidates for decomposition out of O(2) possible ones. A special strategy for selecting candidates makes it likely that all other decompositions are encoded in the selected ones. Second, the decompositions for the approved candidates are computed using a novel IntervalCut algorithm. This algorithm does not require re-ordering of the ROBDD. The experimental results on 582 benchmark functions show that the presented heuristic finds 95% of all decompositions on average. For 526 of those functions, it finds 100% of the decompositions. Another contribution of this thesis is in the area of XOR-based synthesis. In many cases, functions with embedded XOR logic have very large expressions in terms of AND, OR and NOT operations. Vice versa, functions which do not have embedded XOR logic might have larger representation if expressed using AND, XOR and NOT. Since both, AND-OR and AND-XOR, minimization algorithms are quite time-consuming for large functions, it is attractive to know a priori which of them should be used for a given function. In this thesis a solution to this problem is given. We formulate a sufficient condition for Boolean functions to have a decomposition of type f = (g⊕h)+ r, with the total number of product-terms in g, h and r smaller than the number of product-terms in f . This condition is used to develop an algorithm for deciding whether a given function is likely to benefit from XOR minimization.
Visual support system for selecting reactive elements in intelligent environments

Concerning gestural interaction in realistic environments there often is an offset between perceived and actual direction of pointing that makes it difficult to reliably select elements in the environment. This work presents a visual support system that provides feedback to a user gesturing freely in an environment and thus enabling reliable selection of and interaction with reactive elements in intelligent environments. A prototype has been created that is showcasing this feedback method based on gesture recognition using the Microsoft Kinect and feedback provision using a custom laser-robot. Finally an evaluation has been performed, in order to prove the efficiency of such a system, acquire usability feedback and determine potential learning effects for gesture-based interaction.
1 DISCRIMINATION AND SEQUENTIALIZATION OF EVENTS IN PERCEPTION

There are different conceptions of time in different scientific disciplines. Apart from millennia of philosophical discussions of time (cf. [1]), a particularly outstanding discrepancy persists between the concepts of time used in fundamental physical theories on the one hand and psychological theories of perception and cognition on the other. The prevailing concept of time in physical theories is that of an (abstract) time serving as a continuous background for the representation of events usually conceived as discrete, e.g. in terms of Boolean decisions of alternatives. In the spirit of Newton’s absolute time, the evolution of states or observables related to such events is described as a function of a one-dimensional parameter time. A tightly related philosophical conception of time is Kant’s notion of time a priori. Every physical (and other scientific) theory refers to an empirical reality to which measurement and observation provide access. A basic methodological cornerstone of such empirical access is the distinction between observing tools and an observed system. Although this distinction is not universally prescribed in particular instances, observation generally presupposes an outside perspective upon the observed system, which is sometimes called a third person perspective. By contrast, psychology and cognitive science additionally involve the option of a first person perspective, taking into account experiential aspects of perception and cognition. This includes an internal mode of knowledge acquisition sometimes called introspection. Such a perspective is often disregarded in contemporary research, but emphasized explicitly by phenomenologically oriented philosophers
Populating knowledge base with collective entity mentions: A graph-based approach

Populating a knowledge base with new entity mentions extracted from unstructured text can help enhance its coverage and freshness. It naturally consists of two subtasks, namely, fine-grained entity classification and entity linking. Existing studies often focus on one of these two subtasks and they usually populate entity mentions in the same text by implicitly assuming that they are independent. However, these entity mentions are often semantically related to each other and it would be better to populate them into the knowledge base collectively. For solving these problems, in this paper we propose an interdependence graph based and unified collective inference approach, called CIIGA, to populating a knowledge base with collective entities, which can jointly determine the proper locations of all entity mentions in the same text by exploiting their interdependence relationships. Experimental results show that this approach can achieve significant accuracy improvement, as compared to the baseline approach, APOLLO, on the task of knowledge base population with multiple entities.
Thermal Spreading Resistance of Arbitrary-Shape Heat Sources on a Half-Space: A Unified Approach

Thermal spreading/constriction resistance is an important phenomenon where a heat source/sink is in contact with a body. Thermal spreading resistance associated with heat transfer through the mechanical contact of two bodies occurs in a wide range of applications. The real contact area forms typically a few percent of the nominal contact area. In practice, due to random nature of contacting surfaces, the actual shape of microcontacts is unknown; therefore, it is advantageous to have a model applicable to any arbitrary-shape heat source. Starting from a half-space representation of the heat transfer problem, a compact model is proposed based on the generalization of the analytical solution of the spreading resistance of an elliptical source on a half-space. Using a “bottom-up” approach, unified relations are found that allow accurate calculation of spreading resistance over a wide variety of heat source shapes under both isoflux and isothermal conditions.
On the Usage of 16 Bit Indices in Recursively Stored Sparse Matrices

In our earlier work, we have investigated the feasibility of utilization of recursive partitioning in basic (BLAS oriented) sparse matrix computations, on multi-core cache-based computers. Following encouraging experimental results obtained for SPMV and SPSV operations, here we proceed to tune the storage format. To limit the memory bandwidth overhead we introduce usage of shorter (16 bit) indices in leaf sub matrices (at the end of the recursion). Experimental results obtained for the proposed approach on 8-core machines illustrate speed improvements, when performing sparse matrix-vector multiplication.
A Time and Space Efficient Algorithm for Approximately Solving Large Imperfect Information Games

This paper proposes a novel approach for computing an approximate equilibrium in a game of imperfect information. Our approach involves decomposition; that is, breaking the problem down into subproblems that can be solved independently. We compare our approach to other decomposition approaches, and illustrate that our approach is guaranteed to find an equilibrium when applied to the recovery problem.
Approximate optimal tracking control for a class of discrete-time non-affine systems based on GDHP algorithm

In this paper an infinite-time optimal tracking control scheme is presented for a class of discrete-time non-affine systems, where the optimal tracking controller is composed of two sub-controllers: the feedforward controller and the feedback controller. The feedforward controller is designed by implicit function theorem, while the feedback controller is realized by the greedy Globalized Dual Heuristic Programming (GDHP) iteration algorithm. To facilitate the implementation of the algorithm, three neural networks are adopted. The simulation results have demonstrated the validity of the proposed optimal tracking control scheme.
Predicting quality of wine based on chemical attributes

Ordinal data structure is difficult to leverage upon due to the fact that it has both the properties of regression and classification. In this paper, we devised a simple new method that leverages upon ordinal data structure of the data: Additive Logistic Regression (ALR). This method is used to predict the quality of Portuguese white wine based on the chemical attributes of the wine. We compare ALR to non-parametric methods. The results showed that our method, albeit simple, out-performs the non-parametric methods.