Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaches to Combine Techniques Used by Ensemble Learning Methods ⋆

Discuss approaches to combine techniques used by ensemble learning methods. Randomness which is used by Bagging and Random Forests is introduced into Adaboost to get robust performance under noisy situation. Declare that when the randomness introduced into AdaBoost equals to 100, the proposed algorithm turns out to be a Random Forests with weight update technique. Approaches are discussed to improve the performance of Random Forests with weight update technique introduced.
3D Reconstruction of Static Human Body with a Digital Camera

Nowadays the interest in 3D reconstruction and modeling of real humans is one of the most challenging problems and a topic of great interest. The human models are used for movies, video games or ergonomics applications and they are usually created with 3D scanner devices. In this paper a new method to reconstruct the shape of a static human is presented. Our approach is based on photogrammetric techniques and uses a sequence of images acquired around a standing person with a digital still video camera or with a camcorder. First the images are calibrated and orientated using a bundle adjustment. After the establishment of a stable adjusted image block, an image matching process is performed between consecutive triplets of images. Finally the 3D coordinates of the matched points are computed with a mean accuracy of ca 2 mm by forward ray intersection. The obtained point cloud can then be triangulated to generate a surface model of the body or a virtual human model can be fitted to the recovered 3D data. Results of the 3D human point cloud with pixel color information are presented.
Anti-TNF-α Drugs Differently Affect the TNFα-sTNFR System and Monocyte Subsets in Patients with Psoriasis

TNF-α has a central role in the development and maintenance of psoriatic plaques, and its serum levels correlate with disease activity. Anti-TNF-α drugs are, however, ineffective in a relevant percentage of patients for reasons that are currently unknown. To understand whether the response to anti-TNF-α drugs is influenced by the production of anti-drug antibodies or by the modulation of the TNFα-TNFα receptor system, and to identify changes in monocyte phenotype and activity, we analysed 119 psoriatic patients who either responded or did not respond to different anti-TNF-α therapies (adalimumab, etanercept or infliximab), and measured plasma levels of TNF-α, TNF-α soluble receptors, drug and anti-drug antibodies. Moreover, we analyzed the production of TNF-α and TNF-α soluble receptors by peripheral blood mononuclear cells (PBMCs), and characterized different monocyte populations. We found that: i) the drug levels varied between responders and non-responders; ii) anti-infliximab antibodies were present in 15% of infliximab-treated patients, while anti-etanercept or anti-adalimumab antibodies were never detected; iii) plasma TNF-α levels were higher in patients treated with etanercept compared to patients treated with adalimumab or infliximab; iv) PBMCs from patients responding to adalimumab and etanercept produced more TNF-α and sTNFRII in vitro than patients responding to infliximab; v) PBMCs from patients not responding to infliximab produce higher levels of TNF-α and sTNFRII than patients responding to infliximab; vi) anti- TNF-α drugs significantly altered monocyte subsets. A complex remodelling of the TNFα-TNFα receptor system thus takes place in patients treated with anti-TNF-α drugs, that involves either the production of anti-drug antibodies or the modulation of monocyte phenotype or inflammatory activity.
Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee

In this paper, we address learning problems for high dimensional data. Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling high-dimensionality challenge in machine learning. Recently, various non-oblivious randomized reduction methods have been developed and deployed for solving many numerical problems such as matrix product approximation, low-rank matrix approximation, etc. However, they are less explored for the machine learning tasks, e.g., classification. More seriously, the theoretical analysis of excess risk bounds for risk minimization, an important measure of generalization performance, has not been established for non-oblivious randomized reduction methods. It therefore remains an open problem what is the benefit of using them over previous oblivious random projection based approaches. To tackle these challenges, we propose an algorithmic framework for employing non-oblivious randomized reduction method for general empirical risk minimizing in machine learning tasks, where the original high-dimensional features are projected onto a random subspace that is derived from the data with a small matrix approximation error. We then derive the first excess risk bound for the proposed non-oblivious randomized reduction approach without requiring strong assumptions on the training data. The established excess risk bound exhibits that the proposed approach provides much better generalization performance and it also sheds more insights about different randomized reduction approaches. Finally, we conduct extensive experiments on both synthetic and real-world benchmark datasets, whose dimension scales to O(10^7), to demonstrate the efficacy of our proposed approach.
‘Let’s move:’ a systematic review of spouse-involved interventions to promote physical activity

Abstract Introduction: Physical inactivity elevates risk of chronic conditions and all-cause mortality. Given that health behavior changes often co-occur within couples, spouse-involved physical activity interventions have the potential to increase and maintain physical activity behavior of both partners. The purpose of this systematic review was to synthesize studies of physical activity interventions that include both partners in marital dyads to determine whether and how active involvement of both partners shapes the physical activity behavior change process. Methods: Randomized trials (N = 6) published through June 2017 were included in this systematic review under the following conditions: (1) both partners were directly involved in the intervention treatment group(s); (2) couples were randomly assigned to study conditions; and (3) physical activity or exercise was assessed as a primary outcome. Results: The six trials included 783 couples, with mean ages ranging from 27 to 71 years. These studies of couples and physical activity change were guided by established socio-behavioral perspectives and used a variety of strategies to promote behavior change. Overall, each of these studies demonstrated improvement in physical activity behavior, though improvements in physical activity did not differ consistently between intervention and comparison groups and sometimes were short-lived. Discussions: This systematic review provides initial support, albeit limited, that spouse-involved interventions can be effective in increasing physical activity. Addressing the needs and behavior change of both partners has the potential to increase effectiveness of couple-focused approaches to promote physical activity initiation and maintenance.
A Randomized Controlled Trial to Assess Pain and Magnetic Resonance Imaging-Based (MRI-Based) Structural Spine Changes in Low Back Pain Patients After Yoga Practice

Background The present study aimed at determining whether 12 weeks of yoga practice in patients with chronic LBP and MRI-based degenerative changes would result in differences in: (i) self-reported pain, anxiety, and spinal flexibility; and (ii) the structure of the discs or vertebrae. Material/Methods Sixty-two persons with MRI-proven degenerative intervertebral discs (group mean ±S.D., 36.2±6.4 years; 30 females) were randomly assigned to yoga and control groups. However, testing was conducted on only 40 subjects, so only their data are included in this study. The assessments were: self-reported pain, state anxiety, spinal flexibility, and MRI of the lumbosacral spine, performed using a 1.5 Tesla system with a spinal surface column. The yoga group was taught light exercises, physical postures, breathing techniques, and yoga relaxation techniques for 1 hour daily for 3 months. No intervention was given to the control group except for routine medical care. A repeated-measures analysis of variance (ANOVA) with post hoc analyses (which was Bonferroni-adjusted) was used. The Ethics Committee of Patanjali Research Foundation had approved the study which had been registered in the Clinical Trials Registry of India (CTRI/2012/11/003094). Results The yoga group showed a significant reduction in self-reported pain and state anxiety in a before/after comparison at 12 weeks. A few patients in both groups showed changes in the discs and vertebrae at post-intervention assessment. Conclusions Within 12 weeks, yoga practice reduced pain and state anxiety but did not alter MRI-proven changes in the intervertebral discs and in the vertebrae.
Distributed Collaborative Virtual Environment : PaulingWorld

This paper introduces Distributed PaulingWorld, a Distributed Virtual Environment application that supports collaborative visualization of molecular structures among multiple users within the same virtual environment. All the participants in the virtual environment have the same level of interaction in the application. In the application, a virtual menu that is attached to the left hand of the user is used to manipulate the molecule and the environment. The user that has the virtual menu has total control of the environment and the viewpoint of the users in the virtual environment. However, the virtual menu can also be transfer to another user in the virtual environment. Only the user that has the menu can chose to transfer the menu. At that point, the other user, upon receiving the virtual menu, will have the capability to manipulate the molecule and the virtual environment. Users are represented by avatars to indicate their location within the virtual environment.
SMEs Key Failure-Factors: A Comparison between the United Kingdom and Nigeria

Abstract To comparatively examine the individual impact of ten “key factors” influencing business failure within the small and medium-sized enterprises (SMEs) sector between the United Kingdom (UK) and Nigeria. A survey instrument testing ten key variables was developed and data was generated by sampling key informants. Forty five questionnaires each were administered and two interviews each were conducted between the UK and Nigeria. The results showed that while poor management was found to be the most crucial factor influencing to SMEs failure in the UK, poor economic conditions and infrastructural inadequacy were found to be most crucial factors in Nigeria. Relevant statistical analyses undertaken led to the rejection of the two hypotheses of the study. This study adopted a small survey sample of key informants due to the short time frame for conducting the study; hence limiting the ability of the results to be generalised. The results of this study reveal that internal factors are responsible for SMEs failures in the UK. Hence more effort needs to be focused in the area of management development for SMEs. External factors were found to be the key factor influencing SMEs failure in Nigeria; hence a lot needs to be done in Nigeria in terms of improving the economic conditions and dilapidated state of infrastructural and social support. This study attempts to fill the gap in comparative studies on factors influencing SMEs failure between the UK and Nigeria.
A Semi‐Lagrangian CIP Fluid Solver without Dimensional Splitting

In this paper, we propose a new constrained interpolation profile (CIP) method that is stable and accurate but requires less amount of computation compared to existing CIP‐based solvers. CIP is a high‐order fluid advection solver that can reproduce rich details of fluids. It has third‐order accuracy but its computation is performed over a compact stencil. These advantageous features of CIP are, however, diluted by the following two shortcomings: (1) CIP contains a defect in the utilization of the grid data, which makes the method suitable only for simulations with a tight CFL restriction; and (2) CIP does not guarantee unconditional stability. There have been several attempts to fix these problems in CIP, but they have been only partially successful. The solutions that fixed both problems ended up introducing other undesirable features, namely increased computation time and/or reduced accuracy. This paper proposes a novel modification of the original CIP method that fixes all of the above problems without increasing the computational load or reducing the accuracy. Both quantitative and visual experiments were performed to test the performance of the new CIP in comparison to existing fluid solvers. The results show that the proposed method brings significant improvements in both accuracy and speed.
The McPAT Framework for Multicore and Manycore Architectures: Simultaneously Modeling Power, Area, and Timing

This article introduces McPAT, an integrated power, area, and timing modeling framework that supports comprehensive design space exploration for multicore and manycore processor configurations ranging from 90nm to 22nm and beyond. At microarchitectural level, McPAT includes models for the fundamental components of a complete chip multiprocessor, including in-order and out-of-order processor cores, networks-on-chip, shared caches, and integrated system components such as memory controllers and Ethernet controllers. At circuit level, McPAT supports detailed modeling of critical-path timing, area, and power. At technology level, McPAT models timing, area, and power for the device types forecast in the ITRS roadmap. McPAT has a flexible XML interface to facilitate its use with many performance simulators. Combined with a performance simulator, McPAT enables architects to accurately quantify the cost of new ideas and assess trade-offs of different architectures using new metrics such as Energy-Delay-Area2 Product (EDA2P) and Energy-Delay-Area Product (EDAP). This article explores the interconnect options of future manycore processors by varying the degree of clustering over generations of process technologies. Clustering will bring interesting trade-offs between area and performance because the interconnects needed to group cores into clusters incur area overhead, but many applications can make good use of them due to synergies from cache sharing. Combining power, area, and timing results of McPAT with performance simulation of PARSEC benchmarks for manycore designs at the 22nm technology shows that 8-core clustering gives the best energy-delay product, whereas when die area is taken into account, 4-core clustering gives the best EDA2P and EDAP.
Threats to Validity in the Design and Conduct of Preclinical Efficacy Studies: A Systematic Review of Guidelines for In Vivo Animal Experiments

Background The vast majority of medical interventions introduced into clinical development prove unsafe or ineffective. One prominent explanation for the dismal success rate is flawed preclinical research. We conducted a systematic review of preclinical research guidelines and organized recommendations according to the type of validity threat (internal, construct, or external) or programmatic research activity they primarily address. Methods and Findings We searched MEDLINE, Google Scholar, Google, and the EQUATOR Network website for all preclinical guideline documents published up to April 9, 2013 that addressed the design and conduct of in vivo animal experiments aimed at supporting clinical translation. To be eligible, documents had to provide guidance on the design or execution of preclinical animal experiments and represent the aggregated consensus of four or more investigators. Data from included guidelines were independently extracted by two individuals for discrete recommendations on the design and implementation of preclinical efficacy studies. These recommendations were then organized according to the type of validity threat they addressed. A total of 2,029 citations were identified through our search strategy. From these, we identified 26 guidelines that met our eligibility criteria—most of which were directed at neurological or cerebrovascular drug development. Together, these guidelines offered 55 different recommendations. Some of the most common recommendations included performance of a power calculation to determine sample size, randomized treatment allocation, and characterization of disease phenotype in the animal model prior to experimentation. Conclusions By identifying the most recurrent recommendations among preclinical guidelines, we provide a starting point for developing preclinical guidelines in other disease domains. We also provide a basis for the study and evaluation of preclinical research practice. Please see later in the article for the Editors' Summary
An autonomous online malicious spam email detection system using extended RBF network

In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1st, 2013 to August 29th, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on average over the testing period.
CTLA-4: a negative regulator of autoimmune disease

CTLA-4, a CD28 homologue expressed on activated T cells, binds with high affinity to the CD28 ligands, B7-1 (CD80) and B7-2 (CD86). This study was designed to examine the role of CTLA-4 in regulating autoimmune disease. Murine relapsing-remitting experimental autoimmune encephalomyelitis (R-EAE) is a demyelinating disease mediated by PLP139- 151-specific CD4+ T cells in SJL/J mice. Anti-CTLA-4 mAbs (or their F(ab) fragments) enhanced in vitro proliferation and pro-inflammatory cytokine production by PLP139-151-primed lymph node cells. Addition of either reagent to in vitro activation cultures potentiated the ability of T cells to adoptively transfer disease to naive recipients. In vivo administration of anti-CTLA-4 mAb to recipients of PLP139-151-specific T cells resulted in accelerated and exacerbated disease. Finally, anti- CTLA-4 treatment of mice during disease remission resulted in the exacerbation of relapses. Collectively, these results suggest that CTLA- 4 mediates the downregulation of ongoing immune responses and plays a major role in regulating autoimmunity.
Barriers and facilitators of consumer use of nutrition labels at sit-down restaurant chains

Abstract Objective Numerous localities have mandated that chain restaurants post nutrition information at the point of purchase. However, some studies suggest that consumers are not highly responsive to menu labelling. The present qualitative study explored influences on full-service restaurant customers’ noticing and using menu labelling. Design Five focus groups were conducted with thirty-six consumers. A semi-structured script elicited barriers and facilitators to using nutrition information by showing excerpts of real menus from full-service chain restaurants. Setting Participants were recruited from a full-service restaurant chain in Philadelphia, Pennsylvania, USA, in September 2011. Subjects Focus group participants were mostly female, African American, with incomes <$US 60 000, mean age 36 years and education 14·5 years. At recruitment, 33 % (n 12) reported changing their order after seeing nutrition information on the menu. Results Three themes characterized influences on label use in restaurants: nutrition knowledge, menu design and display, and normative attitudes and behaviours. Barriers to using labels were low prior knowledge of nutrition; displaying nutrition information using codes; low expectations of the nutritional quality of restaurant food; and restaurant discounts, promotions and social influences that overwhelmed interest in nutrition and reinforced disinterest in nutrition. Facilitators were higher prior knowledge of recommended daily intake; spending time reading the menu; having strong prior interest in nutrition/healthy eating; and being with people who reinforced dietary priorities. Conclusions Menu labelling use may increase if consumers learn a few key recommended dietary reference values, understand basic energy intake/expenditure scenarios and if chain restaurants present nutrition information in a user-friendly way and promote healthier items.
Bootstrap Analysis of Genetic Networks inferred by the Method Using LPMs

Recently, we proposed a genetic network inference method using linear programming machines (LPMs). As this method infers genetic networks by solving linear programming problems, its computational time is very short. However, generic networks inferred by the method using the LPMs often contain a large number of false-positive regulations. When we try to apply the inference method to actual problems, we must experimentally validate the inferred regulations. Therefore, it is important to reduce the number of false-positive regulations. To decrease the number of regulations we must validate, this study assigns confidence values to all of the possible regulations. For this purpose, we combine a bootstrap method and the method using the LPMs. Through numerical experiments on artificial genetic network inference problems, we check the effectiveness of assessing the confidence values of the regulations.
Imaging scatterometry and microspectrophotometry of lycaenid butterfly wing scales with perforated multilayers

We studied the structural as well as spatial and spectral reflectance characteristics of the wing scales of lycaenid butterfly species, where the scale bodies consist of perforated multilayers. The extent of the spatial scattering profiles was measured with a newly built scatterometer. The width of the reflectance spectra, measured with a microspectrophotometer, decreased with the degree of perforation, in agreement with the calculations based on multilayer theory.