Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential of Using Acoustical Emission to Detect Termites Within Wood

Acoustical emission (AE) equipment was used to detect drywood termites Incisitermes minor in ponderosa pine Pinus ponderosa blocks under laboratory conditions. Using a 60 kHz transducer, AE levels were recorded for 0, 5, 10, 15, and 20 termites per block. The association of AE and varying numbers of drywood termites best fit an exponential curve. These data suggest that the detection capabilities of AE increase with increasing numbers of termites. The implications of this finding to the potential use of AE in detecting termites under field conditions are discussed.
B cells lacking RelB are defective in proliferative responses, but undergo normal B cell maturation to Ig secretion and Ig class switching

A number of distinct functional abnormalities have been observed in B cells derived from p50/ NF-kappa B or c-rel knockout mice. RelB, another member of the NF-kappa B/Rel family of transcription factors, is expressed during the latter stages of B cell maturation and can bind to regulatory sites within the Ig heavy chain locus. Therefore, we tested the ability of B cells from relB knockout mice (relB-/-) to proliferate, undergo maturation to IgM secretion, and switch to the expression of downstream Ig isotypes in response to distinct activators including LPS, anti-CD40 mAb or CD40 ligand, and/or dextran anti-IgD antibodies in combination with various cytokines, including IL-4, IL-5, IFN-gamma, and TGF-beta. B cells lacking RelB showed up to 4-fold reductions in DNA synthesis in response to LPS, CD40, and membrane Ig- dependent activation relative to controls. However, relB-/- B cells were comparable to control B cells in their ability to undergo maturation to IgM secretion and switch to the expression of IgG3, IgG1, IgG2b, IgG2a, IgE, and/or IgA under all activation conditions tested. Thus, RelB, like c-Rel and p50/NF-kappa B, plays a role in B cell proliferation. However, in contrast to c-Rel and p50/ NF-kappa B, it is not critically involved in maturation to Ig secretion or expression of Ig isotypes.
Unmasking the crowd: participants’ motivation factors, expectations, and profile in a crowdsourced law reform

ABSTRACT This article examines the demographic characteristics, motivations, and expectations of participants in a crowdsourced off-road traffic law reform in Finland. We found that the participants were mainly educated, full-time working professional males with a strong interest in off-road traffic. Though a minority, the women participating in the process produced more ideas than the men. The crowd was motivated by a mix of intrinsic and extrinsic factors. Intrinsic motivations included fulfilling civic duty, affecting the law for sociotropic reasons, to deliberate with and learn from peers. Extrinsic motivations included changing the law for financial gain or other benefits. Participation in crowdsourced policy-making was an act of grassroots advocacy, whether to pursue one’s own interest or more altruistic goals, such as protecting nature. The motivations driving the participation were in part similar to those observed in traditional democratic processes, such as elections as well as other online collaborations such as crowdsourced journalism and citizen science. The crowds’ behavior was, however, paradoxical. They participated despite the fact that they did not expect that their contributions would affect the law.
DUKE: A Solution for Discovering Neighborhood Patterns in Ego Networks

Given the rapid growth of social media websites and the ease of aggregating ever-richer social data, an inevitable research question that can be expected to emerge is whether different interaction patterns of individuals and their meaningful interpretation can be captured for social network analysis. In this work, we present a novel solution that discovers occurrences of prototypical 'ego network' patterns from social media and mobile-phone networks, to provide a data-driven instrument to be used in behavioral sciences for graph interpretations. We analyze nine datasets gathered from social media websites and mobile phones, together with 13 network measures, and three unsupervised clustering algorithms. Further, we use an unsupervised feature similarity technique to reduce redundancy and extract compact features from the data. The reduced feature subsets are then used to discover ego patterns using various clustering techniques. By cluster analysis, we discover that eight distinct ego neighborhood patterns or ego graphs have emerged. This categorization allows concise analysis of users' data as they change over time. We provide fine-grained analysis for the validity and quality of clustering results. We perform clustering verification based on the following three intuitions: i) analyzing the clustering patterns for the same set of users crawled from three social media networks, ii) associating metadata information with the clusters and evaluating their performance on real networks, iii) studying selected participants over an extended period to analyze their behavior.
Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning

Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus–response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal.
Spectra-limited Diffusion in the Characterization of Textural Patches in Images

Ross T. Whitaker Department of Computer Science, University of North Carolina Chapel Hill, North Carolina, 27514 Image segmentation on the basis of texture is an important aspect of natural vision as well as a widely studied problem in artificial vision. This paper presents a method for discriminating patches in digital images on the basis of homogeniety of local properties within the image. The principles of nonuniform scale, as implemented by variable conductance diffusion, are combined with frequency decomposition in order to produce a patchwise Fourier analysis does that does not suffer from the strictly global nature of the conventional Fourier transform. The result is a system of reaction diffusion equations that are a natural generalization to conventional scale space. This algorithm that results from this technique shows that parallel processing elements associated with spatial regions in patches can generate descriptions of patch textures and boundaries via local interactions. Experiments conducted on a highly parallel SIMD architecture indicate that these ideas are readily implemented in parallel algorithms and are effective for solving some simple texture discrimination tasks.
Economic evaluation of the differential benefits of home visits with telephone calls and telephone calls only in transitional discharge support

Background: home visits and telephone calls are two often used approaches in transitional care, but their differential economic effects are unknown. Objective: to examine the differential economic benefits of home visits with telephone calls and telephone calls only in transitional discharge support. Design: cost-effectiveness analysis conducted alongside a randomised controlled trial (RCT). Participants: patients discharged from medical units randomly assigned to control (control, N = 210), home visits with calls (home, N = 196) and calls only (call, N = 204). Methods: cost-effectiveness analyses were conducted from the societal perspective comparing monetary benefits and quality-adjusted life years (QALYs) gained. Results: the home arm was less costly but less effective at 28 days and was dominating (less costly and more effective) at 84 days. The call arm was dominating at both 28 and 84 days. The incremental QALY for the home arm was −0.0002/0.0008 (28/84 days), and the call arm was 0.0022/0.0104 (28/84 days). When the three groups were compared, the call arm had a higher probability being cost-effective at 84 days but not at 28 days (home: 53%, call: 35% (28 days) versus home: 22%, call: 73% (84 days)) measuring against the NICE threshold of £20,000. Conclusion: the original RCT showed that the bundled intervention involving home visits and calls was more effective than calls only in the reduction of hospital readmissions. This study adds a cost perspective to inform policymakers that both home visits and calls only are cost-effective for transitional care support, but calls only have a higher chance of being cost-effective for a sustained period after intervention.
A Constraint Logic Programming Approach to the CrewRostering

In this paper, we present a constraint based approach to the Crew Rostering Problem. A solution to this problem is the optimal sequencing of a given set of duties into rosters in order to satisfy a set of constraints. The optimality criterion concerns the minimization of the number of crews needed to cover the duties. This kind of problem has been traditionally solved with Operations Research (OR) techniques. We have used a mixed approach based mainly on the Constraint Logic Programming paradigm (CLP), but exploiting an eeective lower bound procedure taken from the OR. In this way, we can solve very large instances of the problem by maintaining the advantages of CLP such as declarativeness and rapid prototyping, while overcoming the limitations of CLP in dealing with large sized instances of hard problems.
Process mining in oncology: A literature review

Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.
Compensation and fuzzy control of tension in strip winding control system

The precise control of winding tension is a key technique for manufacturing strip wound prestressed dies. The principles of producing and controlling of the winding tension are discussed in this paper. Considering a set point of variable tension in a strip winding control system, the main reasons of tension fluctuating are analyzed. An integrated control strategy of compensation and fuzzy control is proposed, which includes forward feedback compensation control of velocity, fuzzy control of tension in part of unwinding and fuzzy control of velocity in part of winding. The simulation model of strip winding control system with a double motors structure is set up using Matlab. The simulation results show that the performance of winding control system is improved by adopting this kind of integrated control strategy. The stability and accuracy of velocity of motor is improved, and the degree of tension fluctuating is reduced obviously. This kind of control strategy can be applied in actual system in order to improve the distribution of prestress within the strip wound dies, and make products achieve a longer service life.
Strain effects on hole current in silicon nanowire FETs

Hole transport simulation based on the nonequilibrium Green's function and tight-binding formalism has been performed for strained Si nanowire FETs with a diameter of 1.5nm and 2.5 nm. Simulation results show that for Si nanowire FETs with a diameter of 2.5 nm, the compressive strain enhances the ballistic hole current, while the tensile strain gives opposite results. For Si nanowire FETs with a diameter of 1.5 nm, the ballistic hole current hardly depends on the strain magnitude.
Evaluation for computational platforms of LC-MS based label-free quantitative proteomics: A global view

Label-free shotgun proteomics is a promising semi-quantitative protein profi ling method with capability of comparing a large number of samples in a single experiment. One of the key challenges in this proteomics approach is the high requirement of computational capability for tasks such as feature detection and LC-MS alignment due to the complexity of proteomics systems. Many software tools have been developed in recent years to aid these processes, yet it is often not clear to users whether these tools extract information from raw data correctly and comprehensively. In this paper, we described a comprehensive procedure to provide a fast and global view for performances of LC-MS label-free computational software. Two high quality mass spectrometry datasets with carefully controlled QC samples and spikedin proteins were also provided as benchmark datasets for such evaluations.
Using a decompiler for real-world source recovery

Despite their 40 year history, native executable decompilers have found very limited practical application in commercial projects. The success of Java decompilers is well known, and a few decompilers perform well by recognising patterns from specific compilers. This work describes the experience gained from applying a native executable decompiler, assisted by a commercial disassembler and hand editing, to a real-world Windows-based application. The clients had source code for a prototype version of the program, and an executable that performed better, for which the source code was not available. The project was to recover the algorithm at the core of the program, and if time permitted, the recovery of other pieces of source code. Despite the difficulties, the core algorithm was successfully decompiled, and a portion of the rest of the program as well. There were surprises, including the ability to recover almost all original class names, and the complete class hierarchy.
Learning Structure and Schemas from Heterogeneous Domains in Networked Systems: A Survey

The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.
Cyclic loading of tendon fascicles using a novel fatigue loading system increases interleukin-6 expression by tenocytes

Repetitive strain or ‘overuse’ is thought to be a major factor contributing to the development of tendinopathy. The aims of our study were to develop a novel cyclic loading system, and use it to investigate the effect of defined loading conditions on the mechanical properties and gene expression of isolated tendon fascicles. Tendon fascicles were dissected from bovine‐foot extensors and subjected to cyclic tensile strain (1 Hz) at 30% or 60% of the strain at failure, for 0 h (control), 15 min, 30 min, 1 h, or 5 h. Post loading, a quasi‐static test to failure assessed damage. Gene expression at a selected loading regime (1 h at 30% failure strain) was analyzed 6 h post loading by quantitative real‐time polymerase chain reaction. Compared with unloaded controls, loading at 30% failure strain took 5 h to lead to a significant decrease in failure stress, whereas loading to 60% led to a significant reduction after 15 min. Loading for 1 h at 30% failure strain did not create significant structural damage, but increased Collagen‐1‐alpha‐chain‐1 and interleukin‐6 (IL6) expression, suggesting a role of IL6 in tendon adaptation to exercise. Correlating failure properties with fatigue damage provides a method by which changes in gene expression can be associated with different degrees of fatigue damage.
A bricolage perspective on healthcare information systems design: an improvisation model

Pressured by escalating costs, continual demand for high quality, and the speed of technological advances, the need for change and improvisation has become a critical priority for the healthcare industry. Now society demands that healthcare providers offer better patient care through the careful use of information technologies. For that, practitioners are urged to expand the boundaries of innovative IS design strategies. Unfortunately, research on healthcare information systems (HIS) improvisation remains relatively underdeveloped. Thus, this study uses the organizational improvisation and bricolage theoretical lenses, from the perspective of a case study, to examine how strategic improvisation might give rise to fruitful HIS novel design performances. Theoretically, we provide an inductively derived strategic conceptual model of improvisation that couples with network, structure, and institutional bricolage to execute a 'resource-time-effort' model. This enables us to improvise a superior HIS that offers quality patient-centric healthcare delivery and a valuable improvisation model. Professionally, this study contributes three key insights for IS improvisation in the healthcare industry.
Testability, fault size and the domain-to-range ratio: An eternal triangle

A number of different concepts have been proposed that, loosely speaking, revolve around the notion of software testability. Indeed, the concept of testability itself has been interpreted in a variety of ways by the software community. One interpretation is concerned with the extent of the modifications a program component requires, in terms of its input and output variables, so that the entire behaviour of the component is observable and controllable. Another interpretation is the ease with which faults, if present in a program, can be revealed by the testing process and the propagation, infection and execution (PIE) model has been proposed as a method of estimating this. It has been suggested that this particular interpretation of testability might be linked with the metric domain-to-range ratio (DRR), i.e. the ratio of the cardinality of the set of all inputs (the domain) to the cardinality of the set of all outputs (the range). This paper reports work in progress exploring some of the connections between the concepts mentioned. In particular, a simple mathematical link is established between domain-to-range ratio and the observability and controllability aspects of testability. In addition, the PIE model is re-considered and a relationship with fault size is observed. This leads to the suggestion that it might be more straightforward to estimate PIE testability by an adaptation of traditional mutation analysis. The latter suggestion exemplifies the main goals of the work described here, namely to seek greater understanding of testability in general and, ultimately, to find easier ways of determining it.
Two Recommendation Algorithms Based on Deformed Linear Combinations

Data mining for recommender systems has gained a lot of interest in the recent years. "ECML/PKDD Discovery Challenge 2011" was organized to improve current recommender system of the VideoLectures.Net website. Two main tasks of the challenge simulate new-user and new-item recommendation (cold-start mode) and clickstream based recommendation (normal mode). This paper provides detailed descriptions of two simple algorithms which were very successful in the both tasks. The main idea of the algorithms is construction of a linear combination equal to a vector of estimations of lectures popularity after viewing a certain lecture (or lectures). Each addend in the combination describes similarity of lectures using the part of the data. The algorithms are improved by transforming the combination to non-linear function. Lectures with the highest estimations of popularity are recommended to users.
2.1.1 Isoperformance: Analysis and Design of Complex Systems with Known or Desired Outcomes

Tradeoffs between performance, cost and risk frequently arise during analysis and design of complex systems. Many such systems have both human and technological components and can be described by mathematical input‐output models. Oftentimes such systems have known or desired outcomes or behaviors. This paper proposes “isoperformance” as an alternative approach for analyzing and designing systems by working backwards from a set of desired performance targets to a set of acceptable solutions. This is in contrast to the traditional “forward” process, which starts first in the design space and attempts to predict performance in objective space. Isoperformance can quantify and visualize the tradeoffs between determinants (independent design variables) of a known or desired outcome. For deterministic systems, performance invariant contours can be computed using sensitivity analysis and contour following. In the case of stochastic systems, the isoperformance curves can be obtained by regression analysis, given a statistically representative data set. Examples from opto‐mechanical systems design and human factors are presented to illustrate specific applications of the method.