Miracle-dz/UniFAR-FTUs huggingface dataset:
Filtering Medical Documents Using Automated and Human Classification Methods

The goal of this research is to clarify the role of document classification in information filtering. An important function of classification, in managing computational complexity, is described and illustrated in the context of an existing filtering system. A parameter called classification homogeneity is presented for analyzing unsupervised automated classification by employing human classification as a control. Two significant components of the automated classification approach, vocabulary discovery and classification scheme generation, are described in detail. Results of classification performance revealed considerable variability in the homogeneity of automatically produced classes. Based on the classification performance, different types of interest profiles were created. Subsequently, these profiles were used to perform filtering sessions. The filtering results showed that with increasing homogeneity, filtering performance improves, and, conversely, with decreasing homogeneity, filtering performance degrades.
Efficient Multicast Delivery for Data Redundancy Minimization Over Wireless Data Centers

With the explosive growth of cloud-based services, large-scale data centers are widely built for housing critical computing resources to gain significant economic benefits. In data centers, the cloud services are generally accomplished by multicast-based group communications. Recently, many well-known industries, such as Microsoft, Google, and IBM, adopt high-speed wireless technologies to augment network capacity in data centers. However, those well-known multicast delivery schemes for traditional wired data centers do not consider the unique characteristics of wireless communications, which may result in unnecessary data transmissions and network congestions. Under the coexisting scenario of wired and wireless links, this paper studies multicast tree construction and maintenance problems. The objective is to minimize the total multicast traffic. We prove the problems are NP-hard and propose efficient heuristic algorithms for the two problems. Based on real traces and practical settings obtained from commercial data centers, a series of experiments are conducted, and the experimental results show that our proposed algorithms are effective for reducing multicast data traffic. The results also provide useful insights into the design of multicast tree construction and maintenance for wireless data center networks.
Strip-Loaded Slotline Resonators for Differential Wideband Bandpass Filters With Intrinsic Common-Mode Rejection

In this paper, novel strip-loaded slotline resonators are proposed for design and exploration of differential wideband bandpass filters (BPFs) with intrinsic common-mode (CM) rejection. When the loaded stubs on the slotline resonator are in the format of microstrip lines, there are two types of lines in the resonator and the resonator can be fed or tightly coupled from the loaded strip stubs. In this way, the troublesome orthogonal transitions for feeding the slotline resonator can be avoided. With the use of this hybrid microstrip/slotline structure, the proposed stub-loaded slotline resonators have their CM rejection themselves as an inherent property. Only the differential-mode (DM) transmission performances need to be focused on. In this context, the constructed resonators operate under triple- and quadruple-mode operation and they are tightly coupled with feeding lines via parallel coupled lines. After the multimode resonance properties of these proposed hybrid resonators are investigated, two differential BPFs with five and six in-band transmission poles are designed, fabricated, and measured. All the simulated frequency responses of these two designed filters have demonstrated their excellent DM wide passband performance with high CM rejection, as validated in experiment.
Using Ontology to create 3D Animations for Training Purposes

D Computer-based animations are nowadays used for training purposes in a wide range of industrial applications like assembly, maintenance and operations. Their creation, also known as authoring, is usually a time consuming task, demanded to professional 3D designers, who need at first a good understanding of the involved entities and actions in order to realize the customized animations. The proposed methodology deals with the use of an ontology in order to filter and understand generic natural language training requests; once identified the proper actors and actions, they are associated to the corresponding models and movements to be performed in the virtual environment and translated in a 3D graphic format template. The result is a customized animation which can be created by a non-expert designer and then visualized by the worker through many modalities, on a desktop computer or on a PDA for example. Role of the ontology is to reduce the overall complexity of the animation authoring process by assuring the necessary comprehension of customized training requests as well as reusability and extensibility of the structure of the modeled object and of animations' components in different domains.
Groove model of tibia‐femoral osteoarthritis in the rat

Several experimental models of osteoarthritis in rats are used to study the pathophysiology of osteoarthritis. Many mechanically induced models have the limitation that permanent joint instability is induced by, for example, ligament transection or meniscal damage. This permanent instability will counteract the potential beneficial effects of therapy. The groove model of osteoarthritis uses a one‐time trigger, surgically induced cartilage damage on the femoral condyles, and has been validated for the canine tibia‐femoral compartment. The present study evaluates this model for the rat knee joint. The articular cartilage of the weight bearing surface of both femoral condyles and trochlea were damaged (grooved) without damaging the underlying subchondral bone. Severity of joint degeneration was histologically assessed, in addition to patella cartilage damage, and subchondral bone characteristics by means of (contrast‐enhanced) micro‐CT. Mild histological degeneration of the surgically untouched tibial plateau cartilage was observed in addition to damage of the femoral condyles, without clear synovial tissue inflammation. Contrast enhanced micro‐CT demonstrated proteoglycan loss of the surgically untouched patella cartilage. Besides, a more sclerotic structure of the subchondral bone was observed. The tibia‐femoral groove model in a rat results in mild knee joint degeneration, without permanent joint instability and joint inflammation. This makes the rat groove model a useful model to study the onset and progression of post‐traumatic non‐inflammatory osteoarthritis, creating a relatively sensitive model to study disease modifying osteoarthritic drugs. © 2016 The Authors. Journal of Orthopaedic Research published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. J Orthop Res 35:496–505, 2017.
Scavenging complex genomic information using mobile code: an evaluation

Mobile code solutions can improve the performances of applications over the Internet by reducing the amount of data that must be transferred across the network. The research on genome examines enormous amount of data stored on many distributed databases to retrieve the information relevant for the specific investigation, and thus is likely to greatly benefit for mobile code solutions. This paper studies the benefits of mobile code solutions for research on genome, by comparing different solutions with analytical models, and presenting the results of case studies that allow to identify when mobile code overwhelm client/server solutions. The measured improvements of performance indicate that the retrieval of information from genome databases is a promising domain for mobile code applications.
A corpus of science journalism for analyzing writing quality

We introduce a corpus of science journalism articles, categorized in three levels of writing quality. 1 The corpus fulﬁlls a glaring need for realistic data on which applications concerned with predicting text quality can be developed and evaluated. In this article we describe how we identiﬁed, guided by the judgements of renowned journalists, samples of excellent, very good and typical writing. The ﬁrst category comprises extraordinarily well-written pieces as identiﬁed by expert journalists. We expanded this set with other articles written by the authors of these excellent samples to form a set of very good writing samples. In addition, our corpus also comprises a larger set of typical journalistic writing. We provide details about the corpus and the text quality evaluations it can support. Our intention is to further extend the corpus with annotations of phenomena that reveal quan-tiﬁable differences between levels of writing quality. Here we introduce two such annotations that have promise for distinguishing amazing from typical writing: text generality/speciﬁcity and communicative goals. We present manual annotation experiments for speciﬁcity of text and also explore the feasibility of acquiring these annotations automatically. For communicative goals, we present an automatic clustering method to explore the possible set of communicative goals and develop guidelines for future manual annotations. We ﬁnd that the annotation of general/speciﬁc nature on sentence level can be performed reasonably accurately fully automatically, while automatic annotations of communicative goals reveals salient characteristics of journalistic writing but does not align with categories we wish to annotate in future work. Still with the current automatic annotations, we provide evidence that features based on speciﬁcity and communicative goals are indeed predictive of writing
Distortion-Delay Tradeoff for a Gaussian Source Transmitted over a Fading Channel

We study the end-to-end distortion-delay tradeoff for a Gaussian source transmitted over a fading channel. The analog source is quantized and stored in a buffer until it is transmitted. There are two extreme cases as far as buffer delay is concerned: no delay and infinite delay. We observe that there is a significant power gain by introducing a buffer delay. Our goal is to investigate the situation between these two extremes. Using the recently proposed effective capacity concept, we derive a closed-form expression for this tradeoff. In order to characterize the convergence behavior, we derive an asymptotically tight upper bound for our tradeoff curve, which approaches the infinite delay lower bound polynomially. Numerical results demonstrate that introduction of a small amount of delay can save significant transmission power
Opinion Maximization in Social Networks

The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, this process manifests itself also in online social networks and social media. The key characteristic of successful promotion campaigns is that they take into consideration such opinion-formation dynamics in order to create a overall favorable opinion about a specific information item, such as a person, a product, or an idea. In this paper, we adopt a well-established model for social-opinion dynamics and formalize the campaign-design problem as the problem of identifying a set of target individuals whose positive opinion about an information item will maximize the overall positive opinion for the item in the social network. We call this problem CAMPAIGN. We study the complexity of the CAMPAIGN problem, and design algorithms for solving it. Our experiments on real data demonstrate the efficiency and practical utility of our algorithms.
Stable and Robust Sampling Strategies for Compressive Imaging

In many signal processing applications, one wishes to acquire images that are sparse in transform domains such as spatial finite differences or wavelets using frequency domain samples. For such applications, overwhelming empirical evidence suggests that superior image reconstruction can be obtained through variable density sampling strategies that concentrate on lower frequencies. The wavelet and Fourier transform domains are not incoherent because low-order wavelets and low-order frequencies are correlated, so compressive sensing theory does not immediately imply sampling strategies and reconstruction guarantees. In this paper, we turn to a more refined notion of coherence-the so-called local coherence-measuring for each sensing vector separately how correlated it is to the sparsity basis. For Fourier measurements and Haar wavelet sparsity, the local coherence can be controlled and bounded explicitly, so for matrices comprised of frequencies sampled from a suitable inverse square power-law density, we can prove the restricted isometry property with near-optimal embedding dimensions. Consequently, the variable-density sampling strategy we provide allows for image reconstructions that are stable to sparsity defects and robust to measurement noise. Our results cover both reconstruction by ℓ1-minimization and total variation minimization. The local coherence framework developed in this paper should be of independent interest, as it implies that for optimal sparse recovery results, it suffices to have bounded average coherence from sensing basis to sparsity basis-as opposed to bounded maximal coherence-as long as the sampling strategy is adapted accordingly.
A New Technique to Multiplex Stereo Images: LSB Watermarking and Hamming Code

The vision is seeing an image using two different perspectives using human eyes. Any image has a size in the memory of the system, this image could be compressed or decompressed through several methods. In this paper, the disparity using block matching is used as a method of stereo image compression. It gives a high compression rate. On the other hand, more compression can be done by watermarking. Since block matching produced very small information, a simple method such as the Least Significant Bit is used along with two levels of Hamming code in order to increase the robustness. The compression and the watermarking results are demonstrated, and the system is analyzed.
An Empirical Investigation of the Strategic Use of Debt

In this paper I test two models of strategic debt behavior. The general idea is that if a government anticipates the possibility of defeat in the next election it will try to use the debt strategically in order to influence the policy of its successor. Previous empirical studies have either rejected the strategic explanation of debt or have not been able to isolate this effect. I argue that these findings are perhaps less surprising since there are several potential difficulties using U.S. or OECD data to test the strategic explanation. One problem with the U.S. data is the scarcity of degrees of freedom while a problem with the OECD data is the pooling assumption. To come to grips with these two problems I use a data set from Swedish local governments. The main advantages of this panel data set are the homogeneity of the sample and the large number of observations from elections, nearly 2000. After controlling for other possible economic and demographic determinants of debt behavior, the main findings of this paper strongly suggest that a right-wing government accumulates more debt during its term of office if it thinks that it will be defeated as compared to when it expects to remain in office. On the other hand, a left-wing government decreases the level of debt the higher the possibility of its defeat. Moreover, the larger the inherited debt the more a newly elected government has to reduce spending and raise taxes. These results are consistent with the predictions from a model developed by Persson and Svensson (1989).
Multi-objective χ-Armed bandits

Many of the standard optimization algorithms focus on optimizing a single, scalar feedback signal. However, real-life optimization problems often require a simultaneous optimization of more than one objective. In this paper, we propose a multi-objective extension to the standard χ-armed bandit problem. As the feedback signal is now vector-valued, the goal of the agent is to sample actions in the Pareto dominating area of the objective space. Therefore, we propose the multi-objective Hierarchical Optimistic Optimization strategy that discretizes the continuous action space in relation to the Pareto optimal solutions obtained in the multi-objective objective space. We experimentally validate the approach on two well-known multi-objective test functions and a simulation of a real life application, the filling phase of a wet clutch. We demonstrate that the strategy allows to identify the Pareto front after just a few epochs and to sample accordingly. After learning, several multi-objective quality indicators indicate that the set of sampled solutions by the algorithm very closely approximates the Pareto front.
SHP-1 and SHP-2 Associate with Immunoreceptor Tyrosine-Based Switch Motif of Programmed Death 1 upon Primary Human T Cell Stimulation, but Only Receptor Ligation Prevents T Cell Activation1

To study the cis- and trans-acting factors that mediate programmed death 1 (PD-1) signaling in primary human CD4 T cells, we constructed a chimeric molecule consisting of the murine CD28 extracellular domain and human PD-1 cytoplasmic tail. When introduced into CD4 T cells, this construct mimics the activity of endogenous PD-1 in terms of its ability to suppress T cell expansion and cytokine production. The cytoplasmic tail of PD-1 contains two structural motifs, an ITIM and an immunoreceptor tyrosine-based switch motif (ITSM). Mutation of the ITIM had little effect on PD-1 signaling or functional activity. In contrast, mutation of the ITSM abrogated the ability of PD-1 to block cytokine synthesis and to limit T cell expansion. Further biochemical analyses revealed that the ability of PD-1 to block T cell activation correlated with recruitment of Src homology region 2 domain-containing phosphatase-1 (SHP-1) and SHP-2, and not the adaptor Src homology 2 domain-containing molecule 1A, to the ITSM domain. In TCR-stimulated T cells, SHP-2 associated with PD-1, even in the absence of PD-1 engagement. Despite this interaction, the ability of PD-1 to block T cell activation required receptor ligation, suggesting that colocalization of PD-1 with CD3 and/or CD28 may be necessary for inhibition of T cell activation.
Lesions to the Motor System Affect Action Perception

Several studies have shown that the motor system is involved in action perception, suggesting that action concepts are represented through sensory–motor processes. Such conclusions imply that motor system impairments should diminish action perception. To test this hypothesis, a group of 10 brain-damaged patients with hemiplegia (specifically, a lesion at the motor system that affected the contralesional arm) viewed point-light displays of arm gestures and attempted to name each gesture. To create the dynamic stimuli, patients individually performed simple gestures with their unaffected arm while being videotaped. The videotapes were converted into point-light animations. Each action was presented as it had been performed, that is, as having been produced by the observer's unaffected arm, and in its mirror reversed orientation, that is, as having been produced by the observer's hemiplegic arm. Action recognition accuracy by patients with hemiplegia was compared with that by 8 brain-damaged patients without any motor deficit and by 10 healthy controls. Overall, performance was better in control observers than in patients. Most importantly, performance by hemiplegic patients, but not by nonhemiplegic patients and controls, varied systematically as a function of the observed limb. Action recognition was best when hemiplegic patients viewed actions that appeared to have been performed by their unaffected arm. Action recognition performance dropped significantly when hemiplegic patients viewed actions that appeared to have been produced with their hemiplegic arm or the corresponding arm of another person. The results of a control study involving the recognition of point-light defined animals in motion indicate that a generic deficit to visual and cognitive functions cannot account for this laterality-specific deficit in action recognition. Taken together, these results suggest that motor cortex impairment decreases visual sensitivity to human action. Specifically, when a cortical lesion renders an observer incapable of performing an observed action, action perception is compromised, possibly by a failure to map the observed action onto the observer's contralesional hemisoma.
Understanding the Usability Construct: User-Perceived Usbility

The usability profession has seen success in industry and academia, as well as recognition in the popular press. However, inconsistent measurement, unreliable problem identification, inappropriate high-level goals, and a lack of valid metrics highlight recent usability literature. Rather than support or refute these findings, we offer a research introspection that we hope contributes to all of these issues by improving our basic understanding of the construct of usability. A Usability Concept Survey (UCS) containing 64 potential usability characteristics was created and administered to 46 users who rated how integral each characteristic was to usability. Multivariate analyses of these user perceptions were used to construct: 1) a taxonomy of usability to classify usability characteristics, 2) data-driven general dimensions of usability, 3) a map of usability space showing specific usability characteristics within the general dimensions; and, 4) a definition of usability. We believe this better understanding of the construct of usability can contribute directly to improving usability objectives, measures, and practice.
MortalitySIRS/CARS Continuum in Sepsis and Predict Reshape the Understanding of the Circulating Cytokine/Inhibitor Profiles

Mortality in sepsis remains unacceptably high and attempts to modulate the inﬂammatory response failed to improve survival. Previous reports postulated that the sepsis-triggered immunological cascade is multimodal: initial systemic inﬂammatory response syndrome (SIRS; excessive pro-, but no/low anti-inﬂammatory plasma mediators), intermediate homeostasis with a mixed antiinﬂammatory response syndrome (MARS; both pro- and anti-inﬂammatory mediators) and ﬁnal compensatory anti-inﬂammatory response syndrome (CARS; excessive anti-, but no/low proinﬂammatory mediators). To verify this, we examined the evolution of the inﬂammatory response during the early phase of murine sepsis by repetitive blood sampling of septic animals. Increased plasma concentrations of proinﬂammatory (IL-6, TNF, IL-1 (cid:1) , KC, MIP-2, MCP-1, and eotaxin) and anti-inﬂammatory (TNF soluble receptors, IL-10, IL-1 receptor antagonist) cytokines were observed in early deaths (days 1–5). These elevations occurred simultaneously for both the pro- and anti-inﬂammatory mediators. Plasma levels of IL-6 (26 ng/ml), TNF- (cid:2) (12 ng/ml), KC (33 ng/ml), MIP-2 (14 ng/ml), IL-1 receptor antagonist (65 ng/ml), TNF soluble receptor I (3 ng/ml), and TNF soluble receptor II (14 ng/ml) accurately predicted mortality within 24 h. In contrast, these parameters were not elevated in either the late-deaths (day 6–28) or survivors. Surprisingly, either pro- or anti-inﬂammatory cytokines were also reliable in predicting mortality up to 48 h before outcome. These data demonstrate that the initial inﬂammatory