Miracle-dz/UniFAR-FTUs huggingface dataset:
Histologic Evaluation of Ulcerative Colitis: A Systematic Review of Disease Activity Indices

Background:Ulcerative colitis (UC) is an idiopathic inflammatory disorder. Currently, the main goals of treatment are to induce and maintain clinical and/or endoscopic remission. However, evidence indicates that persistent disease activity on colonic biopsies in the setting of clinical or endoscopic remission is an independent predictor of poor outcomes. A number of previous studies have proposed histologic indices for use in specific trials of UC. The aim of this study was to systematically review the existing histological indices for UC and assess their potential use in both patient management and clinical trials. Methods:We performed a systematic review of histological indices evaluating disease activity in UC. MEDLINE (Ovid), EMBASE (Ovid), PubMed, the Cochrane Library (CENTRAL), and Digestive Diseases Week (DDW) abstracts of randomized and/or controlled trials clinical trials were searched from inception to February 2013 for applicable studies. Data from these studies were reviewed and analyzed. Results:After systematically applying inclusion criteria, we identified 108 scientific articles including 88 clinical studies and 21 related clinical reviews. Eighteen indices of histological activity in UC were identified and reviewed. Conclusions:Although multiple histological scoring indices for assessment of UC disease activity currently exist, none of these instruments were developed using a formal validation process and their operating properties remain poorly understood. Future studies are needed to address this deficiency.
Visually summarizing the Evolution of Documents under a Social Tag

Tags are intensively used in social platforms to annotate resources: Tagging is a social phenomenon, because users do not only annotate to organize their resources but also to associate semantics to resources contributed by third parties. This leads often to semantic ambiguities: Popular tags are associated with very disparate meanings, even to the extend that some tags (e.g. ”beautiful” or ”toread”) are irrelevant to the semantics of the resources they annotate. We propose a method that learns a topic model for documents under a tag and visualizes the different meanings associated
The Vehicle Routing Problem with Discrete Split Delivery and Time Windows

The Discrete Split Delivery Vehicle Routing Problem with Time Windows (DSDVRPTW) consists of designing the optimal set of routes to serve, at least cost, a given set of customers while respecting constraints on vehicles capacity and customer time windows. The delivery request of a customer consists of several discrete items which cannot be split further. The problem belongs to the class of split delivery problems since each customers demand can be split in orders, i.e. feasible combinations of items, and each customer can be visited by more than one vehicle. In this work, we model the DSDVRPTW as a mixed integer linear program, assuming that all feasible orders are known in advance and that each vehicle can serve at most one order per customer. Remarkably, service time at customers location depends on the serviced combination of items, which is a modeling feature rarely found in literature. We present a branch-and-price algorithm, analyzing the implications of the classical Dantzig-Wolfe reformulation. Preliminary computational results on instances based on Solomons data set are discussed.
Equilibrium theory of cylindrical discharges with special application to helicons

Radiofrequency discharges used in industry often have centrally peaked plasma density profiles n(r) although ionization is localized at the edge, even in the presence of a dc magnetic field. This can be explained with a simple cylindrical model in one dimension as long as the short-circuit effect at the endplates causes a Maxwellian electron distribution. Surprisingly, a universal profile can be obtained, which is self-similar for all discharges with uniform electron temperature Te and neutral density nn. When all collisions and ionizations are radially accounted for, the ion drift velocity toward the wall reaches the Bohm velocity at a radius which can be identified with the sheath edge, thus obviating a pre-sheath calculation. For non-uniform Te and nn, the profiles change slightly but are always peaked on axis. For helicon discharges, iteration with the HELIC code for antenna-wave coupling yields profiles consistent with both energy deposition and diffusion profiles. Calculated density is in absolute-v.
Cleaning the Periodicity in Discrete- and Continuous-Time Nonlinear Dynamical Systems

We investigate periodicity suppression in two-dimensional parameter-spaces of discrete- and continuous-time nonlinear dynamical systems, modeled respectively by a two-dimensional map and a set of three first-order ordinary differential equations. We show for both cases that, by varying the amplitude of an external periodic forcing with a fixed angular frequency, windows of periodicity embedded in a chaotic region may be totally suppressed.
Mortality After Incident Cancer in People With and Without Type 2 Diabetes

OBJECTIVE Type 2 diabetes is associated with an increased risk of several types of cancer and with reduced survival after cancer diagnosis. We examined the hypotheses that survival after a diagnosis of solid-tumor cancer is reduced in those with diabetes when compared with those without diabetes, and that treatment with metformin influences survival after cancer diagnosis. RESEARCH DESIGN AND METHODS Data were obtained from >350 U.K. primary care practices in a retrospective cohort study. All individuals with or without diabetes who developed a first tumor after January 1990 were identified and records were followed to December 2009. Diabetes was further stratified by treatment regimen. Cox proportional hazards models were used to compare all-cause mortality from all cancers and from specific cancers. RESULTS Of 112,408 eligible individuals, 8,392 (7.5%) had type 2 diabetes. Cancer mortality was increased in those with diabetes, compared with those without (hazard ratio 1.09 [95% CI 1.06–1.13]). Mortality was increased in those with breast (1.32 [1.17–1.49]) and prostate cancer (1.19 [1.08–1.31]) but decreased in lung cancer (0.84 [0.77–0.92]). When analyzed by diabetes therapy, mortality was increased relative to nondiabetes in those on monotherapy with sulfonylureas (1.13 [1.05–1.21]) or insulin (1.13 [1.01–1.27]) but reduced in those on metformin monotherapy (0.85 [0.78–0.93]). CONCLUSIONS This study confirmed that type 2 diabetes was associated with poorer prognosis after incident cancer, but that the association varied according to diabetes therapy and cancer site. Metformin was associated with survival benefit both in comparison with other treatments for diabetes and in comparison with a nondiabetic population.
Nonlinear imaging (NIM) of barely visible impact damage (BVID) in composite panels using a semi and full air-coupled linear and nonlinear ultrasound technique

Two non-contact methods were evaluated to address the reliability and reproducibility concerns affecting industry adoption of nonlinear ultrasound techniques for non-destructive testing and evaluation (NDT/E) purposes. A semi and a fully air-coupled linear and nonlinear ultrasound method was evaluated by testing for barely visible impact damage (BVID) in composite materials. Air coupled systems provide various advantages over contact driven systems; such as: ease of inspection, no contact and lubrication issues and a great potential for non-uniform geometry evaluation. The semi air-coupled setup used a suction attached piezoelectric transducer to excite the sample and an array of low-cost microphones to capture the signal over the inspection area, while the second method focused on a purely air-coupled setup, using an air-coupled transducer to excite the structure and capture the signal. One of the issues facing nonlinear and any air-coupled systems is transferring enough energy to stimulate wave propagation and in the case of nonlinear ultrasound; damage regions. Results for both methods provided nonlinear imaging (NIM) of damage regions using a sweep excitation methodology, with the semi aircoupled system providing clearer results.
A cost model for software maintenance & evolution

The purpose of this essay is to present a costing model for software maintenance and evolution based on a separation of fixed and variable costs. There has always been a problem in distinguishing between the maintenance activities covered by the standard maintenance fee and those charged extra to the user. Separating these two types of costs is essential to every maintenance operation to prevent costs from getting out of control. In this paper, the author proposes a solution, which can lead to better cost estimations and a financially more stable maintenance operation. Particular emphasis is placed on a sharp division between work done to maintain the system functionality as it is and work done to enhance that functionality.
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking

Visual tracking usually requires an object appearance model that is robust to changing illumination, pose, and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object appearance model is built. This approach has the following limitations: 1) The bases are data driven, so they can be easily corrupted, and 2) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these high-frequency coefficients, we simultaneously obtain a compact 3D-DCT-based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCT algorithm which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational complexity. Based on this incremental 3D-DCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object. We then embed the discriminative criterion into a particle filtering framework for object state inference over time. Experimental results demonstrate the effectiveness and robustness of the proposed tracker.
Personalizing Therapy in Advanced Non–Small Cell Lung Cancer

Abstract The recognition that non–small cell lung cancer (NSCLC) is not a single disease entity, but rather a collection of distinct molecularly driven neoplasms, has permanently shifted the therapeutic landscape of NSCLC to a personalized approach. This personalization of NSCLC therapy is typified by the dramatic response rates seen in EGFR mutant NSCLC when treated with targeted tyrosine kinase inhibitor therapy and in ALK translocation–driven NSCLC when treated with ALK inhibitors. Targeted therapeutic approaches in NSCLC necessitate consideration of more invasive biopsy techniques aimed at providing sufficient tissue for both histological determination and molecular profiling in all patients with stage IV disease both at the time of diagnosis and at the time of disease progression. Comprehensive genotyping efforts have identified oncogenic drivers in 62% lung adenocarcinomas and an increasing proportion of squamous cell carcinomas of the lung. The identification of these oncogenic drivers and the triage of patients to clinical trials evaluating novel targeted therapeutic approaches will increasingly mold a landscape of personalized lung cancer therapy where each genotype has an associated targeted therapy. This review outlines the state of personalized lung cancer therapy as it pertains to individual NSCLC genotypes.
Sum of Squares Basis Pursuit with Linear and Second Order Cone Programming

We devise a scheme for solving an iterative sequence of linear programs (LPs) or second order cone programs (SOCPs) to approximate the optimal value of any semidefinite program (SDP) or sum of squares (SOS) program. The first LP and SOCP-based bounds in the sequence come from the recent work of Ahmadi and Majumdar on diagonally dominant sum of squares (DSOS) and scaled diagonally dominant sum of squares (SDSOS) polynomials. We then iteratively improve on these bounds by pursuing better bases in which more relevant SOS polynomials admit a DSOS or SDSOS representation. Different interpretations of the procedure from primal and dual perspectives are given. While the approach is applicable to SDP relaxations of general polynomial programs, we apply it to two problems of discrete optimization: the maximum independent set problem and the partition problem. We further show that some completely trivial instances of the partition problem lead to strictly positive polynomials on the boundary of the sum of squares cone and hence make the SOS relaxation fail.
Optimal detection of blurred edges

I develop a variational model for the detection of blurred edges. Though yielding a complicated general solution, the model gives insight into edge detection models previously proposed for step edges. In particular it shows that a previous IIR solution for step edges is correct even in the FIR case, and suggests why operators approximating the first derivative of a gaussian are successful, despite their suboptimality for step edge detection.
Streamroller:: automatic synthesis of prescribed throughput accelerator pipelines

In this paper, we present a methodology for designing a pipeline of accelerators for an application. The application is modeled using sequential C language with simple stylizations. The synthesis of the accelerator pipeline involves designing loop accelerators for individual kernels, instantiating buffers for arrays used in the application, and hooking up these building blocks to form a pipeline. A compiler-based system automatically synthesizes loop accelerators for individual kernels at varying performance levels. An integer linear program formulation which simultaneously optimizes the cost of loop accelerators and the cost of memory buffers is proposed to compose the loop accelerators to form an accelerator pipeline for the whole application. Cases studies for some applications, including FMRadio and Beamformer, are presented to illustrate our design methodology. Experiments show significant cost savings are achieved through hardware sharing, while achieving the prescribed throughput requirements.
Benefits and risks of fish consumption for the human health

The article aimed at identifying and discussing scientific evidences on the benefits and risks of fish consumption the human health. There was a systematic survey for articles published from 2003 and May 2011, at the MedLine, Scopus, SciELO, Lilacs and Google Scholar databases. The key words used were: fish, food intake, omega-3 fatty acids, fatty fish, benefits, risk, and consumption. The search produced 12,632 articles, 25 eligible cohort studies on possible benefits, 61 on risks and 10 studies that assessed the "risk/benefit" relation. Of the 25 works, 14 suggested a preventive effect of fish consumption related to cardiovascular diseases, depression, cataract and some types of cancer. Evidences of a relation between exposure to mercury and an increase in the risk of neurological disorders, but not of cardiovascular diseases, were also found. Given the importance of fish consumption, its possible risks and the lack of Brazilian studies on the topic, it is important to conduct more longitudinal studies that assess both the benefits and risks of fish consumption for the human health. We also emphasize the need for policies to reduce exposure of fish and seafood to mercury and other contaminants.
Appointment scheduling in health care: Challenges and opportunities

Appointment scheduling systems are used by primary and specialty care clinics to manage access to service providers, as well as by hospitals to schedule elective surgeries. Many factors affect the performance of appointment systems including arrival and service time variability, patient and provider preferences, available information technology and the experience level of the scheduling staff. In addition, a critical bottleneck lies in the application of Industrial Engineering and Operations Research (IE/OR) techniques. The most common types of health care delivery systems are described in this article with particular attention on the factors that make appointment scheduling challenging. For each environment relevant decisions ranging from a set of rules that guide schedulers to real-time responses to deviations from plans are described. A road map of the state of the art in the design of appointment management systems is provided and future opportunities for novel applications of IE/OR models are identified.
Scalable transmission over heterogenous networks

Transmission of layered source information, such as scalable video coding (SVC), over heterogenous wireless networks is considered in this work. Scalable transmission enables dynamic adaption of source information to the condition of user equipments, and thus is suitable for heterogenous networks in which the transmission link quality varies substantially. Leveraging tools in stochastic geometry, a comprehensive analysis is conducted for several different transmission protocols, focusing on two key performance metrics, Standard-Definition outage probability and High-Definition probability. The proposed transmission protocols are compared in different aspects, and the benefit of interference cancellation is shown to be significant.
Structural damage detection based on iteratively reweighted l1 regularization algorithm

Structural damage usually appears in a few sections or members only, which is sparse compared with the total elements of the entire structure. According to the sparse recovery theory, the recently developed damage detection methods employ the l1 regularization technique to exploit the sparsity condition of structural damage. However, in practice, the solution obtained by the l1 regularization is typically suboptimal. The l0 regularization technique outperforms the l1 regularization in various aspects for sparse recovery, whereas the associated nonconvex optimization problem is NP-hard and computationally infeasible. In this study, a damage detection method based on the iteratively reweighted l1 regularization algorithm is proposed. An iterative procedure is employed such that the nonconvex optimization problem of the l0 regularization can be efficiently solved through transforming it into a series of weighted l1 regularization problems. Experimental example demonstrates that the proposed damage detection method can accurately locate the sparse damage over a large number of elements. The advantage of the iteratively reweighted l1 regularization algorithm over the l1 regularization in damage detection is also demonstrated.
Long-term resilient behaviour of thawed saturated silty clay under repeated cyclic loading: experimental evidence and evolution model

In seasonally frozen regions, subgrade soils are not only being subjected to large numbers of repeated cyclic loading at a stress level lower than shear strength, but also suffer from the effect of freeze–thaw cycles, which could make the long-term resilient behaviours of subgrade soils as important as the short-term resilient behaviours determined by standard test procedures. In this study, three series of undrained cyclic tri-axial tests were performed to investigate the evolution of the resilient modulus for thawed saturated silty clay during the long-term repeated cyclic loading. Effects of the freeze–thaw cycles, cyclic stress amplitude, confining pressure and loading frequency were considered. Based on the experimental evidences, it was found that the normalised resilient modulus decreases with an increase in the number of freeze–thaw cycles, cyclic stress amplitude and number of loading cycles, respectively, but increases with an increase in confining pressure and increasing loading frequency. An evolution model, which considers the effects of these five influencing factors together with the values of fitting parameters, was established and validated. The proposed model can be used for predicting the evolution of resilient modulus for thawed saturated subgrade clays under the long-term repeated traffic loading, and assist in evaluating the long-term deformation of pavement in seasonally frozen regions.
Fast Algorithm for Approximate k-Nearest Neighbor Graph Construction

The k-Nearest Neighbor (k-NN) graphs are widely used in data mining and machine learning. How to construct a high quality k-NN graph for generic similarity measures efficiently is crucial for many applications. In this paper, we propose a new approach to effectively and efficiently construct an approximate k-NN graph. Our framework is as follows: (1) generate a random k-NN graph approximation, Gf, (2) perform random hierarchical partitions of the space to construct an approximate neighborhood graph Gp, which is then combined with graph Gf to yield a more accurate graph Gm, (3) neighborhood propagation is conducted on Gm to further enhance the accuracy, and output the solution as graph Gf, (4) repeat the process of (2) and (3) several times until a reasonable solution is reached. The experiments on a variety of real data sets and a high intrinsic dimensional synthetic data set verify the high performance of the proposed method and demonstrate that it is superior to previous state-of-the-art k-NN graph construction approaches.
Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews

In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online consumer reviews. The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and (b) consumers' opinions on the important aspects greatly influence their overall opinions on the product. In particular, given consumer reviews of a product, we first identify the product aspects by a shallow dependency parser and determine consumers' opinions on these aspects via a sentiment classifier. We then develop an aspect ranking algorithm to identify the important aspects by simultaneously considering the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions. The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach. We further apply the aspect ranking results to the application of document-level sentiment classification, and improve the performance significantly.