Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolated isochromosome 17q: a distinct type of mixed myeloproliferative disorder/myelodysplastic syndrome with an aggressive clinical course

A clinicopathologic study was performed on 15 patients with haematological malignancies in which isochromosome 17q [i(17q)] was the sole structural chromosome abnormality identified in bone marrow. The data indicated that an isolated i(17q) is associated with a distinct type of mixed chronic myeloproliferative/myelodysplastic disorder with an aggressive clinical course. The patients ranged in age from 37 to 83 years (median 60) with a M:F ratio of 3:1. All cases were chronic myeloid disorders with mixed proliferative and dysplastic features, making classification difficult. 11 patients tested for BCR/ABL gene fusion were normal. A low bone marrow blast count (<5%) at presentation was a typical finding. All cases had severe myeloid dysplasia which included non‐segmented neutrophils and an increase in the monocyte/macrophage lineage. Fluorescence in situ hybridization (FISH) analysis of one case showed the i(17q) to involve all myeloid lineages, but not the lymphocytes. For cases with complete follow‐up (n = 11) the median survival was 2.5 years (range 0.83–5.25) and 64% progressed to AML prior to death. The following features were identified which defined the haematological disorder associated with an isolated i(17q): (1) adult patient, (2) chronic myeloid disorder with clonal involvement of all myeloid lineages, (3) mixed chronic myeloproliferative/myelodysplastic features, (4) severe hyposegmentation of neutrophil nuclei, (5) prominence of the monocyte/macrophage lineage, (6) high risk for progression to AML, and (7) median survival of 2.5 years.
Confidence-based Rank-level Fusion for Audio-visual Person Identification System

A multibiometric identification system establishes the identity of a person based on the input biometric data presented to its sub-systems. Each sub-system compares the features extracted from the input against the templates of all identities stored in gallery. The best matched identity is ranked highest in the ranked list. In rank-level fusion, the ranked lists from different sub-systems are combined to reach a final decision. However, the state-of-the-art rank-level fusion methods consider that all sub-systems are equally reliable in terms of classifying the probe data. In practice, the probe data may be affected by different sources of degradation (e.g., illumination and pose variation on the face image, environmental noise) and thus affecting the overall recognition accuracy. In this paper, robust rank-level fusion methods (e.g., confidence based highest rank and Borda count) are proposed by using confidence measures for each sub-system in the decision making process. Experimental results show that the proposed confidence based rank-level fusion achieved higher recognition rates than state-of-the-art rank-level fusion methods.
Constrained clustering via spectral regularization

We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not. Unlike previous methods that modify the similarity matrix with pairwise constraints, we adapt the spectral embedding towards an ideal embedding as consistent with the pairwise constraints as possible. Our formulation leads to a small semidefinite program whose complexity is independent of the number of objects in the data set and the number of pairwise constraints, making it scalable to large-scale problems. The proposed approach is applicable directly to multi-class problems, handles both must-link and cannot-link constraints, and can effectively propagate pairwise constraints. Extensive experiments on real image data and UCI data have demonstrated the efficacy of our algorithm.
Autoimmune hepatitis in patients with human immunodeficiency virus (HIV)

Rationale: Chronic liver disease is a major cause of morbidity and mortality in patients with HIV. However, autoimmune hepatitis (AIH) in patients with HIV has rarely been reported. Our aim was to evaluate a cohort of patients with HIV and AIH and identify clinical presentations and outcomes. Patient Concerns: Management of autoimmune hepatitis in context of human immunodeficiency virus, long-term outcomes, and safety in setting of underlying immunocompromised state. Diagnoses: Autoimmune Hepatitis, Human Immunodeficiency Virus, Hepatotoxicity, Liver Injury, Liver Transplantation. Interventions: We retrospectively reviewed the charts of patients with HIV and AIH based on histological, serologic, biochemical demographic, and clinical data. Outcomes: Five patients were identified with autoimmune hepatitis; 4 of 5 were women, and all were African or African-American. The age at the time of AIH diagnosis was 46.6 ± 13.4 years. All patients acquired HIV sexually and all had CD4 counts >250 cells/uL (456–1011 cells/uL) and undetectable HIV viral loads at the time of AIH diagnosis. One patient presented with acute liver failure necessitating liver transplantation and developed AIH posttransplantation. At the time of diagnosis, the AST were 350 ± 448 U/L, ALT 247 ± 190 U/L, bilirubin 7 ± 12 mg/dL, and alkaline phosphatase 126 ± 53 U/L. All patients had histologic evidence of AIH on liver biopsies. Patients were successfully treated with prednisone and azathioprine, without a decrease in CD4 <250 cells/uL, infectious complications or significant side effects. Lessons: AIH occurs in patients with well-controlled HIV. In our patient cohort, immunosuppressive therapy with prednisone and azathioprine was safe and effective in inducing remission, without significant complications or development of opportunistic infections.
Geometrico-static Problem of Underconstrained Cable-Driven Parallel Robots With n Cables,"

—This paper studies the direct geometrico-static problem of underconstrained cable-driven parallel robots (CDPRs) supported by n cables, with n ≤ 6 . The task consists in identifying the equilibrium poses of the end-effector when cable lengths are speciﬁed. The problem is challenging, because the end-effector preserves some freedoms after cable lengths are assigned by motors. Hence, kinematics and statics are coupled, and they must be tackled simultaneously. A general method is presented to model the problem by a set of algebraic equations, and a least-degree univariate polynomial in the corresponding ideal is numerically found for any value of n . For the efﬁcient computation of the solution set, a software is developed which implements an algorithm based on homotopy continuation. Distinctive features of the code are that it ﬁnds all problem solutions, including those with slack cables, and stability analysis is integrated in order to identify feasible conﬁgurations.
Wisdom of the Crowd in Egocentric Video Curation

Videos recorded by wearable egocentric cameras can suffer from quality degradations that cannot always be fixed by current methods. When several wearable video cameras are viewing the same scene, each having highly variable quality, it is possible to combine them into a single high-quality video. Current techniques select for each point in time the highest quality video stream, but the highest quality video may not be relevant. E.g. the best quality video can come from a person that happen to look sideways from the main attraction. We propose the curation of a single video stream from multiple egocentric videos by requiring that the selected video will also view the most interesting region in the scene. Importance of a region is determined by the "wisdom of the crowd", i.e. the number of cameras looking at a region. The resulting video is more interesting and of higher quality than any individual video streams can possibly obtain. Several examples are presented demonstrating the effectiveness of this technique.
Blind identification of graph filters with sparse inputs

Network processes are often conceptualized as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear graph filters modeling underlying network dynamics. This paper deals with the problem of blind graph filter identification, which finds applications in social and brain networks, to name a few. Given a graph signal y modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients h and the input signal x which is assumed to be sparse. While the filtered graph signal is a bilinear function of x and h, y is also a linear combination of the entries of the rank-one matrix xhT. As with blind deconvolution of time (or spatial) domain signals, it is shown that the blind graph filter identification problem can be tackled via rank minimization subject to linear constraints. Graph-dependent conditions under which the solution set of the optimization problem includes only rank-one matrices are derived. Numerical tests with synthetic and real-world networks corroborate the effectiveness of the blind identification approach.
Hierarchical Network-on-Chip for Embedded Many-Core Architectures

The need for computing power drastically increases and one good solution is to use many-core architectures. Besides, complex embedded applications become data-dependent and their execution time depends on their input data. For this reason, on-line task and data allocation is needed to optimize the architecture efficiency. Moreover, communications are a complex problem in many-core architectures. Because of dynamic allocation, communication paths and network loads become unpredictable, which must be handled by the network. This paper proposes an evaluation of different network topologies in terms of performance and area for many-core architectures. It concludes that hierarchical networks are the best trade-off. In particular, the MultiCross topology is 10 times more efficient than the mesh topology.
Novel nano-rough polymers for cartilage tissue engineering

This study presents an innovative method for creating a highly porous surface with nanoscale roughness on biologically relevant polymers, specifically polyurethane (PU) and polycaprolactone (PCL). Nanoembossed polyurethane (NPU) and nanoembossed polycaprolactone (NPCL) were produced by the casting of PU and PCL over a plasma-deposited, spiky nanofeatured crystalline titanium (Ti) surface. The variables used in the process of making the spiky Ti surface can be altered to change the physical properties of the spiky particles, and thus, the cast polymer substrate surface can be altered. The spiky Ti surface is reusable to produce additional nanopolymer castings. In this study, control plain PU and PCL polymers were produced by casting the polymers over a plain Ti surface (without spikes). All polymer surface morphologies were characterized using both scanning electron microscopy and atomic force microscopy, and their surface energies were measured using liquid contact angle measurements. The results revealed that both NPU and NPCL possessed a higher degree of nanometer surface roughness and higher surface energy compared with their respective unaltered polymers. Further, an in vitro study was carried out to determine chondrocyte (cartilage-producing cells) functions on NPU and NPCL compared with on control plain polymers. Results of this study provided evidence of increased chondrocyte numbers on NPU and NPCL compared with their respective plain polymers after periods of up to 7 days. Moreover, the results provide evidence of greater intracellular protein production and collagen secretion by chondrocytes cultured on NPU and NPCL compared with control plain polymers. In summary, the present in vitro results of increased chondrocyte functions on NPU and NPCL suggest these materials may be suitable for numerous polymer-based cartilage tissue-engineering applications and, thus, deserve further investigation.
Multivariate variance ratio statistics

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a smoothly varying risk premium in calendar time, and show that the limiting distribution is the same as in the constant mean adjustment case. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to three weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. We find evidence of a reduction of linear predictability in the most recent period, for small and medium cap stocks. We find similar results for the main UK stock indexes. The main findings are not substantially affected by allowing for a slowly varying risk premium.
Pavlovian Conditioning of Larval Drosophila: An Illustrated, Multilingual, Hands-On Manual for Odor-Taste Associative Learning in Maggots

Larval Drosophila offer a study case for behavioral neurogenetics that is simple enough to be experimentally tractable, yet complex enough to be worth the effort. We provide a detailed, hands-on manual for Pavlovian odor-reward learning in these animals. Given the versatility of Drosophila for genetic analyses, combined with the evolutionarily shared genetic heritage with humans, the paradigm has utility not only in behavioral neurogenetics and experimental psychology, but for translational biomedicine as well. Together with the upcoming total synaptic connectome of the Drosophila nervous system and the possibilities of single-cell-specific transgene expression, it offers enticing opportunities for research. Indeed, the paradigm has already been adopted by a number of labs and is robust enough to be used for teaching in classroom settings. This has given rise to a demand for a detailed, hands-on manual directed at newcomers and/or at laboratory novices, and this is what we here provide. The paradigm and the present manual have a unique set of features: The paradigm is cheap, easy, and robust; The manual is detailed enough for newcomers or laboratory novices; It briefly covers the essential scientific context; It includes sheets for scoring, data analysis, and display; It is multilingual: in addition to an English version we provide German, French, Japanese, Spanish and Italian language versions as well. The present manual can thus foster science education at an earlier age and enable research by a broader community than has been the case to date.
User Interface Beaming -- Seamless Interaction with Smart Things Using Personal Wearable Computers

For the Internet of Things to be adopted in people's homes and at their workplaces, it is important to provide mechanisms that support them when controlling and monitoring smart things in their surroundings. We present the concept of user interface beaming, where the capabilities of different personal wearable computers are combined to allow users to conveniently interact with smart things in their environment. Smartglasses are used to select a target smart thing by means of current object recognition technologies. Then, an appropriate user interface for the target is rendered on the user's smartwatch. This interface is continuously updated to reflect state changes of the target and can be used to interact with that smart thing using different interaction modalities.
Acute respiratory infections during pregnancy and congenital abnormalities: a population‐based case‐control study

ABSTRACT Diseases of respiratory system caused by acute infections are among the most common maternal diseases during pregnancy. The objective of the study was to estimate the association between congenital abnormalities and acute respiratory infections during the first trimester of pregnancy. The data set of the Hungarian Case‐Control Surveillance of Congenital Abnormalities including 22 843 cases with congenital abnormalities, 38 151 population controls without congenital abnormalities and 834 malformed controls with Down syndrome between 1980 and 1996 was evaluated. 2118 cases with congenital abnormalities (9.3%), 3455 population controls (9.1%) and 92 malformed controls with Down syndrome (11.0%) had mothers with acute respiratory infections. Of 25 different congenital abnormality groups, esophageal atresia/stenosis showed a high adjusted prevalence odds ratios (POR) with 95% confidence interval (CI) for acute respiratory infections during the first trimester of pregnancy in case mothers compared with population controls (3.6, 1.4–9.1) and malformed controls (1.9, 1.0–3.5), respectively. In addition there was an association between medically recorded acute respiratory infections during the first trimester of pregnancy and a higher risk for some other congenital abnormalities, such as posterior cleft palate and multiple congenital abnormalities. In conclusion a possible association between some congenital abnormalities, particularly esophageal atresia/stenosis and maternal acute respiratory infections cannot be excluded due to the interactions of the microbial agents, related drug treatments and last but not least the indirect effect of maternal diseases, such as fever‐hyperthermia, hypoxia and dietary deficiency. However, periconceptional multivitamin/folic acid supplementation during the early pregnancy was able to reduce the acute respiratory infection related risk for congenital abnormalities.
Gradient-based kernel dimension reduction for supervised learning

This paper proposes a novel kernel approach to linear dimension reduction for supervised learning. The purpose of the dimension reduction is to find directions in the input space to explain the output as effectively as possible. The proposed method uses an estimator for the gradient of regression function, based on the covariance operators on reproducing kernel Hilbert spaces. In comparison with other existing methods, the proposed one has wide applicability without strong assumptions on the distributions or the type of variables, and uses computationally simple eigendecomposition. Experimental results show that the proposed method successfully finds the effective directions with efficient computation.
Earlier Migration Timing, Decreasing Phenotypic Variation, and Biocomplexity in Multiple Salmonid Species

Climate-induced phenological shifts can influence population, evolutionary, and ecological dynamics, but our understanding of these phenomena is hampered by a lack of long-term demographic data. We use a multi-decade census of 5 salmonid species representing 14 life histories in a warming Alaskan stream to address the following key questions about climate change and phenology: How consistent are temporal patterns and drivers of phenology for similar species and alternative life histories? Are shifts in phenology associated with changes in phenotypic variation? How do phenological changes influence the availability of resource subsidies? For most salmonid species, life stages, and life histories, freshwater temperature influences migration timing – migration events are occurring earlier in time (mean = 1.7 days earlier per decade over the 3–5 decades), and the number of days over which migration events occur is decreasing (mean = 1.5 days per decade). Temporal trends in migration timing were not correlated with changes in intra-annual phenotypic variation, suggesting that these components of the phenotypic distribution have responded to environmental change independently. Despite commonalities across species and life histories, there was important biocomplexity in the form of disparate shifts in migration timing and variation in the environmental factors influencing migration timing for alternative life history strategies in the same population. Overall, adult populations have been stable during these phenotypic and environmental changes (λ ≈1.0), but the temporal availability of salmon as a resource in freshwater has decreased by nearly 30 days since 1971 due to changes in the median date of migration timing and decreases in intra-annual variation in migration timing. These novel observations advance our understanding of phenological change in response to climate warming, and indicate that climate change has influenced the ecology of salmon populations, which will have important consequences for the numerous species that depend on this resource.
Do Better Educated Immigrants Choose a Higher Reference Income? Evidence from the German Socio-Economic Panel

I propose two new tests of Falk and Knell’s (2004) prediction that individuals’ reference income increases with ability. To overcome the diculty that the reference income is not observed in existing large N data sets, I extend their model to establish a link between immigrants’ reference income and their return visits to their countries of origin. I derive the (arguably counter-intuitive) predictions that more educated immigrants are less likely to have returned to their country of origin for a visit, and that they are more likely to have more diculty feeling at home when they do return for a visit. Both predictions are conrmed on four waves of data from the German Socio
A critique of word similarity as a method for evaluating distributional semantic models

This paper aims to re-think the role of the word similarity task in distributional semantics research. We argue while it is a valuable tool, it should be used with care because it provides only an approximate measure of the quality of a distributional model. Word similarity evaluations assume there exists a single notion of similarity that is independent of a particular application. Further, the small size and low inter-annotator agreement of existing data sets makes it challenging to find significant differences between models.
Automatic Relevance Determination in Nonnegative Matrix Factorization

Abstract Nonnegative matrix factorization (NMF) has be-come a popular technique for data analysis and dimensionalityreduction. However, it is often assumed that the number oflatent dimensions (or components) is given. In practice, onemust choose a suitable value depending on the data and/orsetting. In this paper, we address this important issue by usinga Bayesian approach to estimate the latent dimensionality,or equivalently, select the model order. This is achieved viaautomatic relevance determination (ARD), a technique that hasbeen employed in Bayesian PCA and sparse Bayesian learning.We show via experiments on synthetic data that our techniqueis able to recover the correct number of components, while itis also able to recover an effective number of components fromreal datasets such as the MIT CBCL dataset.Index Terms Nonnegative matrix factorization, Bayesianmodel selection, Automatic relevance determination. I. I NTRODUCTION Nonnegative matrix factorization (NMF) [7] is a widelyused technique that is employed for non-subtractive, part-based representation of nonnegative data. There are numer-ous diverse applications of NMF including audio signalprocessing [5], image classication [6] and email surveil-lance [1]. Given a nonnegative data matrix V 2 R
A Maximum Entropy Framework that Integrates Word Dependencies and Grammatical Relations for Reading Comprehension

Automatic reading comprehension (RC) systems can analyze a given passage and generate/extract answers in response to questions about the passage. The RC passages are often constrained in their lengths and the target answer sentence usually occurs very few times. In order to generate/extract a specific precise answer, this paper proposes the integration of two types of "deep" linguistic features, namely word dependencies and grammatical relations, in a maximum entropy (ME) framework to handle the RC task. The proposed approach achieves 44.7% and 73.2% HumSent accuracy on the Remedia and ChungHwa corpora respectively. This result is competitive with other results reported thus far.