Miracle-dz/UniFAR-FTUs huggingface dataset:
Density-Difference Estimation

We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small estimation error incurred in the first stage can cause a big error in the second stage. In this letter, we propose a single-shot procedure for directly estimating the density difference without separately estimating two densities. We derive a nonparametric finite-sample error bound for the proposed single-shot density-difference estimator and show that it achieves the optimal convergence rate. We then show how the proposed density-difference estimator can be used in L2-distance approximation. Finally, we experimentally demonstrate the usefulness of the proposed method in robust distribution comparison such as class-prior estimation and change-point detection.
A New Process for Manufacturing Straw Particleboard by Impregnating and Enzyme Treatment

However, the wheat straw surface was difficult to be penetrated by adhesive due to the wax layer existed on its surface. It is a key point that how to manufacture straw particleboard with traditional resin. This paper explored a novel method to solve this problem by pre-treating straw particles with enzyme and impregnating with resin. Different from conventional spray application, straw was impregnated with phenolic resin (PF). Part of these straws was pretreated with xylanase and lipase before impreanating to improve the bonding between adhesive and straw. The results indicated that when straw was pretreated with lipase and and then applied 10% PF by impregnating, the properties of wheat straw particleboard were optimal, bending strength was 13.81 MPa, elastic modulus was 2.67GPa, internal bond strength was 0.113MPa and 2h thickness swelling was 16.74%.
Acute liver failure after recommended doses of acetaminophen in patients with myopathies

Objective:To determine the likelihood that recommended doses of acetaminophen are associated with acute liver failure in patients with myopathies. Design:Retrospective analysis. Setting:Level III pediatric intensive care unit. Patients:Two pediatric patients with myopathies and acute liver failure. Clinical Investigations:We determined acetaminophen protein adduct levels, in combination with a literature review and systematic evaluation of the cases, using the Roussel Uclaf Causality Assessment Method for drug-induced liver injury to assess causality between recommended acetaminophen dosing and acute liver failure in two children with myopathies. Main Results:The serum adduct levels were consistent with the values previously reported in children with acute liver injury following acetaminophen overdose. We found four similar cases of acute liver failure in pediatric and adult patients with myopathies following recommended acetaminophen doses in the literature (n = 3) and personal communication (n = 1). The Roussel Uclaf Causality Assessment Method suggested a probable relationship between acetaminophen use at recommended doses and acute liver failure in our myopathy patients. Conclusion:Our data suggest that some patients with myopathies who are receiving recommended doses of acetaminophen may be at increased risk for the development of toxicity resulting in acute liver failure. More studies are needed to corroborate these findings. In the meantime, we would advise physicians to be alert in these patients while taking acetaminophen, especially when critically ill or postoperative.
Morphometric Analysis of the Hard Parts of Pseudodactylogyrus anguillae and Pseudodactylogyrus bini (Monogenea: Dactylogyridae) on the Gill Apparatus of the European Eels (Anguilla anguilla) from the Freshwaters of Latvia

Abstract We investigated the spatial distribution and measured the hard parts of the attachment apparatuses and reproductive organs of the 2 eel-specific monogenean species Pseudodactylogyrus anguillae and Pseudodactylogyrus bini. Comparative analysis focused on parasite microhabitats within the gill apparatus. Additionally, we compared the morphometric characteristics of the monogeneans based on geographical locality and seasonality. The attachment apparatus and reproductive organ characteristics of the monogeneans were similar in all gill apparatus microhabitats. However, measurements differed significantly between localities and between seasons, with the results suggesting that abiotic factors play a larger role in determining parasite morphology than do the gill microhabitats.
FPGA based Speeded Up Robust Features

We present an implementation of the Speeded Up Robust Features (SURF) on a Field Programmable Gate Array (FPGA). The SURF algorithm extracts salient points from image and computes descriptors of their surroundings that are invariant to scale, rotation and illumination changes. The interest point detection and feature descriptor extraction algorithm is often used as the first stage in autonomous robot navigation, object recognition and tracking etc. However, detection and extraction are computationally demanding and therefore can't be used in systems with limited computational power. We took advantage of algorithm's natural parallelism and implemented it's most demanding parts in FPGA logic. Several modifications of the original algorithm have been made to increase it's suitability for FPGA implementation. Experiments show, that the FPGA implementation is comparable in terms of precision, speed and repeatability, but outperforms the CPU and GPU implementation in terms of power consumption. Our implementation is intended to be used in embedded systems which are limited in computational power or as the first stage preprocessing block, which allows the computational resources to focus on higher level algorithms.
The importance of peripheral immune cells in inflammatory bowel disease.

BACKGROUND/AIMS Inappropriate down regulation of an activated immune system is considered as the main pathogenetic mechanism in inflammatory bowel disease. Migration of circulating cells to a diseased intestine is considered as an important factor in the pathogenesis of inflammatory bowel disease. We aimed to evaluate some features of circulating immune cells in inflammatory bowel disease. METHODS Twenty-two control, 29 Crohn's disease and 17 ulcerative colitis patients were studied. CD2, CD3, CD4, CD8, CD11b, CD11c, CD25, CD45RA, CD45RO, CD54 and HLA DR on the surface of peripheral blood lymphocytes and CD11b, CD11c, CD45RA and CD45RO on the phagocytes were researched with two-color immunofluorescence flow cytometry. RESULTS The percentages of CD2+ and CD4+ lymphocytes were found significantly reduced in ulcerative colitis. CD3+ and CD8+ lymphocytes in inflammatory bowel disease were higher than in controls. CD45RA+ lymphocytes were found significantly decreased in ulcerative colitis and active Crohn's disease. CD45RO+ lymphocytes and CD45RO+, CD11b+ and CD11c+ phagocytes were significantly increased in Crohn's disease. CONCLUSIONS We demonstrated that there were significant differences between ulcerative colitis and Crohn's disease in the expression of some important surface markers on the peripheral blood immune cells. It seems that circulating CD11b-CD11c and CD45RA-CD45RO expressing phagocytes are important in inflammatory bowel disease and may be useful in distinguishing Crohn's disease from ulcerative colitis. These findings may give us some clues about the immunopathogenesis of inflammatory bowel disease.
It’s a Contradiction – no, it’s not: A Case Study using Functional Relations

Contradiction Detection (CD) in text is a difficult NLP task. We investigate CD over functions (e.g., BornIn(Person)=Place), and present a domain-independent algorithm that automatically discovers phrases denoting functions with high precision. Previous work on CD has investigated hand-chosen sentence pairs. In contrast, we automatically harvested from the Web pairs of sentences that appear contradictory, but were surprised to find that most pairs are in fact consistent. For example, "Mozart was born in Salzburg" does not contradict "Mozart was born in Austria" despite the functional nature of the phrase "was born in". We show that background knowledge about meronyms (e.g., Salzburg is in Austria), synonyms, functions, and more is essential for success in the CD task.
A Cellular Neural Network Methodology for Deformable Object Simulation

This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extrapolating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation
Neural fuzzy based indoor localization by Kalman filtering with propagation channel modeling

In this study, an indoor localization based on the received signal strength indication (RSSI) in wireless sensor networks (WSN) is proposed. The presented approach proceeds in two phases: the first phase is based on the recorded received signal strength at the certain location. The interpolation, curve fitting and an adaptive neural fuzzy inference system (ANFIS) are used to develop the indoor propagation model, respectively. Thus the strength of the received radio signal can be converted to a physical distance approximately; in the second phase, based on the available distances from the positions localized in the test bed are estimated by using an extended Kalman filter (EKF). In comparison among the propagation models based on the interpolation, ANFIS and curve fitting, the experimental results show that the proposed approach provides a precise performance.
A STUDY OF THOUGHT, LANGUAGE AND COMMUNICATION (T.L.C) DISORDERS IN SCHIZOPHRNIA*

SUMMARY This study examines in detail - i) the magnitude, nature and severity of thought disorder in schizophrenia, ii) the correlations between type and severity of thought disorder with socio-demographic and clinical variables, and iii) differences between different subtypes of schizophrenia. Forty five schizophrenics (Research Diagnostic Criteria) were assessed by ‘live’ interview as well as tape recorded interviews. Instruments used for assessment were (a) Scale for assessment of Thought, Language and Communication (Andreasen 1978), (b) Brief Psychiatric Rating Scale (Overall & Gorham 1962), (c) Mini Mental State (Folstein 1975), and (d) Clinical and demographic data recording proforma. The Schizophrenic patients were subdivided as (i) Acute and chronic (R.D.C.), (ii) Paranoid and non-paranoid; and (iii) Negative, positive, mixed (Andreasen's criteria) and intragroup and intergroup differences were computed. Poverty of speech, tangentiality, derailment, loss of goal, perseveration were found to be the commonest thought disorders. Positive and negative thought disorders were seen in equiproportion in both positive and negative schizophrenics. Significant differences were noted between thought disorders and education as well as habitat. Rural patients more often had negative formal thought disorders. Literates had more often clanging, neologism, circumstantiality and echolalia. This study provides ample information on the nature of thought disorder in Indian schizophrenic subjects.
A Statistical Constraint Dependency Grammar (CDG) Parser

CDG represents a sentence's grammatical structure as assignments of dependency relations to functional variables associated with each word in the sentence. In this paper, we describe a statistical CDG (SCDG) parser that performs parsing incrementally and evaluate it on the Wall Street Journal Penn Treebank. Using a tight integration of multiple knowledge sources, together with distance modeling and synergistic dependencies, this parser achieves a parsing accuracy comparable to several state-of-the-art context-free grammar (CFG) based statistical parsers using a dependency-based evaluation metric. Factors contributing to the SCDG parser's performance are analyzed.
Determinants of Organisational Learning in the generation of technological distinctive competencies

This paper presents an analysis of the influence of Organisational Learning (OL), as a process of knowledge creation, in the development of Technological Distinctive Competencies (TDCs). We also examine how the existence of certain organisational key variables in the OL process, not treated jointly from this perspective, positively affects OL and influences the generation of TDCs. The hypotheses proposed in this respect are tested on a sample of 140 Spanish industrial companies, applying a structural linear equation model according to the Partial Least Squares (PLS) approach. Our findings indicate that factors such as Entrepreneurial Orientation (EO) of the organisation, its Learning Orientation (LO) and Information Technology (IT) have a positive impact on OL; whereas, the technological distinctive competences are the result of the OL. On the other hand, the EO of the organisation and the IT play an important role in the creation process of TDCs.
Depression Screening and Treatment Among Nonpregnant Women of Reproductive Age in the United States, 1990-2010

Introduction Whether routine screening for depression among nonpregnant women of reproductive age improves identification and treatment of the disorder remains unclear. We conducted a systematic review of the literature to address 5 key questions specific to this population: 1) What are the current national clinical practice recommendations and guidelines for depression screening; 2) What are the prevalence and predictors of screening; 3) How well do screening tools detect depression; 4) Does screening lead to diagnosis, treatment, and improved outcomes; and 5) What are the most effective treatment methods? Methods We searched bibliographic databases for full-length articles published in English between 1990 and 2010 that addressed at least 1 of our key questions. Results We identified 5 clinical practice guidelines pertinent to question 1, and 12 systematic reviews or post-hoc analyses of pooled data that addressed questions 3 through 5. No systematic reviews addressed question 2; however, we identified 4 individual studies addressing this question. Current guidelines do not recommend universal screening for depression in adults, unless staff supports are in place to diagnose, treat, and follow up patients. Reported screening rates ranged from 33% to 84% among women. Several validated screening tools for depression exist; however, their performance among this population is unknown. Screening in high-risk populations may improve the patient's receipt of diagnosis and treatment. Effective treatments include exercise, psychotherapy, and pharmacotherapy. Conclusion More research is needed on whether routine screening for depression among women of reproductive age increases diagnosis and treatment of depression, improves preconception health, and reduces adverse outcomes.
Extraction of topic evolutions from references in scientific articles and its GPU acceleration

This paper provides a topic model for extracting topic evolutions as a corpus-wide transition matrix among latent topics. Recent trends in text mining point to a high demand for exploiting metadata. Especially, exploitation of reference relationships among documents induced by hyperlinking Web pages, citing scientific articles, tumblring blog posts, retweeting tweets, etc., is put in the foreground of the effort for an effective mining. We focus on scholarly activities and propose a topic model for obtaining a corpus-wide view on how research topics evolve along citation relationships. Our model, called TERESA, extends latent Dirichlet allocation (LDA) by introducing a corpus-wide topic transition probability matrix, which models reference relationships as transitions among topics. Our approximated variational inference updates LDA posteriors and topic transition posteriors alternately. The main issue is execution time amounting to O(MK2), where K is the number of topics and M is that of links in citation network. Therefore, we accelerate the inference with Nvidia CUDA compatible GPUs. We compare the effectiveness of TERESA with that of LDA by introducing a new measure called diversity plus focusedness (D+F). We also present topic evolution examples our method gives.
A Reconfigurable Antenna for LTE/WWAN Mobile Handset Applications

In this letter, a reconfigurable antenna for mobile handset applications is proposed. This antenna mainly consists of a longer strip, a shorter strip, and a p-i-n diode. Coupling is generated at the gap between the longer strip and the shorter strip. Depending on the different states of the p-i-n diode, the degree of coupling between them is controlled, and the fundamental resonant frequency of the proposed antenna is adjusted. As a result, the proposed antenna in the low band covers the LTE700/GSM850/900 bands. The high band is formed by combining higher-order mode of the longer strip and quarter-wavelength mode of the shorter strip to cover the GSM1800/1900/UMTS/LTE2300/2500 bands.
A 9.2-GHz digital phase-locked loop with peaking-free transfer function

This paper describes a digital phase-locked loop (PLL) that realizes a peaking-free jitter transfer. That is the PLL's second-order transfer function does not have a closed-loop zero. Such a PLL does not exhibit overshoots in the phase step response and achieves fast settling. Unlike the previously-reported peaking-free PLLs the proposed PLL implements the peaking-free loop filter directly in digital domain without requiring additional components. A time-to-digital converter (TDC) is implemented as a set of three binary phase-frequency detectors that oversample the timing error with time-varying offsets achieving a linear TDC gain and PLL bandwidth insensitive to the jitter condition. And a 9.2-GHz digitally-controlled LC oscillator (DCO) with transformer-based tuning realizes a predictable DCO gain set by a ratio between two digitally-controlled currents. The prototype 9.2-GHz-output digital PLL fabricated in a 65nm CMOS demonstrates a fast settling time of 1.58-μs with 690-kHz bandwidth. The PLL has a 3.477-psrms divided clock jitter and -120dBc/Hz phase noise at 10MHz offset while dissipating 63.9-mW at a 1.2-V supply.
Does Alerting have special Requirements for Query Languages?

Alerting Systems keep its users informed about new documents and events they are interested in. The power of such services depends on the expressiveness of the language used to define a user’s interest. In this paper, we show restrictions of existing languages and discuss additional language parameters. We will emphasize briefly the problem of different underlying time systems and propose a common profile structure that can serve as basis for a Profile Definition Language for Alerting.
Corrosion of high Ni-Cr alloys and Type 304L stainless steel in HNO/sub 3/-HF

Nineteen alloys were evaluated as possible materials of construction for steam heating coils, the dissolver vessel, and the off-gas system of proposed facilities to process thorium and uranium fuels. Commercially available alloys were found that are satisfactory for all applications. With thorium fuel, which requires HNO/sub 3/-HF for dissolution, the best alloy for service at 130/sup 0/C when complexing agents for fluoride are used is Inconel 690; with no complexing agents at 130/sup 0/C, Inconel 671 is best. At 95/sup 0/C, six other alloys tested would be adequate: Haynes 25, Ferralium, Inconel 625, Type 304L stainless steel, Incoloy 825, and Haynes 20 (in order of decreasing preference); based on composition, six untested alloys would also be adequate. The ions most effective in reducing fluoride corrosion were the complexing agents Zr/sup 4 +/ and Th/sup 4 +/; Al/sup 3 +/ was less effective. With uranium fuel, modestly priced Type 304L stainless steel is adequate. Corrosion will be most severe in HNO/sub 3/-HF used occasionally for flushing and in solutions of HNO/sub 3/ and corrosion products (ferric and dichromate ions). HF corrosion can be minimized by complexing the fluoride ion and by passivation of the steel with strong nitric acid. Corrosion causedmore » by corrosion products can be minimized by operating at lower temperatures. « less
Learning the kernel matrix via predictive low-rank approximations

Efficient and accurate low-rank approximations of multiple data sources are essential in the era of big data. The scaling of kernel-based learning algorithms to large datasets is limited by the O(n^2) computation and storage complexity of the full kernel matrix, which is required by most of the recent kernel learning algorithms. We present the Mklaren algorithm to approximate multiple kernel matrices learn a regression model, which is entirely based on geometrical concepts. The algorithm does not require access to full kernel matrices yet it accounts for the correlations between all kernels. It uses Incomplete Cholesky decomposition, where pivot selection is based on least-angle regression in the combined, low-dimensional feature space. The algorithm has linear complexity in the number of data points and kernels. When explicit feature space induced by the kernel can be constructed, a mapping from the dual to the primal Ridge regression weights is used for model interpretation. The Mklaren algorithm was tested on eight standard regression datasets. It outperforms contemporary kernel matrix approximation approaches when learning with multiple kernels. It identifies relevant kernels, achieving highest explained variance than other multiple kernel learning methods for the same number of iterations. Test accuracy, equivalent to the one using full kernel matrices, was achieved with at significantly lower approximation ranks. A difference in run times of two orders of magnitude was observed when either the number of samples or kernels exceeds 3000.
Adding Unlabeled Samples to Categories by Learned Attributes

We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a large-scale dataset, Image Net.