Miracle-dz/UniFAR-FTUs huggingface dataset:
New Graph Structured Sparsity Model for Multi-label Image Annotations

In multi-label image annotations, because each image is associated to multiple categories, the semantic terms (label classes) are not mutually exclusive. Previous research showed that such label correlations can largely boost the annotation accuracy. However, all existing methods only directly apply the label correlation matrix to enhance the label inference and assignment without further learning the structural information among classes. In this paper, we model the label correlations using the relational graph, and propose a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications. As a result, our new method will capture and utilize the hidden class structures in relational graph to improve the annotation results. In proposed objective, a large number of structured sparsity-inducing norms are utilized, thus the optimization becomes difficult. To solve this problem, we derive an efficient optimization algorithm with proved convergence. We perform extensive experiments on six multi-label image annotation benchmark data sets. In all empirical results, our new method shows better annotation results than the state-of-the-art approaches.
Final Report: Integrated Ecosystem Model for Alaska and Northwest Canada Project

1. U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska Fairbanks, Fairbanks, AK 2. International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 3. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 4. Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK FINAL REPORT: Integrated Ecosystem Model for Alaska and Northwest Canada Project
A Study of Method Development, Validation, and Forced Degradation for Simultaneous Quantification of Paracetamol and Ibuprofen in Pharmaceutical Dosage Form by RP-HPLC Method

A rapid and stability-indicating reversed phase high-performance liquid chromatography (RP-HPLC) method was developed for simultaneous quantification of paracetamol and ibuprofen in their combined dosage form especially to get some more advantages over other methods already developed for this combination. The method was validated according to United States Pharmacopeia (USP) guideline with respect to accuracy, precision, specificity, linearity, solution stability, robustness, sensitivity, and system suitability. Forced degradation study was validated according to International Conference on Harmonisation (ICH). For this, an isocratic condition of mobile phase comprising phosphate buffer (pH 6.8) and acetonitrile in a ratio of 65:35, v/v at a flow rate of 0.7 mL/minute over RP C18 (octadecylsilane (ODS), 150 × 4.6 mm, 5 μm, Phenomenex Inc.) column at ambient temperature was maintained. The method showed excellent linear response with correlation coefficient (R2) values of 0.999 and 1.0 for paracetamol and ibuprofen respectively, which were within the limit of correlation coefficient (R2 > 0.995). The percent recoveries for two drugs were found within the acceptance limit of (97.0–103.0%). Intra-and inter-day precision studies of the new method were less than the maximum allowable limit percentage of relative standard deviation (%RSD) ≤ 2.0. Forced degradation of the drug product was carried out as per the ICH guidelines with a view to establishing the stability-indicating property of this method and providing useful information about the degradation pathways, degradation products, and how the quality of a drug substance and drug product changes with time under the influence of various stressing conditions. The degradation of ibuprofen was within the limit (5–20%, according to the guideline of ICH), while paracetamol showed <20% degradation in oxidation and basic condition.
Person-Based Cognitive Therapy for Distressing Psychosis

A clear, practical and client-centred framework is provided for clinicians, which includes: * the use of Mindfulness for people with psychosis * ‘two chair methods’, developed by the author for working with psychosis * a detailed look at the ABC model applied to psychosis * guidelines for working with symptomatic beliefs * a new perspective on the therapeutic relationship and collaboration * a new approach to case formulation based in Vygotsky’s concept of the Zone of Proximal Development * guidelines on the use of group therapy for psychosis.
Activation in unipolar cardiac electrograms: a frequency analysis

Several detectors of local activations in unipolar cardiac electrograms are discussed. The detectors are based on the frequency content of the waveforms. For this study, myocardial regions with no local electrical activity were created with cryoablation in canine ventricles, so that the characteristics of electrograms reflecting local activation could be compared with those with only distant electrical activity. For each electrogram, representations, of the original signal were created using the output of bandpass filters; for each representation, the value of the maximum amplitude was taken as a measurement of the frequency content of the electrogram in that frequency band. The content of each frequency band and the first derivative of the signal were tested as discriminators between local and distant electrical activity. Combinations of frequency bands were also tested using a logistic regression technique. It was found that a detector based on multivariate analysis of different frequency components of a signal may be more effective than single-band filtering in discriminating between local and distant electrical activity in the heart, especially when those components have very different magnitudes. <<ETX>>
Genetic and brain imaging contributions to neuropsychological functioning in preclinical dementia

A veritable explosion of research in neuropsychology has occurred over the past decade in the search for cognitive and brain changes during a so-called “preclinical” phase of dementia that precedes its overt clinical manifestations. Fueling this explosion, in part, has been the revolution in the genetic bases of disease formulated from the international work of decoding the human genome (International Human Genome Sequencing Consortium, 2001; see also Patenaude et al., 2002, for discussion). The discovery of preclinical cognitive, brain, and genetic markers of dementia is helping to push back the point at which diseases can be reliably identified. Very early detection of dementia is extremely important now that a variety of investigational treatments that might prevent or delay disease progression (e.g., amyloid vaccine, anti-oxidants, nonsteroidal anti-inflammatory agents, cholinesterase inhibitors, estrogens, and others in the case of Alzheimer's disease) are on the horizon.
Reuse of Security Assessment Results under Design and Maintenance of IT Systems

Security assessments are costly and time consuming and cannot be carried out from scratch each time a system is updated or modified. This motivates the need for specific methodology addressing the maintenance and reuse of assessment results in particular, and a component oriented approach to security assessment in general. Traditionally, system development methodologies focus on the development of single systems. More recently, the emphasis has shifted towards the development of system product lines. We define a system product line, as a set of systems that share a common, managed set of features satisfying the specific needs of a particular market segment or mission and that are developed from a common set of core entities in a prescribed way. A well accepted idea is that components provide the perfect foundation for the practical application of product line development. The CORAS methodology supports reuse of assessment results in a product-line oriented system development.
Treatment of End Stage Heart Failure Related to Cardiac Amyloidosis

Amyloidosis is a disease characterized by deposition of extracellular proteinaceous mate‐ rial known as amyloid in tissues. Amyloidoses are classified according to the protein composition and the clinical characteristics of the disease [1]. Amyloid protein can accu‐ mulate at various speeds in multiple organ systems and the disease can have localized or systemic manifestations depending on organ involvement. Amyloidotic cardiomyopathy or cardiac amyloidosis is characterized as a restrictive cardiomyopathy associated with in‐ creased ventricular wall thickness caused by the accumulation of amyloid in the heart [2]. Cardiac amyloidosis is of special interest since its occurrence usually has a significant impact on morbidity and prognosis.
An Automatic Snow Depth Probe for Field Validation Campaigns

An automatic snow depth probe (magnaprobe) patented in 1999 (United States Patent 5,864,059, 1999) and produced commercially by Snow‐Hydro LLC has now been used to obtain more than a million simultaneous snow depths (up to 140 cm) and GPS measurements during a wide range of field validation campaigns. The magnaprobe consists of a ski pole‐like rod housing a magnetostrictive device along which a basket and magnet assembly slides. The rod is inserted to the base of the snow, the basket floats on the snow, and when a button is pushed, the distance between rod tip and basket is measured while a position is acquired. The nature of the substrate beneath the snow controls the snow depth accuracy with errors ranging from near zero for hard bases to +5 cm in soft vegetation. The Wide Area Augmentation System‐enabled GPS provides a position accurate to ±2.5 m. The probe increases the speed with which a depth and position measurement can be obtained by a factor of 10 compared to measuring with a traditional ruler probe and writing down the results. The highest boost in snow depth measurement efficiency occurs when the distance between measuring locations is kept relatively small (<10 m). Magnaprobes have materially improved our ability to evaluate airborne and satellite‐based snow depth products and are likely to see continued heavy use over the next decade as efforts to develop satellite systems for monitoring snow remotely are tested in various field settings.
Joint Quantile Regression through Bayesian Semiparametrics

We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and piecewise linearity constraints. We develop a probability model for all quantile curves in a continuum that define a coherent sampling distribution of the response variable. We provide a detailed illustration of model fitting and inference by analyzing wind speed trends of tropical cyclones in the North Atlantic.
High-Resolution Separation of Cardiovascular Proteomes: Two-Dimensional Electrophoresis and Liquid Chromatography

With 2-dimensional electrophoresis and liquid chromatography (LC)-based proteomics firmly established, scientists are provided highly advanced and reliable tools to take major steps toward understanding functional networks leading to cardiovascular disease. This review aims to provide an educational background for 2-dimensional electrophoresis and liquid chromatography-based separation, enabling cardiovascular researchers, in clinical as well as in basic research, to obtain results with high quality regardless of their current experience in the analysis of proteomes. The review begins with an introduction to high-resolution 2-dimensional electrophoresis and liquid chromatography and their capabilities. The biological role and analysis of protein heterogeneity is discussed in detail because 2-dimensional electrophoresis is especially suitable for visualizing distinct forms of proteins at high throughput and is still the method of choice for quantitative comparison of intact proteins. It continues with an overview of what can be expected from in-depth proteome analyses in terms of proteome coverage, leading to a discussion on the major challenges in proteomics, such as the tremendous differences in the abundance of cardiovascular proteins. The latter includes suggestions for tackling difficulties encountered even by experts, for example, during detection. A particular focus is provided on the current trend of subproteome analyses, which encompasses studies from whole organelles down to single protein complexes. Often underrepresented, a brief introduction to software analysis and content management is given. The review concludes with a future perspective on nondenaturing proteomics, which will provide most accurate insights reflecting the physiological role of proteins, as first steps in this direction have demonstrated. In summary, the review explains concepts and discusses approaches, which are fundamental for gaining highly competitive results via high-resolution separation of cardiovascular proteomes. Although DNA and RNA provide inherited templates for life, proteins are the effectors, defining structure and function from the molecular level to all that is visible and physically …
DomainChroma: Providing Optimal Countermeasures against Malicious Domain Names

Domain names and domain name system (DNS) have been used and abused for over 30 years since the 1980s. Although legitimate Internet users rely on domain names as their indispensable infrastructures for using the Internet, attackers use or abuse them as reliable, instantaneous, and distributed attack infrastructure. However, there is a lack of complete understanding of such domain name abuses and the methods for coping with them. In this paper, we design and implement a unified and objective analysis pipeline combining the existing defense solutions to realize practical and optimal defenses against today's malicious domain names. The basic concept underlying our novel analytical approach is malicious domain names' chromatography. Our new analysis pipeline can distinguish among mixtures of malicious domain names for websites. On the basis of this concept, we do not create a hodgepodge of existing solutions but design separation of abused domain names and offer defense information by considering the characteristics of malicious domain names as well as the possible defense solutions and points of defense. Finally, we evaluate our analysis pipeline and output defense information using a large and real dataset to show the effectiveness and validity of our proposed approach.
Modeling intra-speaker variability for speaker recognition

In this paper we present a speaker recognition algorithm that models explicitly intra-speaker inter-session variability. Such variability may be caused by changing speaker characteristics (mood, fatigue, etc.), channel variability or noise variability. We define a session-space in which each session (either train or test session) is a vector. We then calculate a rotation of the session-space for which the estimated intra-speaker subspace is isolated and can be modeled explicitly. We evaluated our technique on the NIST-2004 speaker recognition evaluation corpus, and compared it to a GMM baseline system. Results indicate significant reduction in error rate.
The MATE meta-scheme for coreference in dialogues in multiple languages

objects such as events, actions and propositions can all serve as antecedents of anaphoric expressions. We are not aware of any reliability results for this type of annotation, but the element can be used to identify the antecedents in this type of anaphora. If desired, the annotator could use a second attribute TYPE to specify the type of object introduced by the element; TYPE would have values EVENT, PROP and ACTION. (19) a. The 23-year-old had h, it his head against another player during a game of Aussie-rules football. McGlinn remembered nothing of the collision, but developed a headache and had several seizures. (BBC) b. The 23-year-old had hit his head against another player during a game of Aussie-rules football. McGlinn remembered nothing of the collision , but developed a headache and had several
Association of Progressive CD4+ T Cell Decline in SIV Infection with the Induction of Autoreactive Antibodies

The progressive decline of CD4+ T cells is a hallmark of disease progression in human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV) infection. Whereas the acute phase of the infection is dominated by virus-mediated depletion of memory CD4+ T cells, chronic infection is often associated with a progressive decline of total CD4+ T cells, including the naïve subset. The mechanism of this second phase of CD4+ T cell loss is unclear and may include immune activation–induced cell death, immune-mediated destruction, and regenerative or homeostatic failure. We studied patterns of CD4+ T cell subset depletion in blood and tissues in a group of 20 rhesus macaques inoculated with derivatives of the pathogenic SIVsmE543-3 or SIVmac239. Phenotypic analysis of CD4+ T cells demonstrated two patterns of CD4+ T cell depletion, primarily affecting either naïve or memory CD4+ T cells. Progressive decline of total CD4+ T cells was observed only in macaques with naïve CD4+ T cell depletion (ND), though the depletion of memory CD4+ T cells was profound in macaques with memory CD4+ T cell depletion (MD). ND macaques exhibited lower viral load and higher SIV-specific antibody responses and greater B cell activation than MD macaques. Depletion of naïve CD4+ T cells was associated with plasma antibodies autoreactive with CD4+ T cells, increasing numbers of IgG-coated CD4+ T cells, and increased incidence of autoreactive antibodies to platelets (GPIIIa), dsDNA, and phospholipid (aPL). Consistent with a biological role of these antibodies, these latter antibodies were accompanied by clinical features associated with autoimmune disorders, thrombocytopenia, and catastrophic thrombotic events. More importantly for AIDS pathogenesis, the level of autoreactive antibodies significantly correlated with the extent of naïve CD4+ T cell depletion. These results suggest an important role of autoreactive antibodies in the CD4+ T cell decline observed during progression to AIDS.
Enhancement of Associative Long-Term Potentiation by Activation of β-Adrenergic Receptors at CA1 Synapses in Rat Hippocampal Slices

The objective of this study was to evaluate the role of β-adrenergic receptors in modulating associative long-term potentiation (LTP) induced at CA1 synapses. Two independent Schaffer collateral pathways were stimulated in hippocampal slices. The field EPSP (fEPSP) response evoked in one pathway (the weak pathway) was small, whereas a large response, usually 80–90% of the maximum, was evoked in the strong pathway. After recording of the baseline fEPSP evoked at 0.033 Hz, LTP of the weak pathway could be associatively induced by paired stimulation of the weak and strong pathways 100 times at 6 sec intervals, with stimulation of the weak pathway preceded 3–10 msec. However, pairing protocols with an interval between stimulation of the two pathways >10 msec resulted in no LTP. The induced LTP was NMDA receptor dependent, because 50 μm d,l-APV blocked its induction. Bath application of 1 μm isoproterenol enhanced LTP by increasing the window of the stimulation interval up to 15 msec but did not affect the magnitude of the LTP induced by pairing protocols with intervals <10 msec. Similar results were obtained when the experiments were repeated using whole-cell recording. These results suggest that activation of β-adrenergic receptors can enhance associative LTP by increasing the width of the time window rather than the magnitude of the LTP. Enhancement of LTP by β-adrenergic receptors was blocked in slices by pretreatment with inhibitors of protein kinase A or mitogen-activated protein kinase, suggesting that these signaling cascades are involved in this process.
Code Optimisation Techniques for Lazy Functional Languages

This thesis describes a complete compiler back-end for lazy functional languages, which uses various interprocedural optimisations to produce highly optimised code. The most important contributions of this work are the following. A novel intermediate language, called GRIN (Graph Reduction Intermediate Notation), around which the first part of the back-end is built. The GRIN language has a very "functional flavour", making it well suited for analysis and program transformation, but at the same time provides the "low level" machinery needed to express many concrete implementation details. We apply a program-wide control flow analysis, also called a heap points-to analysis, to the GRIN program. The result of the analysis is used to eliminate unknown control flow in the program, i.e., function calls where the call target is unknown at compile time (due to forcing of closures). We present a large number of GRIN source-to-source program transformations that are applied to the program. Most transformations are very simple but when taken together, and applied repeatedly, produce greatly simplified and optimised code. Several non-standard transformations are made possible by the low level control offered by the GRIN language (when compared to a more high level intermediate language, e.g., the STG language). Eventually, the GRIN code is translated into RISC machine code. We develop an interprocedural register allocation algorithm, with a main purpose of decreasing the function call and return overhead. The register allocation algorithm includes a new technique to optimise the placement of register save and restore instructions, related to Chow's shrink-wrapping, and extends traditional Chaitin-style graph colouring with interprocedural coalescing and a restricted form of live range splitting. The algorithm produces a tailor-made calling convention for each function (the registers used to pass function arguments and results). A combined compile time and runtime approach is used to support garbage collection in the presence of aggressive optimisations (most notably our register allocation), without imposing any mutator overhead. The method includes a runtime call stack traversal and interpretation of registers and stack frames using pre-computed descriptor tables. Experiments sofar have been very promising. For a set of small to medium-sized Haskell programs taken from the nofib benchmark suite, the code produced by our back-end executes several times faster than the code produced by some other compilers (ghc and hbc).
PyNEST: A Convenient Interface to the NEST Simulator

The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 104 neurons and 107 to 109 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST's efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST's native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used.