Miracle-dz/UniFAR-FTUs huggingface dataset:
Rat serum contains a developmentally regulated cholinergic inducing activity

Sympathetic neurons cultured in defined medium do not develop the ability to produce acetylcholine, as do neurons grown with serum supplementation (lacovitti, L., M. I. Johnson, T. H. Joh, and R. P. Bunge (1982) Neuroscience 7:2225–2239; Wolinsky, E. J., S. C. Landis, and P. H. Patterson (1985) J. Neurosci. 5: 1497–1508). The implication that rat serum contains cholinergic inducing activity is further explored here. Dependence of cholinergic induction on serum concentration is demonstrated, and the activity is shown to reside in a macromolecular fraction. Very little cholinergic inducing activity is present in serum obtained from animals younger than 9 postnatal days. This age dependence correlates with the time of transition from noradrenergic to cholinergic transmitter status by the sympathetic innervation of the rat sweat gland in vivo (Landis, S. C., and D. Keefe (1983) Dev. Biol. 98: 349–372).
SD-HDC: Software-Defined Hybrid Optical/Electrical Data Center Architecture

In order to dynamically assign the optical/electrical routing path and optimize the optical/electrical resource allocation, a software-defined hybrid optical/electrical data center architecture (SD-HDC for short) is proposed in this work. A control mechanism that consists of two parallel steps including the new request handle step and the hybrid resource optimize step is introduced in SD-HDC. The new request handle step is used to quickly process the new incoming request while the hybrid resource optimize step periodically optimizes the hybrid optical/electrical network resource. To validate the SD-HDC architecture, a hybrid optical/electical routing algorithm used for handling elephant/mice flows is also proposed in this paper. Experimental results show that the proposed SD- HDC architecture can effectively reduce the blocking probability and path provisioning latency.
One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms

The segmentation of the breast from the background and the pectoral muscle is the first pre-processing step in computerised mammographic analysis. This problem is usually solved by dividing it into two different segmentation strategies, one for the background and another one for the pectoral muscle. In this paper we tackle this problem jointly using a supervised single strategy. Namely, from a set of manually segmented mammograms, we model each of the three regions (breast, pectoral muscle, and background) using position, intensity, and texture information. Although the approach requires a training step, it allows a fast and reliable segmentation of new mammograms. The obtained results using 149 mammograms of the MIAS database show a high degree of overlap between manual and automatic segmentation.
Efficient SMT solving for bit vectors and the extensional theory of arrays

The Satisfiability Modulo Theories (SMT) problem is to decide the satisfiability of a formula expressed in a (decidable) first-order background theory. In this thesis we address the problem of designing, implementing, testing, and debugging an efficient SMT solver for the quantifier-free extensional theory of arrays, combined with bit-vectors. This thesis consists of three main parts. After an introduction into the problem and its domain, the first part considers the design of an efficient decision procedure for the quantifier-free extensional theory of arrays. We discuss the decision procedure in detail. In particular, we prove correctness, provide a complexity analysis, and discuss implementation and optimization details. The second part focuses on the design and implementation details of our SMT solver, called Boolector. In the SMT competitions 2008 and 2009, Boolector clearly won the division of the quantifier-free theory of bit-vectors, arrays and uninterpreted functions QF AUFBV. Moreover, it won the division of the quantifier-free theory of bit-vectors QF BV in 2008 and achieved the second place in 2009. We discuss selected optimization techniques and features such as symbolic overflow detection, propagating unconstrained variables, and under-approximation techniques for bit-vectors. In the last part, this thesis addresses engineering aspects such as testing and debugging techniques optimized for SMT solver development. We show that fuzzing and debugging techniques can be successfully applied to SMT solvers. We report on critical defects, such as segmentation faults and incorrect results, which we have found for state-of-the-art SMT solvers. Finally, we demonstrate the effectiveness of delta-debugging techniques in minimizing failure-inducing SMT formulas.
Marine envenomations. Part 1--Jellyfish.

BACKGROUND Many venomous marine creatures inhabit Australian waters, causing significant morbidity and occasional fatalities. Part 1 of this article looks at jellyfish envenomations, an important overall cause of marine injuries. OBJECTIVE To discuss the features of envenomation by some of the more commonly encountered jellyfish of medical significance, and the recommended first aid and medical management of such envenomations. DISCUSSION It is intended that the information contained in this article will be informative to general practitioners dealing with jellyfish stings throughout Australia. Much of what we know has come from the astute observations of GPs in tropical Australia. However, there remains a lot to be learnt about jellyfish envenomation.
Feature-level image sequence fusion based on histograms of Oriented Gradients

For improving accuracy and robust property of human detection, fusion of image sequences captured from visible-thermal sensors is lucrative. Instead of performing it in pixel-level directly, we try to fuse object features by a novel image sequence fusion algorithm based on gradient feature (GFIF). The GFIF algorithm calculate gradients of input images to form a joint histograms of Oriented Gradient (HOG) descriptor, and these fused features are used to train linear support vector machine (SVM). In our experiment, a color-thermal surveillance dataset is adopted and several multi-resolution fusion algorithms are tested for comparing. By the Detection Error Tradeoff (DET) curves, the GFIF algorithm shows its superiority.
Regulation of mRNA cap methylation

The 7-methylguanosine cap added to the 5′ end of mRNA is essential for efficient gene expression and cell viability. Methylation of the guanosine cap is necessary for the translation of most cellular mRNAs in all eukaryotic organisms in which it has been investigated. In some experimental systems, cap methylation has also been demonstrated to promote transcription, splicing, polyadenylation and nuclear export of mRNA. The present review discusses how the 7-methylguanosine cap is synthesized by cellular enzymes, the impact that the 7-methylguanosine cap has on biological processes, and how the mRNA cap methylation reaction is regulated.
On the efficiency of adaptive MCMC algorithms

We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an "optimal" target process via a learning procedure. We show, under appropriate conditions, that the adaptive process and "optimal" (nonadaptive) MCMC algorithm share identical asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is considered in details and we apply our results to the adaptive Metropolis algorithm of [1]. We also propose a new class of adaptive MCMC algorithms, called quasi-perfect adaptive MCMC which possesses appealing theoretical and practical properties, as demonstrated through numerical simulations.
Büchi Complementation Made Tighter

The complementation problem for nondeterministic word automata has numerous applications in formal verification. In particular, the language-containment problem, to which many verification problems is reduced, involves complementation. For automata on finite words, which correspond to safety properties, complementation involves determinization. The 2n blow-up that is caused by the subset construction is justified by a tight lower bound. For Buchi automata on infinite words, which are required for the modeling of liveness properties, optimal complementation constructions are quite complicated, as the subset construction is not sufficient. From a theoretical point of view, the problem is considered solved since 1988, when Safra came up with a determinization construction for Buchi automata, leading to a 2O(n log n) complementation construction, and Michel came up with a matching lower bound. A careful analysis, however, of the exact blow-up in Safra's and Michel's bounds reveals an exponential gap in the constants hiding in the O( ) notations: while the upper bound on the number of states in Safra's complementary automaton is n2n, Michel's lower bound involves only an n! blow up, which is roughly (n/e)n. The exponential gap exists also in more recent complementation constructions. In particular, the upper bound on the number of states in the complementation construction of Kupferman and Vardi, which avoids determinization, is (6n)n. This is in contrast with the case of automata on finite words, where the upper and lower bounds coincides. In this work we describe an improved complementation construction for nondeterministic Buchi automata and analyze its complexity. We show that the new construction results in an automaton with at most (0.96n)n states. While this leaves the problem about the exact blow up open, the gap is now exponentially smaller. From a practical point of view, our solution enjoys the simplicity of the construction of Kupferman and Vardi, and results in much smaller automata.
Catatonia in Patients with Dementia Admitted to a Geriatric Psychiatry Ward

ABSTRACT To determine the frequency and characteristics of catatonia in persons with dementia, all patients admitted to an old age psychiatry ward were screened for catatonia with the Bush-Francis Catatonia Screening Instrument (BFCSI) over a period of 6 months. Thirty-seven patients were admitted, and there were 14 patients with dementia. The rate of catatonia in these patients was 42.8% (6 out of 14). Five out of six of these patients received treatment for catatonia with lorazepam, all achieving complete remission. Catatonia occurred frequently in patients admitted with dementia.
Transduced PEP-1-Heme Oxygenase-1 Fusion Protein Reduces Remote Organ Injury Induced by Intestinal Ischemia/Reperfusion

Background A fusion protein composed of heme oxygenase-1 (HO-1) and cell-penetrating peptide PEP-1 has been shown to reduce local intestinal injury after intestinal ischemia/reperfusion (I/R). In this study, we investigated the effects of PEP-1-HO-1 fusion protein on remote organ injury induced by intestinal I/R in rats. Material/methods We randomly assigned 24 male Sprague-Dawley rats to 3 groups: Sham, I/R, and I/R plus PEP-1-HO-1 treatment (HO). The model of intestinal I/R was established by occluding the superior mesenteric artery for 45 min followed by 120-min reperfusion. In HO group, PEP-1-HO-1 was administered intravenously 30 min before ischemia, while animals in the Sham and I/R groups received the equal volume of physiological saline. At the end of the experiment, lung, liver, and blood samples were collected and analyzed. Results Malondialdehyde levels and histological injury scores were increased, and superoxide dismutase activities were decreased in the lung and liver tissues in the I/R group compared with the Sham group (P<0.05). Serum levels of alanine aminotransferase, aspartate aminotransferase, tumor necrosis factor-α, interleukin-6, and lung tissue wet weight to dry weight ratio were increased in the I/R group compared with the Sham group (P<0.05). NF-κB expression in intestinal tissues was significantly higher in the I/R group than in the Sham group. These changes were significantly reversed by treatment with PEP-1-HO-1. Conclusions This study demonstrates that administration of PEP-1-HO-1 has a protective role against lung and liver injury after intestinal I/R, attributable to the reduction of released proinflammatory cytokines regulated by NF-κB.
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization

Structured output learning has been successfully applied to object localization, where the mapping between an image and an object bounding box can be well captured. Its extension to action localization in videos, however, is much more challenging, because we need to predict the locations of the action patterns both spatially and temporally, i.e., identifying a sequence of bounding boxes that track the action in video. The problem becomes intractable due to the exponentially large size of the structured video space where actions could occur. We propose a novel structured learning approach for spatio-temporal action localization. The mapping between a video and a spatio-temporal action trajectory is learned. The intractable inference and learning problems are addressed by leveraging an efficient Max-Path search method, thus making it feasible to optimize the model over the whole structured space. Experiments on two challenging benchmark datasets show that our proposed method outperforms the state-of-the-art methods.
Towards a Logic-programming System to Debug ASP Knowledge Bases

This paper is a characterisation in terms of Weak Constraints both for Minimal Generalised Answer Sets and Optimal Answer Sets, of an updates semantics that provides a solid foundation for the implementation of a system to debug knowledge bases. The proposed system can be employed both to identify conﬂicts with upcoming information from a dynamic changing environment, and to locate the source of conﬂict from a given inherent inconsistent knowledge base that is static. In addition, this work introduces some formal speciﬁcations towards the implementation of a debugging solver with no need to use a total-order semantics. Accordingly, the two-fold contribution of this work is to show a simpler theoretical framework of preferences characterised in DLV , as well as general foundation to debug both static and dynamic logic programs.
The impact of agile approaches on software quality attributes an empirical study

Agile software development describes those software systems which undergo rapid changes as a result of the testing and requirements fulfillment processes. This development technique came into view in order to overcome the drawbacks of long software life cycles of traditional development methods. This paper investigates the effects of agile practices on the quality of the produced software systems. We have used 20 open and closed source systems of various sizes and functionalities. While the development process of 9 of the studied systems followed agile approaches, the rest were developed using traditional approaches. Firstly, a set of software metrics is generated to describe each system. The metrics encompass complexity and inheritance characteristics of the studied systems. Secondly, the generated metrics are used as predictors of the type of the followed development process using binary logistic regression. The obtained high goodness-of-fit measures show the strong relationship between the used metrics and the type of the followed development process. More specifically, the study reveals that following agile practices has a great impact on lack of cohesion of methods, fan in and maximum depth of inheritance tree.
Central Auditory Plasticity: Changes in the N1-P2 Complex after Speech-Sound Training

Objective To determine whether the N1-P2 complex reflects training-induced changes in neural activity associated with improved voice-onset-time (VOT) perception. Design Auditory cortical evoked potentials N1 and P2 were obtained from 10 normal-hearing young adults in response to two synthetic speech variants of the syllable /ba. / Using a repeated measures design, subjects were tested before and after training both behaviorally and neurophysiologically to determine whether there were training-related changes. In between pre- and post-testing sessions, subjects were trained to distinguish the −20 and −10 msec VOT /ba/ syllables as being different from each other. Two stimulus presentation rates were used during electrophysiologic testing (390 msec and 910 msec interstimulus interval). Results Before training, subjects perceived both the −20 msec and −10 msec VOT stimuli as /ba. / Through training, subjects learned to identify the −20 msec VOT stimulus as “mba” and −10 msec VOT stimulus as “ba. ” As subjects learned to correctly identify the difference between the −20 msec and −10 msec VOT syllabi, an increase in N1-P2 peak-to-peak amplitude was observed. The effects of training were most obvious at the slower stimulus presentation rate. Conclusions As perception improved, N1-P2 amplitude increased. These changes in waveform morphology are thought to reflect increases in neural synchrony as well as strengthened neural connections associated with improved speech perception. These findings suggest that the N1-P2 complex may have clinical applications as an objective physiologic correlate of speech-sound representation associated with speech-sound training.
Improving Signal Strength in Wireless Mobile Communication using Diversity Combining Technique over Rayleigh Fading Channels

The error probability floor evolves due to performance loss, thus there is a requirement of high energy and high bandwidth to get a desired link. The branches are assumed to be Rayleigh fading. With the increase number of receiver the signal to noise ratio to be improved and bit error rate of all branches are compared. This work confirms the benefit of choosing 2x2MIMO system instead of 1 transmits 2 receive MRC case.
A Methodology for Discovering how to Adaptively Personalize to Users using Experimental Comparisons

We explain and provide examples of a formalism that supports the methodology of discovering how to adapt and personalize technology by combining randomized experiments with variables associated with user models. We characterize a formal relationship between the use of technology to conduct A/B experiments and use of technology for adaptive personalization. The MOOClet Formalism captures the equivalence between experimentation and personalization in its conceptualization of modular components of a technology. This motivates a unified software design pattern that enables technology components that can be compared in an experiment to also be adapted based on contextual data, or personalized based on user characteristics. With the aid of a concrete use case, we illustrate the potential of the MOOClet formalism for a methodology that uses randomized experiments of alternative micro-designs to discover how to adapt technology based on user characteristics, and then dynamically implements these personalized improvements in real time.
Iterative Graph Feature Mining for Graph Indexing

Sub graph search is a popular query scenario on graph databases. Given a query graph q, the sub graph search algorithm returns all database graphs having q as a sub graph. To efficiently implement a subgraph search, subgraph features are mined in order to index the graph database. Many subgraph feature mining approaches have been proposed. They are all "mine-at-once" algorithms in which the whole feature set is mined in one run before building a stable graph index. However, due to the change of environments (such as an update of the graph database and the increase of available memory), the index needs to be updated to accommodate such changes. Most of the "mine-at-once" algorithms involve frequent subgraph or subtree mining over the whole graph database. Also, constructing and deploying a new index involves an expensive disk operation such that it is inefficient to re-mine the features and rebuild the index from scratch. We observe that, under most cases, it is sufficient to update a small part of the graph index. Here we propose an "iterative subgraph mining" algorithm which iteratively finds one feature to insert into (or remove from) the index. Since the majority of indexing features and the index structure are not changed, the algorithm can be frequently invoked. We define an objective function that guides the feature mining. Next, we propose a basic branch and bound algorithm to mine the features. Finally, we design an advanced search algorithm, which quickly finds a near-optimum subgraph feature and reduces the search space. Experiments show that our feature mining algorithm is 5 times faster than the popular graph indexing algorithm gIndex, and that features mined by our iterative algorithm have a better filtering rate for the subgraph search problem.
Clarifications on the integration path of transient energy function

In transient stability analysis, a nonintegrable term is present in the energy function of multimachine power system with transfer conductance. There are two approximation methods, linear path method and trapezoidal approximation method, in calculating the energy numerically. It is proved that there is a special point in the orbit of a point in the stability region, where angular coordinates are equal to, or infinitely close to, those of the stable equilibrium point. Integration path for a point in the stability region is the trajectory from the special point to it. Present assumption on the starting point of the integration path is modified. Linear path method takes a linear approximation of the correct path, while trapezoidal approximation calculates along the fault-on trajectory, which is different from the correct path. Thus the latter method is not preferable in calculation.
Testing for Independence and Lrt for VAD

In this paper we propose a simple method for Voice Activity Detection (VAD) in noisy environments based on periodogram of squares. The approach is based on a test for independence between a sequence of identically distributed random variables using the classical technique of Likelihood Ratio Test (LRT). This algorithm differs from many others in the way the decision rule is formulated (detection tests) and the domain used in this approach. Clear improvements in the speed of speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It has been shown that application of statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The experimental analysis carried out on the AURORA databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR and ETSI AFE f.