Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Extraction in the Image Recognition for Plateau Pikas (Ochotona curzoniae)

The conventional methods for pika observation and investigation are costly both in material resources and manpower, moreover these approaches are difficult to use in long-term and continuous investigation. In this context, a plateau pikas monitoring scheme based on image processing technology was described, and the steps of pika identification is presented. Furthermore, the most challenging issue of pika identification, feature extraction was analyzed in detail. Based on the analysis, the image graying method was utilized to avoid redundance and compress data capacity, and image binarization technology was utilized to eliminate the background noise. Moreover, in order to effectively extract the features, four edge detectors: Sobel, Prewitt, Roberts and Canny were chosen for evaluation, and among these algorithms Roberts detector were found has better performance in the application. At last, median filtering method was utilized in image smoothing for spiky noise elimination and sharp edges preservation. The experiment results showed that the pika detection method based on image processing has the advantages to achieve dynamic and continuous monitor of pika in close quarters, and significantly improved the technique in meadow biogeocenose protection.
Random networks with sublinear preferential attachment: Degree evolutions

We define a dynamic model of random networks, where new vertices are connected to old ones with a probability proportional to a sublinear function of their degree. We first give a strong limit law for the empirical degree distribution, and then have a closer look at the temporal evolution of the degrees of individual vertices, which we describe in terms of large and moderate deviation principles. Using these results, we expose an interesting phase transition: in cases of strong preference of large degrees, eventually a single vertex emerges forever as vertex of maximal degree, whereas in cases of weak preference, the vertex of maximal degree is changing infinitely often. Loosely speaking, the transition between the two phases occurs in the case when a new edge is attached to an existing vertex with a probability proportional to the root of its current degree.
Novel segmentation for twig problem by adding prior nodes in Random-Walk algorithm

In object identification image Segmentation is the first step in digital image processing. It can be used to compress different segments or areas of image. A novel sub-Markov Random Walk (subRW) algorithm with label prior is proposed for seeded image segmentation. It is traditional random walker on a graph with added auxiliary nodes. The uniqueness will be nothing but adding or changing the auxiliary nodes in segmentation algorithm. We face segmentation problem in existing system if the image having very thin and elongated parts. we design a new sub RW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts (i.e Twig Problem). The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous Well Known RW Algorithms for segmentation.
A variable reduction technique for the analysis of ultra large-scale power distribution networks

In the trends of high density high speed and low power consumption, the analysis of power distribution networks is rapidly becoming an essential step in the design of high performance ICs. Nevertheless, the analysis is a great challenge, due to the large size of the networks. In this paper, we propose a variable reduction technique for the analysis of large-scale power distribution networks. The basic procedure of the proposed technique is reducing the power distribution network to a manageable size, solving the equation of the reduced network and then recovering the solutions of the original network. With the proposed variable reduction technique, we have achieved speed improvements up to several tens of times compared to the high performance linear system solution techniques, with no loss of accuracy.
Transforming Exploratory Creativity with DeLeNoX,

We introduce DeLeNoX (Deep Learning Novelty Explorer), a system that autonomously creates artifacts in constrained spaces according to its own evolving interestingness criterion. DeLeNoX proceeds in alternating phases of exploration and transformation. In the exploration phases, a version of novelty search augmented with constraint handling searches for maximally diverse artifacts using a given distance function. In the transformation phases, a deep learning autoencoder learns to compress the variation between the found artifacts into a lower-dimensional space. The newly trained encoder is then used as the basis for a new distance function, transforming the criteria for the next exploration phase. In the current paper, we apply DeLeNoX to the creation of spaceships suitable for use in two-dimensional arcade-style computer games, a representative problem in procedural content generation in games. We also situate DeLeNoX in relation to the distinction between exploratory and transformational creativity, and in relation to Schmidhuber’s theory of creativity through the drive for compression progress.
A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance

Abstract The use of fold-change (FC) to prioritize differentially expressed genes (DEGs) for post-hoc characterization is a common technique in the analysis of RNA sequencing datasets. However, the use of FC can overlook certain population of DEGs, such as high copy number transcripts which undergo metabolically expensive changes in expression yet fail to exceed the ratiometric FC cut-off, thereby missing potential important biological information. Here we evaluate an alternative approach to prioritizing RNAseq data based on absolute changes in normalized transcript counts (ΔT) between control and treatment conditions. In five pairwise comparisons with a wide range of effect sizes, rank-ordering of DEGs based on the magnitude of ΔT produced a power curve-like distribution, in which 4.7–5.0% of transcripts were responsible for 36–50% of the cumulative change. Thus, differential gene expression is characterized by the high production-cost expression of a small number of genes (large ΔT genes), while the differential expression of the majority of genes involves a much smaller metabolic investment by the cell. To determine whether the large ΔT datasets are representative of coordinated changes in the transcriptional program, we evaluated large ΔT genes for enrichment of gene ontologies (GOs) and predicted protein interactions. In comparison to randomly selected DEGs, the large ΔT transcripts were significantly enriched for both GOs and predicted protein interactions. Furthermore, enrichments were were consistent with the biological context of each comparison yet distinct from those produced using equal-sized populations of large FC genes, indicating that the large ΔT genes represent an orthagonal transcriptional response. Finally, the composition of the large ΔT gene sets were unique to each pairwise comparison, indicating that they represent coherent and context-specific responses to biological conditions rather than the non-specific upregulation of a family of genes. These findings suggest that the large ΔT genes are not a product of random or stochastic phenomenon, but rather represent biologically meaningful changes in the transcriptional program. They furthermore imply that high abundance transcripts are associated with particularly cellular states, and as cells change in response to internal or external conditions, the relative distribution of the abundant transcripts changes accordingly. Thus, prioritization of DEGs based on the concept of metabolic cost is a simple yet powerful method to identify biologically important transcriptional changes and provide novel insights into cellular behaviors.
Long Circulating Lectin Conjugated Paclitaxel Loaded Magnetic Nanoparticles: A New Theranostic Avenue for Leukemia Therapy

Amongst all leukemias, Bcr-Abl positive chronic myelogenous leukemia (CML) confers resistance to native drug due to multi drug resistance and also resistance to p53 and fas ligand pathways. In the present study, we have investigated the efficacy of microtubule stabilizing paclitaxel loaded magnetic nanoparticles (pac-MNPs) to ascertain its cytotoxic effect on Bcr-Abl positive K562 cells. For active targeted therapy, pac-MNPs were functionalized with lectin glycoprotein which resulted in higher cellular uptake and lower IC50 value suggesting the efficacy of targeted delivery of paclitaxel. Both pac-MNPs and lectin conjugated pac-MNPs have a prolonged circulation time in serum suggesting increased bioavailability and therapeutics index of paclitaxel in vivo. Further, the molecular mechanism pertaining to pac-induced cytotoxicity was analyzed by studying the involvement of different apoptotic pathway proteins by immunoblotting and quantitative PCR. Our study revealed simultaneous activation of JNK pathway leading to Bcr-Abl instability and the extrinsic apoptotic pathway after pac-MNPs treatment in two Bcr-Abl positive cell lines. In addition, the MRI data suggested the potential application of MNPs as imaging agent. Thus our in vitro and in vivo results strongly suggested the pac-MNPs as a future prospective theranostic tool for leukemia therapy.
Fundamental decarburisation model of AOD process

A mathematical fluid flow model of gas injection in an argon–oxygen decarburisation (AOD) converter process has been coupled with a high temperature thermodynamic model. The current model is a further enhancement of an earlier developed three-dimensional, three-phase model, to also include some thermodynamics of the process. The model is based on fundamental transport equations and includes separate solutions for the steel, slag and the gas phases and their coupling by friction. The AOD model has been used to predict the first injection stage of decarburisation in an AOD converter. The predictions have been found to agree well with the corresponding results from an industrial process control model. One of the important observations from the simulations was that large concentration gradients of carbon exist in the AOD at an early stage and as the first injection step approaching its end the carbon gradients diminish. Also, the results show, in accordance with theory, that the local decarburisation rate is decreased at elevated pressures.
Content and buffer aware scheduling for video delivery over LTE

We propose a content and buffer aware Long Term Evolution (LTE) downlink scheduler for allocating radio resources to multiple users requesting Scalable Video Coding (SVC) encoded videos from a server. The scheduler adaptively controls the video quality by assigning physical resource blocks (PRBs) to the users based on the requested video type, device capability, channel quality, and buffer state in the user equipment (UE). The scheduler maximizes the average video quality across all users for a limited number of PRBs, and can be installed in existing LTE systems without additional equipment and very limited signaling overhead.
Inference by Coincidence and the Extraction of Propositional Information from Text

s 2 FMRI signal when people generate causal inferences during stories. Although the majority of psycholinguistic research concerns how people process words or sentences, the bulk of human communication occurs at the level of discourse. Some language comprehension processes may be far more evident as people comprehend complex discourse than when they comprehend words or sentences. For instance, when people comprehend stories, they generate causal inferences as necessary to fill coherence gaps. Studies with neurological patients and with normal participants demonstrating inference priming of lateralized target words suggest that multiple, bilateral, components of semantic processing are necessary for optimally drawing coherence inferences. I will discuss evidence of such processing f rom several methodologies (brain damage, visual field studies with healthy subjects, and neuroimaging) and types of stimuli (multi-word prime-target pairs, stories that promote inferences, and insight-like verbal problems). Results are interpreted in a framework positing bilateral activation, integration, and selection of semantic representations, each hemisphere performing slightly different computations within each semantic process. Specifically, I will argue that the left hemisphere performs relatively f iner coding of semantic information, which is extremely efficient for most straightforward language comprehension; whereas the right hemisphere performs relatively coarser coding of semantic information, which is especially useful when people understand certain discourse constructions and when semantic integration is difficult, and adds richness to discourse representations generally. The ability to extract propositional information from text is a fundamental prerequisite for models of sentence processing and text comprehension. That people encode sentences in something like propositions, and not simply as lists of words, is well established (see Kintsch 1998 for a summary of the evidence). However, the exact nature of these representations and how the cognitive system goes about the task of transforming a string of words into these propositional representations remains an open question. Current systems require substantive designer intervention to build grammars and conceptual schemas that specify the way in which information is structured (e.g. Lenat 1995). However, as yet no generic account of how these templates could be acquired by people has emerged. The Syntagmatic Paradigmatic (SP) model offers a solution to this problem. Like approaches such as Latent Semantic Analysis (LSA, Landauer & Dumais 1997), the SP model is completely data driven. Rather than propose pre-existing syntactic and conceptual rules, the SP model presumes that people store large numbers of sentence traces. Processing a sentence then becomes a matter of retrieving appropriate …
Lie algebras of smooth sections

Lie algebras of smooth sections are Lie algebras obtained from bundles of Lie algebras, where the latter are vector bundles of which the fibers are Lie algebras. We also consider the C-sections for k ∈ N. This paper, which is essentially my diploma thesis from May 2007 at Technische Universität Darmstadt, studies the derivations, the centroid and the isomorphisms of such Lie algebras and generalizes some facts from Lecomte’s publications [Le79] and [Le80] to the case where the fiber is perfect or centerfree and it gives some more explicit proofs.
Etna: A Fault-tolerant Algorithm for Atomic Mutable DHT Data

This paper presents Etna, an algorithm for atomic reads and writes of replicated data stored in a distributed hash table. Etna correctly handles dynamically changing sets of replica hosts, and is optimized for reads, writes, and reconfiguration, in that order. Etna maintains a series of replica configurations as nodes in the system change, using new sets of replicas from the pool supplied by the distributed hash table system. It uses the Paxos protocol to ensure consensus on the members of each new configuration. For simplicity and performance, Etna serializes all reads and writes through a primary during the lifetime of each configuration. As a result, Etna completes read and write operations in only a single round from the primary. Experiments in an environment with high network delays show that Etna’s read latency is determined by round-trip delay in the underlying network, while write and reconfiguration latency is determined by the transmission time required to send data to each replica. Etna’s write latency is about the same as that of a non-atomic replicating DHT, and Etna’s read latency is about twice that of a non-atomic DHT due to Etna assembling a quorum for every read.
Annealing effects on the elastic modulus of tungsten oxide nanowires

Three-point bend test coupled with transmission electron microscopy (TEM) analysis was carried out on individual tungsten oxide nanowires (NWs) before and after annealing. Three-point bend test monitors the change in the Young’s modulus of the NW after annealing, while TEM provides nanostructural detail changes on the same NW. In this way, insight into the correlation between the mechanical properties of a NW and its nanostructure details can be obtained. Annealing increased the diameter of the NWs by forming a uniform amorphous/polycrystalline outer coating. The coating results in a decrease in Young’s moduli for thicker NWs. On the other hand, annealing led to increased Young’s moduli of thinner NWs, which is attributed to the improved crystallinity in these NWs after annealing. This study points to a more refined strategy for more in-depth understanding of the relationship between the nanostructures and elastic mechanical properties of NWs.
The SocioBox: A Novel Paradigm to Assess Complex Social Recognition in Male Mice

Impairments in social skills are central to mental disease, and developing tools for their assessment in mouse models is essential. Here we present the SocioBox, a new behavioral paradigm to measure social recognition. Using this paradigm, we show that male wildtype mice of different strains can readily identify an unfamiliar mouse among 5 newly acquainted animals. In contrast, female mice exhibit lower locomotor activity during social exploration in the SocioBox compared to males and do not seem to discriminate between acquainted and unfamiliar mice, likely reflecting inherent differences in gender-specific territorial tasks. In addition to a simple quantification of social interaction time of mice grounded on predefined spatial zones (zone-based method), we developed a set of unbiased, data-driven analysis tools based on heat map representations and characterized by greater sensitivity. First proof-of-principle that the SocioBox allows diagnosis of social recognition deficits is provided using male PSD-95 heterozygous knockout mice, a mouse model related to psychiatric pathophysiology.
Mechanisms of progression in native coronary artery disease: role of healed plaque disruption

OBJECTIVE To determine the role of healed plaque disruption in the generation of chronic high grade coronary stenosis. METHODS Coronary arteries obtained at necropsy were perfuse fixed with formal saline for 24 hours at 100 mg Hg. The percentage lumen diameter stenosis was measured in each 3 mm segment containing a plaque, using the lumen size at the nearest histologically normal segment as the reference point. Each segment was prepared for histological examination and stained with Sirius red and immunohistochemistry for smooth muscle actin. Healed disruption was considered to be present when under polarised light there was a break in the yellow–white dense collagen of the cap filled in by more loosely arranged green collagen. Increased smooth muscle density in the green staining areas was required. Each section was read independently by two observers; any segment with discordant views was considered negative. MATERIAL 31 men aged 51–69 dying suddenly of ischaemic heart disease. 39 coronary arteries were studied containing 256 separate plaques, after excluding coronary arteries with old total occlusions, an acute culprit thrombotic lesion, diffuse disease without normal arterial segments, and arteries related to old myocardial scars. RESULTS 16 of 99 plaques causing < 20% diameter stenosis had prior disruption. In the 21–50% stenosis range 16 of 86 plaques showed healed disruption. Stenosis ⩾ 51% by diameter was present in 71 plaques, 52 of which showed a healed disruption pattern. The difference between stenosis < 50% and stenosis ⩾ 51% was significant by the χ2test (p < 0.001). CONCLUSIONS Subclinical episodes of plaque disruption followed by healing are a stimulus to plaque growth that occurs suddenly and is a major factor in causing chronic high grade coronary stenosis. This mechanism would explain the phasic rather than linear progression of coronary disease observed in angiograms carried out annually in patients with chronic ischaemic heart disease.
On the Damage Behavior of Fiber Reinforced Composite Materials

Since fiber reinforced composite materials have been used in main parts of structures, an accurate evaluation of their mechanical characteristics becomes very important. Due to their anisotropic nature and complicated architecture, it is very difficult to reveal the damage mechanism of these materials from the results of mechanical tests. Therefore, there is always a big need to conduct reliable simulations and analytical evaluations. In this paper, the damage behavior of FRP is simulated by finite element analysis using an anisotropic damage model based on damage mechanics. The proposed procedure is applied to two examples; the finite element analysis aided AE damage simulation and the finite element analysis of microscopic damage propagation in woven fabric composite materials. Experimental tests in both cases have been conducted to evaluate the validity of the proposed method. It is recognized that there is a good agreement between the analytical and experimental results, and that the proposed simulation method is very useful for the evaluation of damage mechanism.
Collaborative Filtering for Movie Recommendation using RapidMiner

Recommender System is a special type of information filtering system that provides a prediction which helps the user to evaluate items from a huge collection that the user is likely to find interesting or useful. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations facilitate the users to make decisions in multiple contexts, such as what items to buy, what online news to read or what music to listen to. Recommender Systems have become important in information and decision overloaded in the world. Recommender Systems helped their founders to increase profits. This paper, presents a brief overview of collaborative filtering based movie recommender system and their implementation using rapid miner.