Miracle-dz/UniFAR-FTUs huggingface dataset:
On Capacity Conservation in Wireless Networks

Spectrum allocation and application requirements often result in severe bandwidth limitations on wireless channels. A fundamental challenge is using the limited channel capacity in the most efficient way. This dissertation presents several capacity conservation schemes for wireless networks. The proposed algorithms span across the entire protocol stack but share the same objective of eliminating redundancies and ensuring the timeliness of transmissions. At the lowest level, capacity conservation is improved with a header compression algorithm that unlike previous protocols is capable of compressing MAC headers on multiple-access (shared) channels. Previous header compression schemes are unable to compress MAC headers as they rely on link layer addressing for the correct interpretation of compressed fields. An adaptive encapsulation scheme is presented to maximize the number IP packets sent during a single contention for the channel in an effort to amortize the overhead over multiple IP packets. Efficiency is improved by deferring transmissions to accumulate several packets, all while ensuring transport-level delay requirements and accounting for the time-varying nature of the transmission overhead as network conditions change. The result is an outstanding improvement in the effective throughput. Another header compression algorithm is presented which leverages on routing information to minimize the overhead of frequent context initializations during periods of high node mobility. A stateless compression scheme for control messages is also presented and is used to reduce the overhead of frequent routing updates. Finally, by addressing the redundancy and timeliness issues of multi-hop wireless broadcasting, we improve the capacity conservation in one of the most important applications in multi-hop wireless environments.
Statistical screening of system dynamics models

This paper describes a pragmatic method of searching for the key inputs to a system dynamics model. This analysis is known as screening. The goal is to learn which of the many uncertain inputs stand out as most influential. The method is implemented with readily available software and relies on the simple correlation coefficient to indicate the relative importance of model inputs at different times in the simulation. The screening is demonstrated with two examples with step-by-step instructions. The paper recommends that screening analysis be used in an iterative process of screening and model expansion to arrive at tolerance intervals on model results. The appendices compare screening analysis with analytical methods to identify the key inputs to system dynamics models. Copyright © 2005 John Wiley & Sons, Ltd.
First Molecular Detection of Piroplasm Infection in Pet Dogs from Gansu, China

Babesiosis, the hemolytic disease caused by Babesia, which is a tick-transmitted obligate intraerythrocytic protozoan parasite. This disease is responsible for significant mortality and morbidity rates and enormous economic losses to the livestock industry in tropical and subtropical regions worldwide. In this study, blood samples were collected from 141 pet dogs from Gansu, China, and analyzed for Babesia or Theileria spp. infection using specific PCR and sequencing based on 18S rRNA gene fragments. The results indicated that 18S rRNA gene sequences from 11 samples were similar to the 18S rRNA gene sequences in Babesia canis vogeli (2) and Theileria sinensis (9). The total infected rates of B. canis vogeli and T. sinensis were 1.4% (2/141) and 6.4% (9/141), respectively. This represents the first molecular report of T. sinensis in dogs worldwide and of B. canis vogeli in dogs from Gansu province of China. Furthermore, the finding of T. sinensis in dogs may represent the common infection of this parasite occurring in Gansu.
Intermediate Filaments and Polarization in the Intestinal Epithelium

The cytoplasmic intermediate filament cytoskeleton provides a tissue-specific three-dimensional scaffolding with unique context-dependent organizational features. This is particularly apparent in the intestinal epithelium, in which the intermediate filament network is localized below the apical terminal web region and is anchored to the apical junction complex. This arrangement is conserved from the nematode Caenorhabditis elegans to humans. The review summarizes compositional, morphological and functional features of the polarized intermediate filament cytoskeleton in intestinal cells of nematodes and mammals. We emphasize the cross talk of intermediate filaments with the actin- and tubulin-based cytoskeleton. Possible links of the intermediate filament system to the distribution of apical membrane proteins and the cell polarity complex are highlighted. Finally, we discuss how these properties relate to the establishment and maintenance of polarity in the intestine.
RETREIVAL OF CONTENT BASED HISTOLOGY IMAGES USING MULTIFEATURE FUSION MODEL

Content Based Image Retrieval plays a major role in medical activities, education field and in research areas. Feature combination plays a significant role in Content Based Image Retrieval. The aim of this system is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images by automatically combining heterogeneous visual features. The core approach of the system is Multiobjective learning method which aims at understanding the concept of optimal visual-semantic matching function by jointly considering the different preferences of the group of images. In this system, a new strategy called Multiobjective Optimization strategy is employed in order to handle contradictions which arise in the query images associated with the same keyword. Index Terms — Multifeature Fusion Model, Multiobjective Optimization, Pareto Archived Evolution Strategy (PAES).
Internal Transcribed Spacer Sequencing versus Biochemical Profiling for Identification of Medically Important Yeasts

ABSTRACT In this study, we established an in-house database of yeast internal transcribed spacer (ITS) sequences. This database includes medically important as well as colonizing yeasts that frequently occur in the diagnostic laboratory. In a prospective study, we compared molecular identification with phenotypic identification by using the ID32C system (bioMérieux) for yeast strains that could not be identified by a combination of CHROMagar Candida and morphology on rice agar. In total, 113 yeast strains were included in the study. By sequence analysis, 98% of all strains were identified correctly to the species level. With the ID32C, 87% of all strains were identified correctly to the species or genus level, 7% of the isolates could not be identified, and 6% of the isolates were misidentified, most of them as Candida rugosa or Candida utilis. For a diagnostic algorithm, we suggest a three-step procedure which integrates morphological criteria, biochemical investigation, and sequence analysis of the ITS region.
Extraction of Deﬁnitions in Portuguese: An Imbalanced Data Set Problem

Deﬁnition extraction is an important task in NLP and IR ﬁelds in the context of e.g. question answering, ontology learning, dictionary and glossary construction. When addressed with learning algorithms, it turns out to be a challenging task due to the structure of the data set, the reason being that the deﬁnition-bearing sentences are much fewer than the sentences that are non deﬁnitions. In this paper, we present results from experiments that seek to obtain optimal solutions for this problem by using a corpus written in the Portuguese language. Our results show an improvement of 29 points regarding AUC metric and more than 60 points when considering the F-measure.
CONVERGENCE TO A NOVEL ENVIRONMENT: COMPARATIVE METHOD VERSUSEXPERIMENTAL EVOLUTION

Abstract Laboratory adaptation allows researchers to contrast temporal studies of experimental evolution with comparative studies. The comparative method is here taken to mean the inference of microevolutionary processes from comparisons among contemporaneous populations of diverse origins, from one or multiple species. The data contrasted here come from Drosophila subobscura populations that were introduced to the laboratory at several different times and from two different locations. Two questions were addressed. First, can we correctly infer evolutionary dynamics from comparative data collected simultaneously from disparate populations? In most cases, we could, except for the character of starvation resistance. Second, are the evolutionary dynamics inferred from the comparative approach similar to those revealed by temporal studies of experimental evolution? For fecundity characters, they were. Overall the results show that both comparative and temporal studies are useful, though the former can be uninformative for characters with complex evolutionary trajectories.
Gujrati Character Recognition Using Adaptive Neuro Fuzzy Classifier

This paper presents implementation of an Adaptive Neuro Fuzzy Classifier (ANFC) for recognition of isolated handwritten characters of Gujrati based on [2]. Authors aim to compare the performance of ANFC with weighted k - NN classifier proposed in [1] by them. Fuzzy classification is the task of partitioning a feature space into fuzzy classes. Authors exploit the method of employing adaptive networks based on [2] to solve a fuzzy classification problem. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions are calibrated with back propagation. Towards this aim, authors use a supervised learning procedure based on Scaled Conjugate Gradient (SCG) algorithm to update parameters in an adaptive network. Next, this architecture is deployed for the character recognition problem. From the experimental results, it is summarized that although adaptively adjusted classifier performs well as far as time complexity is concerned but fails to achieve better recognition rates than weighted k - NN. The results are discussed from the viewpoint of feature extraction methods discussed in [1] and their effectiveness on neuro fuzzy classifiers.
Optimization of interactive visual-similarity-based search

At one end of the spectrum, research in interactive content-based retrieval concentrates on machine learning methods for effective use of relevance feedback. On the other end, the information visualization community focuses on effective methods for conveying information to the user. What is lacking is research considering the information visualization and interactive retrieval as truly integrated parts of one content-based search system. In such an integrated system, there are many degrees of freedom like the similarity function, the number of images to display, the image size, different visualization modes, and possible feedback modes. To base the optimal values for all of those on user studies is unfeasible. We therefore develop search scenarios in which tasks and user actions are simulated. From there, the proposed scheme is optimized based on objective constraints and evaluation criteria. In such a manner, the degrees of freedom are reduced and the remaining degrees can be evaluated in user studies. In this article, we present a system that integrates advanced similarity based visualization with active learning. We have performed extensive experimentation on interactive category search with different image collections. The results using the proposed simulation scheme show that indeed the use of advanced visualization and active learning pays off in all of these datasets.
Zymogen Activation Confers Thermodynamic Stability on a Key Peptide Bond and Protects Human Cationic Trypsin from Degradation*

Background: Initial cleavage by chymotrypsin C regulates degradation of human cationic trypsin. Results: Cleavage is reversible and favors calcium-dependent bond formation in trypsin, but not in trypsinogen. Conclusion: Trypsin resistance to degradation derives from the regulated thermodynamic stability of a specific peptide bond that is responsive to physiological environment. Significance: This new paradigm explains the robustness of trypsin functioning in the protease-rich intestinal milieu. Human cationic trypsinogen, precursor of the digestive enzyme trypsin, can be rapidly degraded to protect the pancreas when pathological conditions threaten, while trypsin itself is impressively resistant to degradation. For either form, degradation is controlled by two necessary initial proteolytic events: cleavage of the Leu81–Glu82 peptide bond by chymotrypsin C (CTRC) and cleavage of the Arg122–Val123 peptide bond by trypsin. Here we demonstrate that the Leu81–Glu82 peptide bond of human cationic trypsin, but not trypsinogen, is thermodynamically stable, such that cleavage by CTRC leads to an equilibrium mixture containing 10% cleaved and 90% uncleaved trypsin. When cleaved trypsin was incubated with CTRC, the Leu81–Glu82 peptide bond was re-synthesized to establish the same equilibrium. The thermodynamic stability of the scissile peptide bond was not dependent on CTRC or Leu-81, as re-synthesis was also accomplished by other proteases acting on mutated cationic trypsin. The Leu81–Glu82 peptide bond is located within a calcium binding loop, and thermodynamic stability of the bond was strictly dependent on calcium and on the calcium-coordinated residue Glu-85. Trypsinolytic cleavage of the Arg122–Val123 site was also delayed in trypsin relative to trypsinogen in a calcium-dependent manner, but for this bond cleavage was modulated by kinetic rather than thermodynamic control. Our results reveal that the trypsinogen to trypsin conformational switch modulates cleavage susceptibility of nick sites by altering both the thermodynamics and kinetics of cleavage to protect human cationic trypsin from premature degradation.
CRAM — A Cognitive Robot Abstract Machine for everyday manipulation in human environments

This paper describes CRAM (Cognitive Robot Abstract Machine) as a software toolbox for the design, the implementation, and the deployment of cognition-enabled autonomous robots performing everyday manipulation activities. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed. This way CRAM-programmed autonomous robots are much more flexible, reliable, and general than control programs that lack such cognitive capabilities. CRAM does not require the whole domain to be stated explicitly in an abstract knowledge base. Rather, it grounds symbolic expressions in the knowledge representation into the perception and actuation routines and into the essential data structures of the control programs. In the accompanying video, we show complex mobile manipulation tasks performed by our household robot that were realized using the CRAM infrastructure.
Fast matrix rank algorithms and applications

We consider the problem of computing the rank of an mxn matrix A over a field. We present a randomized algorithm to find a set of r = rank(A) linearly independent columns in O(|A| + rw) field operations, where |A| denotes the number of nonzero entries in A and w < 2.38 is the matrix multiplication exponent. Previously the best known algorithm to find a set of r linearly independent columns is by Gaussian elimination, with running time O(mnrw). Our algorithm is faster when r < max{m,n}, for instance when the matrix is rectangular. We also consider the problem of computing the rank of a matrix dynamically, supporting the operations of rank one updates and additions and deletions of rows and columns. We present an algorithm that updates the rank in O(mn) field operations. We show that these algorithms can be used to obtain faster algorithms for various problems in numerical linear algebra, combinatorial optimization and dynamic data structure.
An Efficient Heterogeneous Memristive xnor for In-Memory Computing

Resistive RAM (RRAM) technologies are gaining importance due to their appealing characteristics, which include non-volatility, small form factor, low power consumption, and ability to perform logic operations in memory. These characteristics make RRAM highly suited for Internet of Things devices and similarly resource-constrained systems. This paper proposes a novel memristor-based xnor gate that enables the execution of xnor/xor function in the memristive crossbar memory. The proposed two-input xnor gate requires two steps to perform the xnor function. The design of the circuit utilizes bipolar and unipolar memristors and permits cascading by only adding an extra step and one computing memristor. To the best of our knowledge, this is the first native stateful xnor logic implementation. Spice simulations have been used to verify the functionality of the proposed circuit. This includes benchmarking the proposed design against the state-of-the-art stateful memristor-based logic circuits. The results for implementing three-input xor using the proposed circuit demonstrate efficient performance in terms of energy, latency, and area. The gate shows 56% saving in energy, 54% less number of steps (latency), and 50% less number of computing MR (area) compared with the state-of-the-art stateful xor/xnor implementations.
Comparison of different grid abstractions for pathfinding on maps

Pathfinding on a map is a fundamental problem in many applications, including robotics and computer games. Typically a grid is superimposed over the map where each cell in the grid forms a unique state. A state-space-based search algorithm, such as A* or IDA*, is then used for finding the optimal (shortest) path. In this paper we analyze the search behavior of both A* and IDA* using different grid representations, providing various new insights via analytical and empirical results.
A comparison between star products on regular orbits of compact Lie groups

In this paper, an algebraic and a differential star product defined on a regular coadjoint orbit of a compact semisimple group are compared. It has been proved that there is an injective algebra homomorphism between the algebra of polynomials with the algebraic star product and the algebra of differential functions with the differential star product structure.
Synaptotagmin‐11 inhibits cytokine secretion and phagocytosis in microglia

Cytokine secretion and phagocytosis are key functions of activated microglia. However, the molecular mechanisms underlying their regulation in microglia remain largely unknown. Here, we report that synaptotagmin‐11 (Syt11), a non‐Ca2+‐binding Syt implicated in Parkinson disease and schizophrenia, inhibits cytokine secretion and phagocytosis in microglia. We found Syt11 expression in microglia in brain slices and primary microglia. Interestingly, Syt11‐knockdown (KD) increased cytokine secretion and NO release in primary microglia both in the absence and presence of lipopolysaccharide. NF‐κB was activated in untreated KD microglia together with enhanced synthesis of IL‐6, TNF‐α, IL‐1β, and iNOS. When the release capacity was assessed by the ratio of extracellular to intracellular levels, only the IL‐6 and TNF‐α secretion capacity was increased in Syt11‐KD cells, suggesting that Syt11 specifically regulates conventional secretion. Consistently, Syt11 localized to the trans‐Golgi network and recycling endosomes. In addition, Syt11 was recruited to phagosomes and its deficiency enhanced microglial phagocytosis. All the KD phenotypes were rescued by expression of an shRNA‐resistant Syt11, while overexpression of Syt11 suppressed cytokine secretion and phagocytosis. Importantly, Syt11 also inhibited microglial phagocytosis of α‐synuclein fibrils, supporting its association with Parkinson disease. Taken together, we propose that Syt11 suppresses microglial activation under both physiological and pathological conditions through the inhibition of cytokine secretion and phagocytosis.
Predicting bioavailability of PAHs and PCBs with porewater concentrations measured by solid‐phase microextraction fibers

Bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) was measured in the deposit‐feeding oligochaete Ilyodrilus templetoni exposed for 28 d to Anacostia River sediment (Washington, DC, USA) and to an initially uncontaminated sediment from Brown Lake (Vicksburg, MS, USA) sequentially diluted with 3 to 25% contaminated New Bedford Harbor sediment (New Bedford, MA, USA). The Anacostia River sediment studies represented exposure to a historically contaminated sediment with limited availability, whereas exposure to the other sediment included both the historically contaminated New Bedford Harbor sediment and fresh redistribution of contaminants into the Brown Lake sediments. Organism tissue concentrations did not correlate with bulk sediment concentrations in the Anacostia River sediment but did correlate with the sequentially diluted sediment. Porewater concentrations measured via disposable solid‐phase microextraction fiber (SPME) with polydimethylsiloxane (PDMS), however, correlated well with organism uptake in all sediments. Bioaccumulation was predicted well by a linear relationship with the product of porewater concentration and compound octanol–water partition coefficient (Anacostia, slope = 1.08, r2 = 0.76; sequentially diluted sediments, slope = 1.24, r2 = 0.76). The data demonstrate that the octanol–water partition coefficient is a good indicator of the lipid–water partition coefficient and that porewater concentrations provide a more reliable indicator of bioaccumulation in the organism than sediment concentrations, even when the route of uptake is expected to be via sediment ingestion. Environ. Toxicol. Chem. 2011; 30:1109–1116. © 2011 SETAC
Index-less flash codes with arbitrary small slices

This paper proposes a novel flash code which is based on the index-less indexed flash codes (ILIFC) but uses significantly small slices of flash cells. ILIFC has good write deficiency in the worst-case scenario, though, the code-rate and the average write deficiency are severely restricted because the size of an internal data block, which is called a slice, is rather large and not adjustable. In this study, a technique is developed to reduce the size of slices in ILIFC-like flash codes. The improvement relaxes the severe constraint on the code-rate, and additionally, contributes to reduce the average write deficiency of the flash code.
Preparation and In Vitro/In Vivo Evaluation of the Buccal Bioadhesive Properties of Slow-Release Tablets Containing Miconazole Nitrate

Abstract Slow-release buccal bioadhesive tablets of miconazole nitrate were prepared by using polymer mixtures of buccoadhesive materials such as hydroxypropylmethylcellulose, sodium carboxymethylcellulose, carbopol 934p, and sodium alginate. The physicochemical properties, swelling index, microenvironment pH, in vitro drug release, in vivo buccoadhesion time, and miconazole salivary concentrations of the prepared tablets were shown to be dependent on the type and composition of the buccoadhesive materials used. The dissolution of miconazole from all the prepared tablets into phosphate buffer (pH 6.8) was controlled and followed non-Fickian release mechanisms. All the prepared tablets gave reasonable buccoadhesion time (2.45–3.65 hr). Infrared spectroscopy and differential scan calorimetry studies revealed the absence of significant interactions between miconazole nitrate and the selected buccoadhesive materials. Duration of the antifungal activity as measured by the inhibition zone of Candida albicans by extracted human saliva was significantly longer (p<0.05), compared with commercial miconazole oral gel (Daktaren®oral gel). Based on the results obtained, the prepared slow-release buccoadhesive tablets of miconazole would markedly prolong the duration of the antifungal activity with more patient convenience.