Miracle-dz/UniFAR-FTUs huggingface dataset:
Symbolic behavioral modeling of low voltage amplifiers

We present the generation of symbolic expressions of low voltage amplifiers by using nullors equivalents of the transistors (MOSFETs) which include only the dominant parasitic elements in order to make a simplification before generation. That way, a reduced system of nodal equations is formulated which can be solved easily by determinant decision diagrams. The advantage of the proposed method is that the resulting symbolic behavioral model is reduced to a minimum order and contains just the dominant circuit elements providing a good insight, which is very useful for analog designers.
Challenges of creating of network-level performance measurement system for SME networks

There has been hardly any research about the management tools and the holistic performance measurement systems which are very important when managing SME networks. The present study is based on two different perspectives to produce added value to discussion on networking. These perspectives are a system view and a view that considers networks as entities. In the present study, the main objective is to create and operationalise a network-level performance measurement system for SME networks. The second objective is to analyse a case network by the means of this performance measurement system. The third objective is to discuss the challenges of building this kind of network-level performance measurement system.
A Tree Decomposition Based Approach to Solve Structured SAT Instances

The main purpose of the paper is to solve structured instances of the satisfiability problem. The structure of a SAT instance is represented by an hypergraph, whose vertices correspond to the variables and the hyper-edges to the clauses. The proposed method is based on a tree decomposition of this hyper-graph which guides the enumeration process of a DPLL-like method. During the search, the method makes explicit some information which is recorded as structural goods and nogoods. By exploiting this information, the method avoids some redundancies in the search, and so it guarantees a bounded theoretical time complexity which is related to the tree-decomposition. Finally, the method is assessed on structured SAT benchmarks.
Single-cell recordings in the human medial temporal lobe

Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons have opened the possibility to narrow down the gap between neurophysiological studies in animals and non‐invasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography) investigations in humans. Here we provide a description of the main procedures for electrode implantation and recordings, the experimental paradigms used and the main steps for processing the data. We also present key characteristics of the so‐called ‘concept cells’, neurons in the human medial temporal lobe with selective and invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative memory. Finally, we present novel results dealing with the stability of the representation given by these neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show that, after an initial decay, the response strength reaches an asymptotic value after approximately 15 presentations that remains above baseline for the whole duration of the experiment.
A comparison of approaches to affective rating of Chinese words on valence-arousal space

The word-level sentiment analysis is an essential issue in opinion mining. One challenge in this field is that not so many lexical items as expected have been labeled with sentimental opinions, especially in Chinese. There are two ways of rating words: one is manual marking which costs lots of resources, energy and time; the other is machine marking which is efficient, convenient and time-saving. There are a few machine rating approaches such as linear regression, support vector regression and weighted graph method. This paper compares the three approaches of linear regression, vector regression and kernel models based on valence-arousal (VA) space in order to study an effective and accurate machine learning algorithm for increasing more Chinese affective words on the existing affective lexicon.
A collaborative Bayesian image retrieval framework

In this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (STRF) and long-term relevance feedback (LTRF) as the means of user interaction. The STRF refers to iterative query-specific model learning during a retrieval session, and the LTRF is the estimation of a user history model from the past retrieval results approved by previous users. The framework is formulated based on the Bayes' theorem, in which the results from STRF and LTRF play the roles of refining the likelihood and the a priori information, respectively, and the images are ranked according to the a posteriori probability. Since the estimation of the user history model is based on the principle of collaborative filtering, the system is referred to as a collaborative Bayesian image retrieval (CLBIR) framework. To evaluate the effectiveness of the proposed framework, nearest neighbor CLBIR (NN-CLBIR) and support vector machine active learning CLBIR (SVMAL-CLBIR) were implemented. Experimental results showed the improvement over content-based methods in terms of both accuracy and ranking due to the integration in the proposed framework.
Research on the blind restoration algorithm of motion-blurred image

The restoration of motion-blurred image is one of important subjects of image restoration. This paper mainly researched on the restoration methods for motion-blurred image based on estimated motion blurring angle and motion blurring length. First, the motion blurring angle is estimated by Hough transform algorithm. In order to reduce the impact of concrete structure on the detection, the edge detection of the blurred image was done by Canny before the detection of the movement direction. Next, the motion blurring length is estimated by differential-autocorrelation. Finally, motion-blurred license plate image is restored by Lucy-Richardson based on motion blurring angle and length. Experiment results show the restoration image of the motion-blurred license plate is very clear.
Outsourcing the Nucleus: Nuclear Pore Complex Genes are no Longer Encoded in Nucleomorph Genomes

The nuclear pore complex (NPC) facilitates transport between nucleus and cytoplasm. The protein constituents of the NPC, termed nucleoporins (Nups), are conserved across a wide diversity of eukaryotes. In apparent exception to this, no nucleoporin genes have been identified in nucleomorph genomes. Nucleomorphs, nuclear remnants of once free-living eukaryotes, took up residence as secondary endosymbionts in cryptomonad and chlorarachniophyte algae. As these genomes are highly reduced, Nup genes may have been lost, or relocated to the host nucleus. However, Nup genes are often poorly conserved between species, so absence may be an artifact of low sequence similarity. We therefore constructed an evolutionary bioinformatic screen to establish whether the apparent absence of Nup genes in nucleomorph genomes is due to genuine absence or the inability of current methods to detect homologues. We searched green plant (Arabidopsis and rice), green alga (Chlamydomonas reinhardtii) and red alga (Cyanidioschyzon merolae) genomes, plus two nucleomorph genomes (Bigelowiella natans and Guillardia theta) with profile hidden Markov models (HMMs) from curated alignments of known vertebrate/yeast Nups. Since the plant, algal and nucleomorph genomes all belong to the kingdom Plantae, and are evolutionarily distant from the outgroup (vertebrate/yeast) training set, we use the plant and algal genomes as internal positive controls for the sensitivity of the searches in nucleomorph genomes. We find numerous Nup homologues in all plant and free-living algal species, but none in either nucleomorph genome. BLAST searches using identified plant and algal Nups also failed to detect nucleomorph homologues. We conclude that nucleomorph Nup genes have either been lost, being replaced by host Nup genes, or, that nucleomorph Nup genes have been transferred to the host nucleus twice independently; once in the evolution of the red algal nucleomorph and once in the green algal nucleomorph.
Smoothing Algorithms and High-Order Singularities in Gravitational Lensing

We propose a new smoothing method for obtaining surface densities from discrete particle positions from numerical simulations. This is an essential step for many applications in gravitational lensing. This method is based on the "scatter" interpretation of the discrete density field in the smoothed particle hydrodynamics. We use Monte Carlo simulations of uniform density fields and one isothermal ellipsoid to empirically derive the noise properties and best smoothing parameters (such as the number of nearest neighbors used). A cluster from high-resolution simulations is then used to assess the reality of high-order singularities such as swallowtails and butterflies in caustics, which are important for the interpretation of substructures in gravitational lenses. We also compare our method with the Delaunay tesselation field estimator using the galaxy studied by Bradac et al. (2004), and we find good agreements. We show that higher order singularities are connected not only with bound subhalos but also with the satellite streams. However, the presence of high-order singularities is sensitive to not only the fluctuation amplitude of the surface density but also the detailed form of the underlying smooth lensing potential (such as ellipticity and external shear).
Dexpler: converting Android Dalvik bytecode to Jimple for static analysis with Soot

This paper introduces Dexpler, a software package which converts Dalvik bytecode to Jimple. Dexpler is built on top of Dedexer and Soot. As Jimple is Soot's main internal representation of code, the Dalvik bytecode can be manipulated with any Jimple based tool, for instance for performing point-to or flow analysis.
When a risky prospect is valued more than its best possible outcome

In this paper, we document a violation of normative and descriptive models of decision making under risk. In contrast to uncertainty effects found by Gneezy, List and Wu (2006), some subjects in our experiments valued lotteries more than the best possible outcome. We show that the overbidding effect is more strongly related to individuals’ competitiveness traits than comprehension of the lottery’s payoff mechanism.
Association of Glomerular Filtration Rate with High-Sensitivity Cardiac Troponin T in a Community-Based Population Study in Beijing

Objective The reduced level of glomerular filtration rate (GFR) is an independent risk factor for cardiovascular disease (CVD) mortality and persistently elevated cardiac biomarkers are frequently observed in patients with end-stage renal disease. In general population, however, the clinical use of the serum levels of cardiac biomarkers has limitations because of the low sensitivity of the assay system. Recently, a highly sensitive cTnT (hs-cTnT) assay has become commercially available. In the present study, the authors investigated the cTnT level using highly sensitive commercial assay, evaluated the association of eGFR with hs-cTnT in community-based population in Beijing. Methods The serum hs-cTnT levels were measured in a community-based population (n=1497; 48.1% men; aged ≥45 years) living in Pingguoyuan area of the Shijingshan district, a metropolitan area in Beijing, China. The lower detection limit of the hs-cTnT assay used in the present study was 3 pg/ml. eGFR was evaluated by the Chinese modified Modification of Diet in Renal Disease equation. Anthropometry, blood pressure, fasting plasma glucose (FPG), serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid (UA) and NT-proBNP were measured. Pearson's correlation and a multivariate logistic regression modelling were used to used to establish the relationship between eGFR value and detectable hs-cTnT value. Results With the highly sensitive assay, detectable hsTnT levels were seen (> 3 pg/ml) in 798 subjects (54.9%) and concentrations of cardiac troponin T were at or above 13.3 pg/ml in 178 patients (11.9%). The level of hs-cTnT was increased in patients with hypertension, diabetes, coronary artery disease and/or renal insufficiency. The result of Pearson correlation showed that eGFR was associated with hs-cTnT (r=−0.14, p<0.001). After adjustment for other risk factors, the results of multivariate logistic regression analysis showed eGFR were independently inversely associated with hs-cTnT (adjusted odds ratio, 0.57; 95% CI, 0.36 to 0.77; p<0.001) in this study group. Conclusion The present study showed that after adjusting for multiple potential confounders, eGFR were significantly associated with cardiac troponin T concentrations as measured with a highly sensitive assay in the community-based subjects.
Finding Strongly Connected Components in a Social Network Graph

A strongly connected component (SCC) of a digraph is a maximal set of vertices such that every vertex is reachable from every other vertex. This topic is very interesting because of the way the algorithm can be used in various applications of network and communications. The Strongly Connected Components Detection (SCCD) algorithm can be a powerful tool in social networking service that is a platform to construct social networks or social relations forming communities, among people who offer similar interests, activities, establishments or genuine associations. SNS can study the evolution of those communities and getting to know what community a person belongs to, may help him, getting better ads targeting their essentials. In this work, we propose to apply SCC Detection algorithm to the Social Network Graph (or SNG) to identify smaller groups of nodes related to each other by some specific criteria (Sports, Health, Technology, Religion, etc). We discuss some findings observed from the application of this algorithm to the SNG.
Exploration and Exploitation as Antecedents of Environmental Performance: The Moderating Effect of Technological Dynamism and Firm Size

This study aims to expand our understanding of environmental performance by adopting exploration and exploitation concepts, which are key types of firm innovation. We reveal exploration and exploitation as two important antecedents of proactive and reactive environmental performance. We also identify the conditions under which the distinct effects of these two types of innovation are moderated. Using a sample of 2060 firm-year observations over a 12-year period in various industries, we find that firm exploration positively affects proactive environmental performance, whereas firm exploitation positively influences reactive environmental performance. Furthermore, we find that the positive effect of exploitation on reactive environmental performance intensifies when technological dynamism is high, and the positive effect of exploration on proactive environmental performance strengthens when a firm is large.
On the Geometry of the Hermite - Fejer Interpolation Problem through Conservative Realizations ∗

In this paper, we give state space realizations for the clas- sical recursive solutions of operator-valued Caratheodory, Nevanlinna - Pick and Hermite - Fejer interpolation problems. These realiza- tions are special in the sense that they satisfy an energy balance law; hence they are called conservative. Observability, controllability and minimality (including the property known as "simplicity") of such re- alizations are studied, too. Finally, the main result of this paper is given, namely a geometric characterization for the McMillan degree of interpolants.
A Comparison Of Adaptive Wormhole Routing Algorithms

Improvement of message latency and network utilization in torus interconnection networks by increasing adaptivity in wormhole routing algorithms is studied. A recently proposed partially adaptive algorithm and four new fully-adaptive routing algorithms are compared with the well-known e-cube algorithm for uniform, hotspot, and local traffic patterns. Our simulations indicate that the partially adaptive north-last algorithm, which causes unbalanced traffic in the network, performs worse than the nonadaptive e-cube routing algorithm for all three traffic patterns. Another result of our study is that the performance does not necessarily improve with full-adaptivity. In particular, a commonly discussed fully-adaptive routing algorithm, which uses 2n virtual channels per physical channel of a k-ary n-cube, performs worse than e-cube for uniform and hotspot traffic patterns. The other three fully-adaptive algorithms, which give priority to messages based on distances traveled, perform much better than the e-cube and partially-adaptive algorithms for all three traffic patterns. One of the conclusions of this study is that adaptivity, full or partial, is not necessarily a benefit in wormhole routing.
Object Persistence Techniques - A Study of Approaches, Benefits, Limits and Challenges

Oriented paradigm becomes pioneer and best choice while selecting language and writing software solution. In last few decades there was significant change observed in developing software solutions. Most of the application developers prefer the object oriented model to exploit its benefits. The major benefit we can obtain from Object- Orientation is of course object itself and the feature that enable us making an object persistent. Object-Persistence feature contributes a major role in designing data model. If the techniques used for Object-Persistence are designed correctly then, we can obtain major benefits in the areas of software productivity, maintainability and cost reduction. There are many ways of implementing Object-Persistence among which Gateway-based method, Object-Relational database method and Object-Oriented database method are the three major categories. In this paper, we discuss about the characteristics of various Object-Persistence techniques, the relevant areas in which those techniques can be employed efficiently and how those techniques can be used effectively on the basis of application characteristics and requirements. We also discuss about the benefits and limitations of persistence techniques. Further, our discussion continues on various challenges that come along the way of Object-Persistence and possible solutions to handle those challenges.
Monte Carlo localization for a guide mobile robot in a crowded environment based on omnivision

This work presents a localization system for a robot guide on a crowded environment based on omnidirectional vision and a map of ceiling landmarks. The developed approach uses a Monte Carlo particle filter to manage uncertainty, both on observations and control, in order to track the position of the robot. We describe how landmarks are detected on each image and how the problem of landmark association is posed. To demonstrate the robustness and reliability of our system, we present experiments carried out in a real environment, the Domus Museum in A Coruña (Spain). Results show that the proposed localization system can run on real time and along middle-long trajectories.
Juha Koskela Software configuration management in agile methods

The development of good quality software is a critical element of successful competition for today’s software market shares. However, software products are becoming larger and more complex; therefore, the development of quality software is neither easy nor rapid. Agile software development methods focus on generating early releases of working products. They aim to deliver business value immediately from the beginning of the project. Regardless of the development method in use, it is important that software development be under control. Software configuration management (SCM) is known as a method of bringing control to the software development process, and thus, proper application of SCM is a key component in the development of quality software. However, currently very few studies on software configuration management in agile methods exist; hence this study. The aim of this publication is to systematically review the existing literature on agile software development methodologies from an SCM point of view. First, analytical SCM lenses based on existing SCM literature are constructed. Second, existing agile methods are analyzed using the lenses constructed. The results show that only two of the existing agile methods take SCM explicitly into account, but most of the methods highly value SCM tool support and its ability to revert to earlier versions of development artefacts. Nonetheless, the basis for successful SCM implementation, SCM planning, has been completely forgotten.
Immunocytochemical studies of actin localization in the central nervous system

The contractile protein, actin, is a major component of the central nervous system (CNS). Its accumulation at presynaptic and postsynaptic sites has been suggested by biochemical and ultrastructural studies. The present work examines directly the distribution of actin within the CNS by immunocytochemical means. Sections of rat CNS were stained with an antiserum against chicken gizzard actin, using a peroxidase- antiperoxidase technique. In Vibratome sections, reaction product appeared widely distributed in the perikaryal cytoplasm of neurons, in axons and dendrites, and in astrocytes and blood vessels. In paraffin sections, however, much of the cytoplasmic reaction was not observed. Instead, the reaction product was concentrated in processes within the neuropil and in discrete densities about the perimeters of neurons. This latter distribution, observed in areas in which large axosomatic synapses are known to occur, was consistent with a concentration at synaptic sites. Immunocytochemistry performed at the ultrastructural level, using a pre-embedding technique, revealed a diffuse cytoplasmic staining, with increased accumulation under the neuronal plasma membrane, particularly at postsynaptic densities.
A space–time model for seasonal hurricane prediction

A space–time count process model is explained and applied to annual North Atlantic hurricane activity. The model uses the best‐track data set of historical hurricane positions and intensities, together with climate variables, to determine local space–time coefficients of a right‐truncated Poisson process. The truncated Poisson space–time autoregressive (TPSTAR) model is motivated by first examining a time‐series model for the entire domain. Then a Poisson generalized linear model is considered that uses grid boxes within the domain and adds offset factors for latitude and longitude. A natural extension is then made that includes instantaneous local and autoregressive coupling between the grids. A final version of the model is found by backward selection of the predictors based on values of Bayesian and Akiake information criteria. The final model has five nearest neighbours and statistically significant couplings. Hindcasts are performed on the hurricane seasons from 1994 to 1997. Results show that, on average, model forecast probabilities are larger in regions in which hurricanes occurred. Quantitative skill assessment indicates some useful skill above climatology—currently the default leading candidate. The TPSTAR model could be a valuable guidance product when issuing seasonal hurricane forecasts. Copyright © 2002 Royal Meteorological Society.