Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of Level of Quality Control Activities in Software Development [Quality Control Scorecards]

This paper presents the method of measuring level of quality control activities to ensure the quality of delivered software during the development cycle. Obviously, there are many key performance indexes derivable from the actual data associated with quality control activities and it is desirable to create a scorecard to integrally represent overall level of quality control activities performed while developing the software deliverables. With the score card, it is possible to evaluate whether enough quality control activities are performed for the project officially and secure the quality of the software deliverables before it is delivered to the customers.
Type I Interferons Promote Fatal Immunopathology by Regulating Inflammatory Monocytes and Neutrophils during Candida Infections

Invasive fungal infections by Candida albicans (Ca) are a frequent cause of lethal sepsis in intensive care unit patients. While a contribution of type I interferons (IFNs-I) in fungal sepsis remains unknown, these immunostimulatory cytokines mediate the lethal effects of endotoxemia and bacterial sepsis. Using a mouse model lacking a functional IFN-I receptor (Ifnar1−/−), we demonstrate a remarkable protection against invasive Ca infections. We discover a mechanism whereby IFN-I signaling controls the recruitment of inflammatory myeloid cells, including Ly6Chi monocytes and neutrophils, to infected kidneys by driving expression of the chemokines CCL2 and KC. Within kidneys, monocytes differentiate into inflammatory DCs but fail to functionally mature in Ifnar1−/− mice, as demonstrated by the impaired upregulation of the key activation markers PDCA1 and iNOS. The increased activity of inflammatory monocytes and neutrophils results in hyper-inflammation and lethal kidney pathology. Pharmacological diminution of monocytes and neutrophils by treating mice with pioglitazone, a synthetic agonist of the nuclear receptor peroxisome proliferator-activated receptor-γ (PPAR-γ), strongly reduces renal immunopathology during Ca infection and improves mouse survival. Taken together, our data connect for the first time the sepsis-promoting functions of IFNs-I to the CCL2-mediated recruitment and the activation of inflammatory monocytes/DCs with high host-destructing potency. Moreover, our data demonstrate a therapeutic relevance of PPAR-γ agonists for microbial infectious diseases where inflammatory myeloid cells may contribute to fatal tissue damage.
AN ALTERNATING MINIMIZATION ALGORITHM FOR NON-NEGATIVE MATRIX APPROXIMATION

Matrix approximation problems with non-negativity constraints arise during the analysis of high-dimensional non-negative data. Applications include dimensionality reduction, feature extraction and statistical factor analysis. This paper discusses a general algorithm for solving these problems with respect to a large class of error measures. The primary goal of this article is to develop a rigorous convergence theory for the algorithm. It also touches on some experimental results of S. Sra [1] which indicate the superiority of this method to some previously proposed techniques. Date: 8 May 2003. J. Tropp is with The University of Texas, Austin, TX 78712 USA, jtropp@ices.utexas. edu. 1 NON-NEGATIVE MATRIX APPROXIMATION 2
Biases in Estimating Long Run Abnormal Returns and Conditional Measures of Performance: The Evidence on Takeovers Re-Examined

Using the UK as a “natural experiment” that allows examination of the effect of the form of payment separately from bidder hostility in a way not easily achievable in the US, we show that the form of payment hypothesis interacts with bid hostility, or “disciplinary bidding” in explaining acquirer wealth effects. Equity financed acquisitions and non-hostile bids generate negative returns, and their combination in a bid is particularly detrimental. Overall, neither cash financing nor bidder hostility generate positive abnormal returns. However, there is some evidence that the combination of cash financing and hostility that uniquely generates positive shareholder returns. In using this experimental arena, we note that recent UK research has produced conflicting evidence on the previously documented stock market ‘anomaly’ of acquiring firms exhibiting substantial negative abnormal returns following acquisitions. This raises the intriguing question as to whether or not the UK experience is different from that of the US, where negative abnormal returns to acquirers is now well-established (Agrawal et al [1992], Loughran and Vijh [1997]). We show that when tests are used which control for the skewness and bias in the estimation of long run abnormal returns, as prescribed in Lyon et al (1999), the negative performance of UK acquiring companies documented by Limmack (1991), Kennedy and Limmack (1996), Gregory (1997) and Conn et al (2004) is confirmed. The evidence here shows that the magnitude of these abnormal returns may have been under-estimated by previous studies. In line with previous research, we find support for the form of payment hypothesis (Aggrawal and Jaffe, 2000), but also show the combination of form of the bid and form of payment is a critical factor in determining post bid returns. We also show that these results are robust to specifications which control for performance conditional upon time-varying risk and expected return measures and for returns calculated in calendar rather than event time.
Compositional Planning Using Optimal Option Models

In this paper we introduce a framework for option model composition. Option models are temporal abstractions that, like macro-operators in classical planning, jump directly from a start state to an end state. Prior work has focused on constructing option models from primitive actions, by intra-option model learning; or on using option models to construct a value function, by inter-option planning. We present a unified view of intra- and inter-option model learning, based on a major generalisation of the Bellman equation. Our fundamental operation is the recursive composition of option models into other option models. This key idea enables compositional planning over many levels of abstraction. We illustrate our framework using a dynamic programming algorithm that simultaneously constructs optimal option models for multiple subgoals, and also searches over those option models to provide rapid progress towards other subgoals.
Performance of linear multi-user MIMO precoding in LTE system

For scenarios with a large number of users to be served in one cell, high capacity gains can be achieved by transmitting independent data streams to different users sharing the same time-frequency resources. This technique is known as Multi-User MIMO (MU-MIMO). In this paper we investigate the performance of MU-MIMO operation in 3GPP LTE for different frequency granularities of the precoder at the OFDM transmitter. We also investigate the impact of channel correlation on the performance of the receiver when it is unaware of the interfererpsilas precoding vector. The performance is evaluated by means of semistatic system simulations.
Collaboratively Searching the Web – An Initial Study

We investigated the hypothesis that precision in web se arch engines can be improved by using relevance information provided by a large number of different users. We tested the hypothesis with a search engine that monitored whic h documents were selected among the results for a query and then used that informati on to re-rank the results when other users submitted the same query. The engine ran on the WWW and received nearly 3,000 queries in a period of two months. An analysis of the performance history of the engine does not show any improvement in the quality of t he results returned for very frequent queries. Anecdotal evidence, however, sugges ts that for less popular queries the engine did learn to re-rank documents in such a way that relevant documents appear at the top of the results list. More experimentati on and especially more data are needed to prove or disprove the hypothesis.
A New Machairodont from the Palmetto Fauna (Early Pliocene) of Florida, with Comments on the Origin of the Smilodontini (Mammalia, Carnivora, Felidae)

South-central Florida’s latest Hemphillian Palmetto Fauna includes two machairodontine felids, the lion-sized Machairodus coloradensis and a smaller, jaguar-sized species, initially referred to Megantereon hesperus based on a single, relatively incomplete mandible. This made the latter the oldest record of Megantereon, suggesting a New World origin of the genus. Subsequent workers variously accepted or rejected this identification and biogeographic scenario. Fortunately, new material, which preserves previously unknown characters, is now known for the smaller taxon. The most parsimonious results of a phylogenetic analysis using 37 cranio-mandibular characters from 13 taxa place it in the Smilodontini, like the original study; however, as the sister-taxon to Megantereon and Smilodon. Accordingly, we formally describe Rhizosmilodon fiteae gen. et sp. nov. Rhizosmilodon, Megantereon, and Smilodon ( = Smilodontini) share synapomorphies relative to their sister-taxon Machairodontini: serrations smaller and restricted to canines; offset of P3 with P4 and p4 with m1; complete verticalization of mandibular symphysis; m1 shortened and robust with widest point anterior to notch; and extreme posterior “lean” to p3/p4. Rhizosmilodon has small anterior and posterior accessory cusps on p4, a relatively large lower canine, and small, non-procumbent lower incisors; all more primitive states than in Megantereon and Smilodon. The former also differs from Megantereon and Smilodon gracilis by having a very small mandibular flange. Rhizosmilodon is the oldest known member of the Smilodontini, suggesting that the tribe originated in North America. Two more derived, similar-sized species evolved in parallel during the Blancan, Megantereon hesperus and Smilodon gracilis. The former is rarer, known only from the north-central and northwestern US, and presumably dispersed into the Old World. The latter is known from the eastern and southern US, and dispersed into South America.
Agent-Based Approach to Plan Sensors Relocation in a Virtual Geographic Environment

Natural disasters represent a typical large-scale dynamic environments. As governments are more and more relying on sensors to monitor natural disasters, many related research problems have to be tackled, in particular the sensors relocation. In this paper we propose to plan the sensors relocation in a virtual geographic environment (VGE) which is synchronized with the real environment (where the sensors are deployed). In this VGE, we combine both simulation-based planning and agent-based planning to conduct relocation. We also apply an enhanced continual planning approach to face the environment dynamism. We use the wildfire fighting as an illustrative scenario.
Sample-based creation of peer summaries for efficient similarity search in scalable peer-to-peer networks

In this paper we introduce a simple yet experimentally convincing approach in the research field of source selection for content-based similarity search in P2P networks or, more concretely, in summary-based P2P systems. In these systems, summaries are used for data source selection when performing k-NN queries on distributed collections of documents represented by feature vectors. We introduce a new type of cluster-based summaries for source selection that can efficiently and cheaply be calculated and distributed in P2P networks. For the summaries generation, a very large number of sample points is used. Each peer in the network assigns its indexing data to their corresponding closest sample points and publishes its constructed summary. We evaluate the quality of these summaries when changing the number of sample points used in experiments on real-world image feature data obtained from a large crawl of the flickr web photo community and show that for higher numbers of sample points we achieve a better retrieval performance. Our experiments show that the proposed summaries yield four times better performance with respect to previous methods. Intuitively, there are some disadvantages to this approach due to the large size of the generated summaries. We show experimentally, that these disadvantages can easily be overcome due to the sparse nature of the generated summaries by simple compression techniques.
Joint Precoding and Equalisation Design Using Oversampled Filter Banks for Dispersive Channels with Correlated Noise

Oversampled filter banks (OSFBs) have recently been considered for channel coding since their redundancy introduced into the transmitted signal permits more freedom in the design of joint transmitter and receiver. Further specifically, they can be exploited to transmit over low noise subspaces or even mitigate dispersiveness of the channel. In this paper we propose a joint precoding and equalisation design using OSFBs, which find a compromise between transmitting over the low-noise subspace of channel noise's polyphase components, and the high-gain subspace of the channel's polyphase components. Polynomial building blocks are permitted and the minimisation of the mean square error (MSE) at the receiver output is achieved. We describe the design, and highlight the communalities and differences of this approach to existing methods. Simulation results show the benefit of the proposed system design compared to existing design approaches.
Title : A Motion Direction Map in Macaque V 2 Running Title : A Direction map in V 2

Acknowledgements: We thank Dr. Robert Friedman for technical assistance. SUMMARY In mammals, the perception of motion starts with direction-selective neurons in the visual cortex. Despite numerous studies in monkey V1 and V2 (primary and second visual cortex), there has been no evidence of direction maps in these areas. In the present study, we used optical imaging methods to study the organization of motion response in Macaque monkey V1 and V2. In contrast to the findings in other mammals (e.g. cats and ferrets), we found no direction maps in macaque V1. Robust direction maps, however, were found in V2 thick/pale stripes and avoided thin stripes. In many cases direction maps were located within thick stripes and exhibited pinwheel or linear organizations. The presence of motion maps in V2 points to a new found prominence of V2 in motion processing, either for contributing to motion perception in the dorsal pathway and/or for motion-cue dependent form perception in the ventral pathway.
Assessment of the integrity of human oocytes retrieved from cryopreserved ovarian tissue after xenotransplantation.

BACKGROUND Previous studies showed that immature oocytes stored in ovarian tissue could develop to the mature stage after transplantation. However, the quality and competency of the oocytes developed in xenografted ovarian tissue have never been investigated. As a pilot study to investigate this uncharted issue, we evaluated microtubule organization and chromatin configuration of human oocytes harvested from xenografted frozen-thawed ovarian tissue. METHODS Frozen-thawed human ovarian tissue was transplanted into severe combined immunodeficient mice. All animals were stimulated with gonadotrophin from 20 weeks after transplantation. Grafts were recovered 36 h after hCG administration. The oocytes were retrieved from the antral follicles (>2 mm diameter), cultured in vitro, stained for microtubule and chromatin localization. RESULTS Five oocytes from 21 female mice and seven oocytes from nine male mice were retrieved. Immunocytochemical examinations of these oocytes after in vitro maturation revealed only two developed to the metaphase II stage. Most oocytes were between prophase and metaphase with abnormal microtubule organization and chromatin configuration. CONCLUSIONS Immature oocytes in stored human ovarian tissue can grow to maturity in host animals after xenotransplantation. Retrieval of oocytes from the xenograft can be carried out and is reproducible. However, many oocytes, grown in host animals and further matured in vitro, showed aberrant microtubule organization and chromatin patterns.
Deterministic Sample Consensus with Multiple Match Hypotheses

RANSAC (Random Sample Consensus) is a popular and effective technique for estimating model parameters in the presence of outliers. Efficient algorithms are necessary for both frame-rate vision tasks and offline tasks with difficult data. We present a deterministic scheme for selecting samples to generate hypotheses, applied to data from feature matching. This method combines matching scores, ambiguity and past performance of hypotheses generated by the matches to estimate the probability that a match is correct. At every stage the best matches are chosen to generate a hypothesis. This method will therefore only spend time on bad matches when the best ones have proven themselves to be unsuitable. The result is a system that is able to operate very efficiently on ambiguous data and is suitable for implementation on devices with limited computing resources.
Comparing the effect of humanoid and human face for the spatial orientation of attention

The current study was designed to investigate how the automatic spatial orientation of attention induced by the perception of another agent's orientation of attention is modulated by the social nature of the other agent. Modified versions of the Posner task, using a real or schematic face with eyes or head looking toward the left or the right before a to-be-detected target appears on one side of the screen have been used to demonstrate a reduction of reaction time (RT) for target detection when the gaze is directed toward the target, even though the cue is not informative. We compared the effect of two agents, the humanoid robotic platform Nao and a real human, using head turn to cue the spatial orientation of attention. Our results reproduced the typical Posner effect, with reduced RT to valid compared to invalid spatial cues. RT increased when no spatial information was provided, interpreted as an increased difficulty to disengage from a direct gaze. RT was also increased when the robot was used instead of the human face and when the eyes of the stimuli were blacked out. Both effects were interpreted as resulting from an increased difficulty to disengage attention from the central stimulus because of its novelty. In all experiments, there was no interaction between cue validity and cue agent, implying that the exact nature of the human-like agent didn't have an effect on the automatic spatial orientation of attention. Altogether, our results imply that a humanoid face is as potent as a human face to trigger an automatic orientation of spatial attention.
NIP/DuoxA is essential for Drosophila embryonic development and regulates oxidative stress response

NIP/DuoxA, originally cloned as a protein capable of binding to the cell fate determinant Numb in Drosophila, was recently identified as a modulator of reactive oxygen species (ROS) production in mammalian systems. Despite biochemical and cellular studies that link NIP/DuoxA to the generation of ROS through the dual oxidase (Duox) enzyme, the in vivo function of NIP/DuoxA has not been characterized to date. Here we report a genetic and functional characterization of nip in Drosophila melanogaster. We show that nip is essential for Drosophila development as nip null mutants die at the 1(st) larval instar. Expression of UAS-nip, but not UAS-Duox, rescued the lethality. To understand the function of nip beyond the early larval stage, we generated GAL4 inducible UAS-RNAi transgenes. da(G32)-GAL4 driven, ubiquitous RNAi-mediated silencing of nip led to profound abnormality in pre-adult development, crinkled wing and markedly reduced lifespan at 29 degrees C. Compared to wild type flies, da-GAL4 induced nip-RNAi transgenic flies exhibited significantly reduced ability to survive under oxidative stress and displayed impaired mitochondrial aconitase function. Our work provides in vivo evidence for a critical role for nip in the development and oxidative stress response in Drosophila.
Path planning and global trajectory tracking control assistance to autonomous vehicle

This paper presents two contributions for the path planning for motion, and convergent global trajectory tracking, which assistance to autonomous vehicle. The path planning for motion is processed by two stages: road network detection and the shortest path estimation for vehicle motion. A road network is detected using the road map images based on image-processing techniques such color filter, segmentation technique. The road map images are collected from online free charge maps services. The road network method estimates not only the shape of road network but also the directed road network, which could not be estimated by the use of only aerial/satellite images. Some lack road segments, which are not annotated by map service, are detected using satellite images based on some filter techniques. The shortest path for motion is estimated using the Dijkstra combining with heuristic based on greedy breadth-first search technique. The road network is converted to the global coordinate, which provides a convenience for online auto-navigation task. The stable and robust control method is used for global trajectory tracking to navigate vehicle motion. The results of simulation and experiment demonstrate the effectiveness of this method under a large scene of the outdoor environments.
Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling

This paper presents a threat-assessment algorithm for general road scenes. A road scene consists of a number of objects that are known, and the threat level of the scene is based on their current positions and velocities. The future driver inputs of the surrounding objects are unknown and are modeled as random variables. In order to capture realistic driver behavior, a dynamic driver model is implemented as a probabilistic prior, which computes the likelihood of a potential maneuver. A distribution of possible future scenarios can then be approximated using a Monte Carlo sampling. Based on this distribution, different threat measures can be computed, e.g., probability of collision or time to collision. Since the algorithm is based on the Monte Carlo sampling, it is computationally demanding, and several techniques are presented to increase performance without increasing computational load. The algorithm is intended both for online safety applications in a vehicle and for offline data analysis.
The Relationship of Extraversion and Neuroticism with Risk Attitude, Risk Perception, and Return Expectations

We analyze the influence of individuals’ degree of extraversion and neuroticism on the determinants of their risk-taking behavior in investment decisions. As there are no studies that investigate the influence of personality traits on risk attitude, risk perception, and return expectations in investment decisions simultaneously, we provide a meaningful contribution to existing literature. We use a unique data set that contains 342 undergraduate business students’ questionnaire responses measuring the students’ degree of extraversion and neuroticism as well as their risk attitude, risk perception, and return expectations. Therefore, we are able to identify which determinants of risk-taking are influenced by extraversion and neuroticism and induce individuals to take investment risks. We find that more extraverted individuals are less risk averse, whereas more neurotic subjects are more risk averse. Beside these results, we find that more conscientious individuals are more risk averse and perceive investments in assets as more risky. Further research should consider individuals’ personality as an influence factor on the determinants of risk-taking behavior in investment decisions.