Miracle-dz/UniFAR-FTUs huggingface dataset:
'Stuff' Happens: A Theoretical Framework for Internal IS Service Recovery

ABSTRACT The Information Service Function (ISF) delivers IS services to internal customers (employees) to assist them in their information- and technology-related tasks. IS service failures are an inevitable event and the ISF must enact processes to recover from these failures. This article proposes a theoretical framework integrating literature from marketing, operations, and IT to depict an integrated model of internal IS service recovery where perceived service justice mediates the relationship between Internal IS Service Recovery Extensiveness (IISSRE) and IS service quality as well as predicts internal IS customer satisfaction and continued commitment to the ISF. This study contributes by establishing a theoretical foundation for IS service recovery that is uniquely contextualized for internal IS settings and by proposing a new construct that captures different aspects of the IS service recovery remedy. A list of propositions to guide future research is developed.
Acute Viral Respiratory Infection Rapidly Induces a CD8+ T Cell Exhaustion–like Phenotype

Acute viral infections typically generate functional effector CD8+ T cells (TCD8) that aid in pathogen clearance. However, during acute viral lower respiratory infection, lung TCD8 are functionally impaired and do not optimally control viral replication. T cells also become unresponsive to Ag during chronic infections and cancer via signaling by inhibitory receptors such as programmed cell death-1 (PD-1). PD-1 also contributes to TCD8 impairment during viral lower respiratory infection, but how it regulates TCD8 impairment and the connection between this state and T cell exhaustion during chronic infections are unknown. In this study, we show that PD-1 operates in a cell-intrinsic manner to impair lung TCD8. In light of this, we compared global gene expression profiles of impaired epitope-specific lung TCD8 to functional spleen TCD8 in the same human metapneumovirus–infected mice. These two populations differentially regulate hundreds of genes, including the upregulation of numerous inhibitory receptors by lung TCD8. We then compared the gene expression of TCD8 during human metapneumovirus infection to those in acute or chronic lymphocytic choriomeningitis virus infection. We find that the immunophenotype of lung TCD8 more closely resembles T cell exhaustion late into chronic infection than do functional effector T cells arising early in acute infection. Finally, we demonstrate that trafficking to the infected lung alone is insufficient for TCD8 impairment or inhibitory receptor upregulation, but that viral Ag–induced TCR signaling is also required. Our results indicate that viral Ag in infected lungs rapidly induces an exhaustion-like state in lung TCD8 characterized by progressive functional impairment and upregulation of numerous inhibitory receptors.
A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The results of the method are compared to those from a Time Delay Neural Network and the C4.5 decision tree algorithm.
Walking on non-planar surfaces using an inverse dynamic stack of tasks

This paper presents a method to handle walking on non-planar surfaces. The trajectories of the center of mass and the next position of the foot are given by a pattern generator. Then, an inverse dynamics control scheme with a quadratic programming optimization solver is used to let the foot go from its initial to its final position, controlling also the center of mass and the waist. The solver can handle an arbitrary number of contact points. When the swinging foot is going down, collision points are detected and they are added as contact points to the model as soon as they appear. If there are three or more contact points, the foot can safely step, but if there are one or two contact points, the foot rotates properly to generate the largest support polygon. Using this heuristic, the foot can stand on non-planar surfaces. Simulation results of the HRP-2 robot walking on a surface with obstacles are presented.
Non-linear factor model for asset selection using multi objective genetic programming

Investors varywith respect to their expected return and aversion to associated risk, and hence also vary in their performance expectations of the stock market portfolios they hold. In this work we present an empirical study of the use of the Multiobjective genetic programming (MOGP) technique for a real world problem of portfolio optimisation on UK FTSE-100 stocks. The MOGP evolves a nonlinear factor model of technical factors for asset ranking, and provides visual insight into the risk return trade-off involved in discovering an approximation to the risk-return efficient frontier of a portfolio optimization problem. We provide preliminary analysis of the set of factors chosen in the evolved solutions. We find evidence for the effect on risk-adjusted returns of firm size, return on equity and cash yield (and little or no evidence for the book to market ratio).
Exploring influential nodes using multi-attribute information

In recent years there has been greatly increased interests in finding influential nodes in social networks. For a long time, PageRank has been widely used and has also been adopted as a solid baseline in the field of influence maximization. However, there are all kinds of interactions among social entities which contributes to varieties of relationships between nodes in information diffusion graphs of social networks. We refer to these relationships as multi attributed relationships and examples include follow, comment in Facebook, coauthor, citation in collaboration networks and so on. PageRank and its variants use just one kind of relationship and haven't taken multi relationships and the differences between relationships into consideration. This results in a crude simplification of the real world and leads to poor performance. To address this problem, in this paper, we construct a multi-attribute information diffusion graph(MAID graph) by integrating different relationships. A variant of PageRank referred as multi-attribute Rank(MA-Rank) is proposed. The experimental results show that our proposed method achieves higher accuracy and efficiency in finding influential nodes comparing with PageRank and LeaderRank.
Precision-bounded Access Control for Privacy-preserving Data Streams

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. When the sensitive stream data is shared without a PPM the privacy can be compromised. The PPM meets privacy requirements, e.g., k-anonymity or l-diversity by generalization of stream data. This imprecision introduced by generalization can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to achieve better accuracy can lead to false negatives if the tuples slide out of the window when a sliding-window query predicate is evaluated for access control mechanism. To set a threshold on the loss of precision, access control mechanism defines the imprecision bound for each query. The challenge is to optimize the time duration for which the stream data is held by PPM so that the imprecision bounds for the maximum possible number of queries are met. In our formulation of the aforementioned problem we present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm. Experiments demonstrate that the proposed heuristic provides better precision as compared to the minimum or maximum delay heuristics.
On the Structure, Covering, and Learning of Poisson Multinomial Distributions

An (n, k)-Poisson Multinomial Distribution (PMD) is the distribution of the sum of n independent random vectors supported on the set Bk={e1,. ,ek} of standard basis vectors in Rk. We prove a structural characterization of these distributions, showing that, for all ε > 0, any (n, k)-Poisson multinomial random vector is ε-close, in total variation distance, to the sum of a discretized multidimensional Gaussian and an independent (poly(k/ε), k)-Poisson multinomial random vector. Our structural characterization extends the multi-dimensional CLT of Valiant and Valiant, by simultaneously applying to all approximation requirements ε. In particular, it overcomes factors depending on log n and, importantly, the minimum Eigen value of the PMD's covariance matrix. We use our structural characterization to obtain an ε-cover, in total variation distance, of the set of all (n, k)-PMDs, significantly improving the cover size of Daskalakis and Papadimitriou, and obtaining the same qualitative dependence of the cover size on n and ε as the k=2 cover of Daskalakis and Papadimitriou. We further exploit this structure to show that (n, k)-PMDs can be learned to within ε in total variation distance from Õk(1/ε) samples, which is near-optimal in terms of dependence on ε and independent of n. In particular, our result generalizes the single-dimensional result of Daskalakis, Diakonikolas and Servedio for Poisson binomials to arbitrary dimension. Finally, as a corollary of our results on PMDs, we give a Õk(1/ε2) sample algorithm for learning (n, k)-sums of independent integer random variables (SIIRVs), which is near-optimal for constant k.
Minimal residual disease detection of leukemic cells in ovarian cortex by eight-color flow cytometry.

STUDY QUESTION How can leukemic cells be detected in cryopreserved ovarian tissue? SUMMARY ANSWER Multicolor flow cytometry (FCM) is useful to evaluate the presence of viable leukemic cells in the ovarian cortex with a high specificity and a robust sensitivity. WHAT IS KNOWN ALREADY Storing ovarian tissue is an option to preserve fertility before gonadotoxic radiotherapy or chemotherapy treatments. However, transplantation of cryopreserved ovarian cortex to women cured of leukemia is currently not possible due to the risk of cancer re-seeding. STUDY DESIGN, SIZE, DURATION We developed an automated ovarian cortex dissociation technique and we used eight-color FCM to identify leukemic cells with a series of dilutions added to ovarian single cell suspensions obtained from healthy cortex. PARTICIPANTS/MATERIALS, SETTINGS, METHODS Healthy ovarian cortex originated from women between 23 and 39 years of age undergoing laparoscopic ovarian drilling for polycystic ovary syndrome. Blood or bone marrow cells were collected in acute lymphoblastic leukemia (ALL) patients at diagnosis. MAIN RESULTS AND THE ROLE OF CHANCE The tissue dissociation technique yield was 1.83 ± 1.49 × 10(6) viable nucleated cells per 100 mg of ovarian cortex. No cell exhibiting a leukemic phenotype was present in the normal ovarian cortex. Added leukemic cells were detected using their leukemia-associated phenotype up to a dilution of 10(-4). When specific gene rearrangements were present, they were detected by real-time quantitative PCR at the same dilution. The ovarian cortex from two leukemia patients was then used, respectively, as positive and negative controls. LIMITATIONS, REASONS FOR CAUTION Making available minimal residual disease (MRD) detection techniques (multicolor FCM, PCR and xenograft), that can be used either alone or together, is essential to add a fail-safe oncological dimension to pre-autograft monitoring. WIDER IMPLICATIONS OF THE FINDINGS This approach can be performed on fresh ovarian tissue during cryopreservation or on frozen/thawed tissue before reimplantation and it is currently the only available technique in cases of ALL where no molecular markers are identified. This new perspective should lead to studies on ovarian tissue from leukemia patients, for whom the presence of MRD should be established before autograft. STUDY FUNDINGS/COMPETING INTEREST(S) The study was supported by the BioMedicine Agency, the Committee of the League against Cancer, the Besançon University Hospital, DGOS/INSERM/INCa and the regional Council of Franche-Comté. There were no conflicts of interest to declare.
Adaptive Noise Cancellation Using Accelerometers for the PPG Signal from Forehead

For the upcoming ubiquitous computing environments in the u-health areas involve the measurement of physiological signals in the daily life. However, the measurement of those signals, such as the photoplethysmography (PPG), and the electrocardiogram (ECG), requires being still tight during the measurement in order to get the accurate result preventing noises caused by the casual movement. In this paper, we propose a method to obtain the accurate physiological signals in the situation where the little movement is allowed. By measuring PPG and motion signals at the forehead during in motion, we calibrate the distorted PPG signal with the motion signals. We show that the calibrated PPG signal is accurate enough by comparing with the result that is measured at the finger without any movement.
A Nonuniform Spatial Rain Attenuation Model for Troposcatter Communication Links

Troposcatter communication can be used as a communication medium for beyond-line-of-sight (BLOS) links. However, wave propagation in troposphere shows significant dependence on hydro-meteors: especially rain. Therefore, the main motivation of this paper is to develop a rain attenuation model for troposcatter communications, which can model nonuniform multiple rain cells for the first time. At the end, we present simulation results for the amount of rain loss and the distribution of maximum data rates under rain in troposcatter links.
DNA footprint analysis of the transcriptional activator proteins NodD1 and NodD3 on inducible nod gene promoters

The Rhizobium meliloti nodD1 and nodD3 gene products (NodD1 and NodD3) are members of the lysR-nodD gene regulator family. They are functionally distinct in that NodD1 transcriptionally activates other nod genes in the presence of a flavonoid inducer such as luteolin, while NodD3 is capable of activating nod gene expression at high levels in the absence of inducer. NodD1 and NodD3 are DNA-binding proteins which interact with DNA sequences situated upstream of the transcription initiation sites of at least three sets of inducible nod genes. We report the footprinting of NodD1- and NodD3-DNA complexes with both DNase I and the 1,10-phenanthroline-copper ion reagent. NodD1 and NodD3 both interacted with the nodABC, nodFE, and nodH promoters and protected from cleavage an extensive piece of DNA, including the nod box, from approximately -20 to -75 from the transcription start site for each of the three promoters. The constitutively activating protein NodD3 displayed an additional hypersensitive cleavage site in its footprint compared with NodD1.
A Low Cost 3 D Acquiring System for Mushroom Robot Based on Webcam and Line Laser

This paper presents a low cost 3D acquiring system for mushroom robot based on webcam and line laser. The system comprises Webcam, semiconductor line laser, motion platform and data process unit. The system can get the 3D information of the Pleurotus eryngii in bottle based on structured light scanning theory. Field test shows the accuracy of the height is less than 2mm and it can be use to locate the buds in the bottle correctly. It has the potention to fulfill the requirement of the location of the mushroom for thinning bud robot. Copyright © 2014 IFSA Publishing, S. L.
Barriers in Initiating Insulin Treatment in Type 2 Diabetes Mellitus among Physicians in Wilayat of Bowsher in Oman

Result: Initiating insulin therapy was shown to be affected by physician, patient and system related factors. Each barrier was identified by highlighting the main categories. Physician factors which included physician knowledge, beliefs, fears and concerns. Patient factor included patient education, compliance and socio-economic status. System factors included short consultation time and lack of resources.
MS-DFA: Multiple-Stride Pattern Matching for Scalable Deep Packet Inspection

As network speeds continue to increase, so does the need for scalable pattern matching for deep packet scanning applications such as signature-based network intrusion detection. Multiple-stride deterministic finite automaton (DFA) increases the performance of pattern matching, because they allow multiple bytes of a packet to be scanned simultaneously. However, traditional multiple-stride DFA either rely on specific hardware for parallel comparison or have a huge memory requirements due to state explosion. In this paper, we present a high throughput, multiple-stride pattern-matching architecture that requires a small storage cost and no specific hardware. The basic idea is to group DFA states/transitions into three coarse-grained and variable-size blocks, so that each individual block can employ different-specific methods to optimize storage requirements and performance. The blocks are naturally identified based on basic observations of DFA characteristics: prefix, linear trie and state dependencies. The performance evaluation is done using the Snort pattern sets. We show that multi-byte striding DFA achieves multi Gb/s pure content inspection in software, while utilizing <3 bytes per pattern character.
Robust watershed segmentation using the wavelet transform

The watershed transform has been used for image segmentation relying mostly on image gradients. However, background noise tends to produce spurious gradients, that cause over-segmentation and degrade the output of the watershed transform. Also, low-contrast edges produce gradients with small magnitudes, which may cause different regions to be erroneously merged. In this paper, a new technique is presented to improve the robustness of watersheds segmentation, by reducing the undesirable over-segmentation. A redundant wavelet transform is used to denoise the image and enhance the edges in multiple resolutions, and the image gradient is estimated with the wavelet transform. The watershed transform is then applied to the obtained gradient image, and segmented regions that do not satisfy specific criteria are removed.
On-line error monitoring for several data structures

We present several examples of programs which efficiently monitor the answers from queries performed on data structures to determine if any errors are present. Our paper includes the first efficient on-line error monitor for a data structure designed to perform nearest neighbor queries. Applications of nearest neighbor queries are extensive and include learning, categorization, speech processing, and data compression. Our paper also discusses on-line error monitors for priority queues and splittable priority queues. On-line error monitors immediately detect if an error is present in the answer to a query. An error monitor which is not on-line may delay the time of detection until a later query is being processed which may allow the error to propagate or may cause irreversible state changes. On-line monitors can allow a more rapid and accurate response to an error. <<ETX>>
Modeling and control of interleaved buck converter for electric vehicle fast chargers

This paper describes in details a three leg interleaved buck converter designed to electric vehicle fast charge stations. A different approach for the current control is presented, in which total output current control is decoupled from the circulating current control. In addition, the impact of the sampling and the discrete model are discussed aiming a discrete time implementation. Finally, the theoretical results are validated in a hardware in the loop (HIL) implementation.
BOEMIE Ontology-Based Text Annotation Tool

The huge amount of the available information in the Web creates the need of effective information extraction systems that are able to produce metadata that satisfy users information needs. The development of such systems, in the majority of cases, depends on the availability of an appropriately annotated corpus in order to learn extraction models. The production of such corpora can be significantly facilitated by annotation tools that are able to annotate, according to a defined ontology, not only named entities but most importantly relations between them. This paper describes the BOEMIE ontology-based annotation tool which is able to locate blocks of text that correspond to specific types of named entities, fill tables corresponding to ontology concepts with those named entities and link the filled tables based on relations defined in the domain ontology. Additionally, it can perform annotation of blocks of text that refer to the same topic. The tool has a user-friendly interface, supports automatic pre-annotation, annotation comparison as well as customization to other annotation schemata. The annotation tool has been used in a large scale annotation task involving 3,000 web pages regarding athletics. It has also been used in another annotation task involving 503 web pages with medical information, in different languages.
Rising from the Sea: Correlations between Sulfated Polysaccharides and Salinity in Plants

High salinity soils inhibit crop production worldwide and represent a serious agricultural problem. To meet our ever-increasing demand for food, it is essential to understand and engineer salt-resistant crops. In this study, we evaluated the occurrence and function of sulfated polysaccharides in plants. Although ubiquitously present in marine algae, the presence of sulfated polysaccharides among the species tested was restricted to halophytes, suggesting a possible correlation with salt stress or resistance. To test this hypothesis, sulfated polysaccharides from plants artificially and naturally exposed to different salinities were analyzed. Our results revealed that the sulfated polysaccharide concentration, as well as the degree to which these compounds were sulfated in halophytic species, were positively correlated with salinity. We found that sulfated polysaccharides produced by Ruppia maritima Loisel disappeared when the plant was cultivated in the absence of salt. However, subjecting the glycophyte Oryza sativa Linnaeus to salt stress did not induce the biosynthesis of sulfated polysaccharides but increased the concentration of the carboxylated polysaccharides; this finding suggests that negatively charged cell wall polysaccharides might play a role in coping with salt stress. These data suggest that the presence of sulfated polysaccharides in plants is an adaptation to high salt environments, which may have been conserved during plant evolution from marine green algae. Our results address a practical biological concept; additionally, we suggest future strategies that may be beneficial when engineering salt-resistant crops.