Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Kernel Log Analysis for Robotics Application

Humanoid robot systems are composed of an assortment of hardware and software components, and they have complex embedded systems and real-time properties. These features make it difficult to isolate or to identify a fault in a short period of time even though such systems are expected to recover quickly in order to avoid any harmful behaviors that may cause harm to the users. This paper presents a new technological method for detecting errors in real-time applications online through the technique of online kernel log monitoring and analysis method. The contributions of approaches are that we present a method for kernel log analysis based on a state transition model of scheduling tasks, and apply it to the kernel logs to detect anomaly behavior of real-time tasks. In order to reduce the analysis overhead of huge volumes of data, we propose a new system that places the kernel log analysis engine on a separate core from the one that runs the kernel log monitoring process. Based on this system, we provide a framework for writing analyzers to detect errors incrementally. In our system, these components work together to solve the problems highlighted by root cause analysis in robotic systems. We applied the proposed system to actual robotics systems and successfully detected several deviated errors and faults that include a serious priority inversion that was not detected in over 10 years of operation in the actual operating system.
A mesoscale geophysical capability/suitability model for vitis vinifera vineyard site selection in the North Carolina Piedmont Triad region, case study: Rockingham County NC

The North Carolina wine industry is growing at a fast pace; many new vineyards are being planted with European varieties. Vitis vinifera varieties in general are the most challenging species of grape grown and the cost of vineyard establishment is high. While many grapes are native to North Carolina, V. vinifera are not; they require considerable effort to consistently produce good quality grapes for wine making. The challenges of growing V. vinifera in this region are primarily due to the warm humid climate which encourages the presence of many insect pests as well as a broad host of viral, bacterial, and fungal diseases. When these risks are considered alongside the possibilities of late spring frost and heavy rain from harvest time tropical systems, it is apparent that there is a need for a system which mitigates these risks and helps guide the choices for vineyard location. The model produced in this research was designed to help guide site selection for V. vinifera vineyard sites in the North Carolina Piedmont. The area of interest for this case study is Rockingham County North Carolina. The primary goal is to give the prospective vineyard operator every opportunity for success by choosing the best possible place to establish a vineyard. This is accomplished using a model based on the science of Geographic Information Systems (GIS) along with predictive geophysical parameters. The model consists of four physical sub model composites which represent the capability/suitability of: climate, land cover, soil, and topography. Using the concepts of map algebra, the four sub model composites are combined to produce the final output that summarizes the physical site suitability of the study area at a spatial resolution of 10 meters.
A simple framework to quantitatively describe monthly precipitation and temperature climatology

Climate descriptors and classifications are vital for ordering past, current and future climatic conditions. Yet, these parsimonious descriptors of climatic conditions only capture specific aspects of this climate signal, and lose all other information available in the observations. As a result, climate descriptions are often not physically insightful when they are applied in other studies. In this study, we show that a sinusoidal function with an annual period can adequately describe the vast majority of monthly precipitation and temperature climates around the world. This finding allows us to synthesize intra‐annual monthly precipitation and temperature climatology using five indices that are easy to interpret. The indices describe (1) the mean precipitation rate ( P‾ ), (2) the mean temperature ( T‾ ), (3) the seasonal precipitation amplitude (δP), (4) the seasonal temperature amplitude (ΔT) and (5) the phase difference between the precipitation and temperature regimes (sd). The combination of the five indices describes the relative time series of precipitation and temperature climatology, in contrast to earlier proposed similarity indices that only capture specific aspects of these time series. We demonstrate how the framework can reproduce many earlier proposed indices and classifications, and provide an example how the framework can be used to classify regions. We argue that the framework provides comprehensive insight into global climatology and can function as a quantitative conceptual basis for climate descriptions among different sciences.
Robust security framework for DVB‐RCS satellite networks (RSSN)

Satellites represent a solution for Internet access in locations with no other telecom infrastructures, for example, on high mobility platforms such as planes, ships or high‐speed trains, or for disaster recovery applications. However, due to peculiar characteristics, satellite networks are prone to different security threats. In this paper, we introduce a novel, robust security architecture for securing digital video broadcasting‐return channel via satellite satellite networks, inspired by the robust security mechanism available in the Institute of Electrical and Electronic Engineers (IEEE) 802.11i wireless local area network. We propose an efficient authentication and key management mechanism, which is exploited through three round‐trips only, demonstrating that it is as secure as IEEE 802.11i. Furthermore, the simulation results show that the proposed security framework needs a very small data overhead and shows better performance than internet protocol security (IPSec), which is commonly used as an end‐to‐end security solution over internet protocol satellite networks. Copyright © 2015 John Wiley & Sons, Ltd.
Carbon in U.S. forests and wood products, 1987-1997: state-by-state estimates

Estimated changes in carbon stocks are reported for the forests and wood products of the 50 U.S. States. Carbon stocks on forest land and in harvested wood products increased between 1987 and 1997 at an annual rate of 190 million metric tons. Most of this increase was in biomass, followed closely by wood products and landfills. Changes in land use since 1987 caused a small decrease in carbon stocks, but this loss was offset by large gains on existing forest land. The East had the greatest gain in carbon stocks with smaller gains estimated for the West. Most of the individual states showed increases in ecosystem and wood-products carbon. Observed changes were attributed to distinct regional and local factors, e.g., timber production, land-use change, and natural disturbance.
Human Language Technologies : The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics Demonstration Session

Automatic interpretation of documents is hampered by the fact that language contains terms which have multiple meanings. These ambiguities can still be found when language is restricted to a particular domain, such as biomedicine. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities but are often only able to identify the meanings for a small set of ambiguous terms. DALE (Disambiguation using Automatically Labeled Examples) is a supervised WSD system that can disambiguate a wide range of ambiguities found in biomedical documents. DALE uses the UMLS Metathesaurus as both a sense inventory and as a source of information for automatically generating labeled training examples. DALE is able to disambiguate biomedical documents with the coverage of unsupervised approaches and accuracy of supervised methods.
Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty

Investment decision making is already difficult for any diverse group of actors with different priorities and views. But the presence of deep uncertainties linked to climate change and other future conditions further challenges decision making by questioning the robustness of all purportedly optimal solutions. While decision makers can continue to use the decision metrics they have used in the past (such as net present value), alternative methodologies can improve decision processes, especially those that lead with analysis and end in agreement on decisions. Such"Agree-on-Decision"methods start by stress-testing options under a wide range of plausible conditions, without requiring us to agree ex ante on which conditions are more or less likely, and against a set of objectives or success metrics, without requiring us to agree ex ante on how to aggregate or weight them. As a result, these methods are easier to apply to contexts of large uncertainty or disagreement on values and objectives. This inverted process promotes consensus around better decisions and can help in managing uncertainty. Analyses performed in this way let decision makers make the decision and inform them on (1) the conditions under which an option or project is vulnerable; (2) the tradeoffs between robustness and cost, or between various objectives; and (3) the flexibility of various options to respond to changes in the future. In doing so, they put decision makers back in the driver's seat. A growing set of case studies shows that these methods can be applied in real-world contexts and do not need to be more costly or complicated than traditional approaches. Finally, while this paper focuses on climate change, a better treatment of uncertainties and disagreement would in general improve decision making and development outcomes.
Dialog Input Ranking in a Multi-Domain Environment Using Transferable Belief Model

This paper presents results of using belief functions to rank the list of candidate information provided in a noisy dialogue input. The information under consideration is the intended task to be performed and the information provided for the completion of the task. As an example, we use the task of information access in a multi-domain dialogue system. Currently, the system contains knowledge of ten different domains. Callers calling in are greeted with an open-ended “How may I help you? ” prompt (Thomson and Wisowaty, 1999; Chu-Carroll and Carpen-ter, 1999; Gorin et al., 1997). After receiving a reply from the caller, we extract word evidences from the recognized utterances. By using transferable belief model (TBM), we in turn determine the task that the caller intends to perform as well as any information provided.
Errors in patient perception of caloric deficit required for weight loss—observations from the Diet Plate Trial *

Persons with obesity may be poor estimators of caloric content of food. Health care professionals encourage patients to consult nutritional labels as one strategy to assess and restrict caloric intake. Among subjects enrolled in a weight loss clinical trial, the objective is to determine the accuracy of subjects' estimates of caloric deficit needed to achieve the desired weight loss. A 6‐month controlled trial demonstrated efficacy of a portion control tool to induce weight loss in 130 obese people with type 2 diabetes. All subjects had previously received dietary teaching from a dietician and a nurse. At baseline, patients were asked how much weight they would like to lose and to quantitatively estimate the caloric deficit required to achieve this weight loss. The stated amount of weight loss desired ranged from 4.5 to 73 kg, with an average of 26.6 kg (n = 127 respondents). Only 30% of participants were willing to estimate the required caloric deficit to lose their target weight. Subjects' per kilograms estimate of caloric deficit required ranged from 0.7 to 2 000 000 calories/kg with a median of 86 calories/kg. Nearly half of subjects (47.4%) underestimated the total required caloric deficit to achieve their target weight loss by greater than 100 000 calories. Despite attendance at a diabetes education centre, this population of obese individuals had a poor understanding of the quantitative relationship between caloric deficit and weight loss. Educational initiatives focused upon quantitative caloric intake and its impact on weight change may be needed to assist obese patients in setting appropriate weight loss goals and achieving the appropriate daily caloric restriction required for success.
Exogenous calreticulin improves diabetic wound healing

A serious consequence of diabetes mellitus is impaired wound healing, which largely resists treatment. We previously reported that topical application of calreticulin (CRT), an endoplasmic reticulum chaperone protein, markedly enhanced the rate and quality of wound healing in an experimental porcine model of cutaneous repair. Consistent with these in vivo effects, in vitro CRT induced the migration and proliferation of normal human cells critical to the wound healing process. These functions are particularly deficient in poor healing diabetic wounds. Using a genetically engineered diabetic mouse (db/db) in a full‐thickness excisional wound healing model, we now show that topical application of CRT induces a statistically significant decrease in the time to complete wound closure compared with untreated wounds by 5.6 days (17.6 vs. 23.2). Quantitative analysis of the wounds shows that CRT increases the rate of reepithelialization at days 7 and 10 and increases the amount of granulation tissue at day 7 persisting to day 14. Furthermore, CRT treatment induces the regrowth of pigmented hair follicles observed on day 28. In vitro, fibroblasts isolated from diabetic compared with wild‐type mouse skin and human fibroblasts cultured under hyperglycemic compared with normal glucose conditions proliferate and strongly migrate in response to CRT compared with untreated controls. The in vitro effects of CRT on these functions are consistent with CRT's potent effects on wound healing in the diabetic mouse. These studies implicate CRT as a potential powerful topical therapeutic agent for the treatment of diabetic and other chronic wounds.
qPMS7: A Fast Algorithm for Finding (ℓ, d)-Motifs in DNA and Protein Sequences

Detection of rare events happening in a set of DNA/protein sequences could lead to new biological discoveries. One kind of such rare events is the presence of patterns called motifs in DNA/protein sequences. Finding motifs is a challenging problem since the general version of motif search has been proven to be intractable. Motifs discovery is an important problem in biology. For example, it is useful in the detection of transcription factor binding sites and transcriptional regulatory elements that are very crucial in understanding gene function, human disease, drug design, etc. Many versions of the motif search problem have been proposed in the literature. One such is the -motif search (or Planted Motif Search (PMS)). A generalized version of the PMS problem, namely, Quorum Planted Motif Search (qPMS), is shown to accurately model motifs in real data. However, solving the qPMS problem is an extremely difficult task because a special case of it, the PMS Problem, is already NP-hard, which means that any algorithm solving it can be expected to take exponential time in the worse case scenario. In this paper, we propose a novel algorithm named qPMS7 that tackles the qPMS problem on real data as well as challenging instances. Experimental results show that our Algorithm qPMS7 is on an average 5 times faster than the state-of-art algorithm. The executable program of Algorithm qPMS7 is freely available on the web at http://pms.engr. uconn.edu/downloads/qPMS7.zip. Our online motif discovery tools that use Algorithm qPMS7 are freely available at http://pms.engr. uconn.edu or http://motifsearch.com.
A Globally Convergent Matricial Algorithm for Multivariate Spectral Estimation

In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab's PEM and Matlab's N4SID.
Adaptation of Hepatitis C Virus to Mouse CD81 Permits Infection of Mouse Cells in the Absence of Human Entry Factors

Hepatitis C virus (HCV) naturally infects only humans and chimpanzees. The determinants responsible for this narrow species tropism are not well defined. Virus cell entry involves human scavenger receptor class B type I (SR-BI), CD81, claudin-1 and occludin. Among these, at least CD81 and occludin are utilized in a highly species-specific fashion, thus contributing to the narrow host range of HCV. We adapted HCV to mouse CD81 and identified three envelope glycoprotein mutations which together enhance infection of cells with mouse or other rodent receptors approximately 100-fold. These mutations enhanced interaction with human CD81 and increased exposure of the binding site for CD81 on the surface of virus particles. These changes were accompanied by augmented susceptibility of adapted HCV to neutralization by E2-specific antibodies indicative of major conformational changes of virus-resident E1/E2-complexes. Neutralization with CD81, SR-BI- and claudin-1-specific antibodies and knock down of occludin expression by siRNAs indicate that the adapted virus remains dependent on these host factors but apparently utilizes CD81, SR-BI and occludin with increased efficiency. Importantly, adapted E1/E2 complexes mediate HCV cell entry into mouse cells in the absence of human entry factors. These results further our knowledge of HCV receptor interactions and indicate that three glycoprotein mutations are sufficient to overcome the species-specific restriction of HCV cell entry into mouse cells. Moreover, these findings should contribute to the development of an immunocompetent small animal model fully permissive to HCV.
The Stone Source of Predynastic Basalt Vessels: Mineralogical Evidence for Quarries in Northern Egypt

Abstract About 22% of all Predynastic stone vessels are basaltic in composition. As a result of their nondescript petrography the raw-material source of these common artefacts is challenging to determine. Electron microprobe analyses of pyroxene and plagioclase in ≥0·0001 g samples from nine Predynastic basalt vessels indicate compositions consistent with an origin from Haddadin basalt outcrops west of Cairo. The raw material or finished vessels were transported 600 km up the Nile to Abydos and Qena, bypassing closer basalt sources in Middle Egypt. These results and stylistic considerations indicate that a major vessel manufacturing industry existed in Northern Egypt, but the location of the workshops has yet to be found. Quarrying of Haddadin basalt for vessels spanned at least 900 years. Accessibility and breakage characteristics of the basalt help account for its prolonged and widespread use, but basalts in Middle Egypt may have similar properties suggesting that either tradition or some unidentified physical quality was also important. This study underscores the utility of the electron microprobe for fingerprinting basalt flows. The factor equations and discrimination diagrams allow the stone source for Egyptian basaltic artefacts to be determined on a more routine basis. It is not necessary to re-sample the quarries and obtain hundreds of mineral analyses to determine a source. Miniscule amounts of material are required, making the method applicable even to extremely valuable antiquities.
Sparse inverse covariance matrix estimation using quadratic approximation

The l1 regularized Gaussian maximum likelihood estimator has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program. In contrast to other state-of-the-art methods that largely use first order gradient information, our algorithm is based on Newton's method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and also present experimental results using synthetic and real application data that demonstrate the considerable improvements in performance of our method when compared to other state-of-the-art methods.
Who’s the Favourite? – A Bivariate Poisson Model for the UEFA European Football Championship 2016

Many approaches that analyze and predict the results of soccer matches are based on two independent pairwise Poisson distributions. The dependence between the scores of two competing teams is simply displayed by the inclusion of the covariate information of both teams. One objective of this article is to analyze if this type of modeling is appropriate or if an additional explicit modeling of the dependence structure for the joint score of a soccer match needs to be taken into account. Therefore, a specific bivariate Poisson model for the two numbers of goals scored by national teams competing in UEFA European football championship matches is fitted to all matches from the three previous European championships, including covariate information of both competing teams. A boosting approach is then used to select the relevant covariates. Based on the estimates, the current tournament is simulated 1,000,000 times to obtain winning probabilities for all participating national teams.
Resistance to chemotherapy: patient variability and cellular heterogeneity.

The issue of resistance to targeted drug therapy is of pressing concern, as it constitutes a major barrier to progress in managing cancer. One important aspect is the role of stochasticity in determining the nature of the patient response. We examine two particular experiments. The first measured the maximal response of melanoma to targeted therapy before the resistance causes the tumor to progress. We analyze the data in the context of a Delbruck-Luria type scheme, wherein the continued growth of preexistent resistant cells are responsible for progression. We show that, aside from a finite fraction of resistant cell-free patients, the maximal response in such a scenario would be quite uniform. To achieve the measured variability, one is necessarily led to assume a wide variation from patient to patient of the sensitive cells' response to the therapy. The second experiment is an in vitro system of multiple myeloma cells. When subject to a spatial gradient of a chemotherapeutic agent, the cells in the middle of the system acquire resistance on a rapid (two-week) timescale. This finding points to the potential important role of cell-to-cell differences, due to differing local environments, in addition to the patient-to-patient differences encountered in the first part. See all articles in this Cancer Research section, "Physics in Cancer Research. "