Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability-Constrained Die Stacking Order in 3

3D integrated circuits (3DICs) with through-silicon vias (TSVs) are an important direction for semiconductor-based products and “More than Moore” scaling. However, 3DICs bring simultaneous challenges of reliability (power and temperature in stacks of thinned die) as well as variability (performance and power) in advanced technology nodes. In this paper, we study variability-reliability interactions and optimizations in 3DICs. Initial motivating studies show that in the presence of manufacturing variability, different die stacking orders can lead to as much as 2 years (∼44%) difference in MTTF of a 3DIC stack. We study MTTF-driven die-stacking optimization with consideration of variability, and propose a “rule-of-thumb” guideline for stacking optimization to improve peak temperature as well as reliability in 3DICs. We also propose integer-linear programming (ILP) methods for reliability-driven die-stacking optimization. Our methods can achieve ∼7% and ∼28% improvement in average and minimum MTTF, respectively, of 3DICs; we also achieve ∼3% improvement in performance under fixed reliability constraints. Our stacking optimizations can help improve 3DIC product yields under reliability requirements. Our research also yields the notable observation that a limited amount of manufacturing variation can “help” improve 3DIC product reliability when die-stacking optimization is applied.
AN EVALUATION OPTIMIZATION APPROACH OF IAAS RESOURCE DISTRIBUTION BASED ON GENETIC ALGORITHM

In cloud computing, IaaS (Infrastructure as a Service) is the foundation of cloud service applications. It enables problems like resource allocation scheduling and bearing capacity to be further optimally solved. Therefore, a genetic optimized IaaS resource optimization evaluation approach is presented. This approach abstractly defines its 7 factors (properties) including usability of server, network performances, load balancing, anomaly notification mechanism, response time support, trusted security and measure payment, and explains quantitative calculation and expressions of each factor. And finally, tested by CloudSim, this approach is indicated to be feasible and effective for resource allocation
Construction of maps for soil recycling in regional infrastructural works integrating soil-quality laws

Abstract A soil-quality map is at present an important tool to integrate laws on soil quality with regional infrastructural works. Basic data are commonly available, but soil quality is an indicator that has to be derived from these data, including site-specific environmental standards. We propose three geostatistics-based methods for the comparison of interpolated contaminant concentrations and standards. The study is illustrated by data from a part of the Betuwe railroad transect, which extends over 12 km in the western Netherlands. As it turns out, a useful procedure is to combine interpolated contaminant concentrations with interpolated threshold values.
Coreference resolution on RDF Graphs generated from Information Extraction: first results

In our research on the use of information extraction to help populate the semantic web, we have encountered significant obstacles to interoperability. One such obstacle is cross-document coreference resolution. In this paper we describe an effort to improve coreference resolution on RDF graphs generated by text analytics. In addition to driving knowledge-base population, our goal is to demonstrate that successfully combining semantic web and natural language processing technologies can offer advantages over either in isolation, and motivates overcoming the obstacles to interoperability. We present some early results that show improvement of coreference resolution using graph-matching algorithms over RDF.
B Selected variables in KPI data analysis

Regularization plays a critical role in modern statistical research, especially in high dimensional variable selection problems. Existing Bayesian methods usually assume independence between variables a priori. In this article, we propose a novel Bayesian approach, which explicitly models the dependence structure through a graph Laplacian matrix. We also generalize the graph Laplacian to allow both positively and negatively correlated variables. A prior distribution for the graph Laplacian is then proposed, which allows conjugacy and thereby greatly simplifies the computation. We show that the proposed Bayesian model leads to proper posterior distribution. We also connect our method with some existing regularization methods, such as Elastic Net, Lasso, OSCAR and Ridge regression. For posterior computation, we develop an efficient MCMC method based on parameter augmentation. Finally, we demonstrate the method through simulation studies and real data analysis.
Beyond Documents: Visualizing Informal Communication

Requirements engineering heavily relies on oral and informal communication like meetings, phone calls, and e-mails. Some requirements are traditionally communicated outside formal documents in many companies. Considering document-based communication only is, therefore, not sufficient. Communication beyond documents should be taken into account when a company wants to improve its requirements engineering practices. We present a notation for visualizing the flow of requirements, explicitly including both informal and document-based communication. An example illustrates the notation by applying it to eliciting and validating security requirements. We compare our visualization approach for flows of requirements to related notations. The comparison shows how differences in notations affect the ability to model essential concepts of our approach. Explicitly modeling flows beyond documents provides new opportunities for requirement awareness, process improvement and innovation of tools and techniques.
A novel vehicle tracking method with occlusion handling using longest common substring of chain-codes

Vehicle tracking is an essential requirement of any vision based Intelligent Transportation System for extracting different traffic parameters, efficiently. Handling inter-object occlusion is the most challenging part of tracking as a process of finding and following interested objects in a sequence of video frames. In this paper we present a system, based on code-book background model for motion segmentation and Kalman filter for tracking with a new approach for occlusion. This approach separates occluded vehicles based on longest common substring of chain codes. We use this tracking system to estimate some traffic parameters. Experimental results show the efficiency of the method
Autophagy-mediated longevity is modulated by lipoprotein biogenesis

ABSTRACT Autophagy-dependent longevity models in C. elegans display altered lipid storage profiles, but the contribution of lipid distribution to life-span extension is not fully understood. Here we report that lipoprotein production, autophagy and lysosomal lipolysis are linked to modulate life span in a conserved fashion. We find that overexpression of the yolk lipoprotein VIT/vitellogenin reduces the life span of long-lived animals by impairing the induction of autophagy-related and lysosomal genes necessary for longevity. Accordingly, reducing vitellogenesis increases life span via induction of autophagy and lysosomal lipolysis. Life-span extension due to reduced vitellogenesis or enhanced lysosomal lipolysis requires nuclear hormone receptors (NHRs) NHR-49 and NHR-80, highlighting novel roles for these NHRs in lysosomal lipid signaling. In dietary-restricted worms and mice, expression of VIT and hepatic APOB (apolipoprotein B), respectively, are significantly reduced, suggesting a conserved longevity mechanism. Altogether, our study demonstrates that lipoprotein biogenesis is an important mechanism that modulates aging by impairing autophagy and lysosomal lipolysis.
N=1 and N=2 Geometry from Fluxes

We provide a proof of the equivalence of N=1 dynamics obtained by deforming N=2 supersymmetric gauge theories by addition of certain superpotential terms, with that of type IIB superstring on Calabi-Yau threefold geometries with fluxes. In particular we show that minimization of the superpotential involving gaugino fields is equivalent to finding loci where Seiberg-Witten curve has certain factorization property. Moreover, by considering the limit of turning off of the superpotential we obtain the full low energy dynamics of N=2 gauge systems from Calabi-Yau geometries with fluxes.
Students Beliefs About the Benefits of Travel and Leisure: A Qualitative Analysis

Millennial generation students are a growing market for the travel and tourism industry, yet tourism marketers lack research regarding travel habits and motivations for them. As a result, the travel and leisure industry have not been able to capitalize on this revenue stream. The purpose of this study was to explore the beliefs, motivations, and attitudes that millennial generation students have about the benefits of travel and leisure. Topics for the literature review included motivations and attitudes, and qualitative research. Essays on motivations and beliefs of travel and leisure were assigned in fall 2009 to the RPTA 214 class and submitted electronically over blackboard. Of the original 22 essays 12 were randomly selected and converted into a master copy to eliminate names. The researcher analyzed the data qualitatively by scanning for word usage and context. The results indicated that the major beliefs, motivations, and attitudes for millennial generation students were: the whole experience, education, freedom, and personal growth. These findings can be used by the travel and leisure industry to better target the millennial generation college students.
Nanoscale Strain-Hardening of Keratin Fibres

Mammalian appendages such as hair, quill and wool have a unique structure composed of a cuticle, a cortex and a medulla. The cortex, responsible for the mechanical properties of the fibers, is an assemblage of spindle-shaped keratinized cells bound together by a lipid/protein sandwich called the cell membrane complex. Each cell is itself an assembly of macrofibrils around 300 nm in diameter that are paracrystalline arrays of keratin intermediate filaments embedded in a sulfur-rich protein matrix. Each macrofibril is also attached to its neighbors by a cell membrane complex. In this study, we combined atomic force microscopy based nano-indentation with peak-force imaging to study the nanomechanical properties of macrofibrils perpendicular to their axis. For indentation depths in the 200 to 500 nm range we observed a decrease of the dynamic elastic modulus at 1 Hz with increasing depth. This yielded an estimate of 1.6GPa for the lateral modulus at 1 Hz of porcupine quill’s macrofibrils. Using the same data we also estimated the dynamic elastic modulus at 1 Hz of the cell membrane complex surrounding each macrofibril, i.e., 13GPa. A similar estimate was obtained independently through elastic maps of the macrofibrils surface obtained in peak-force mode at 1 kHz. Furthermore, the macrofibrillar texture of the cortical cells was clearly identified on the elasticity maps, with the boundaries between macrofibrils being 40–50% stiffer than the macrofibrils themselves. Elasticity maps after indentation also revealed a local increase in dynamic elastic modulus over time indicative of a relaxation induced strain hardening that could be explained in term of a α-helix to β-sheet transition within the macrofibrils.
ALTERNATIVE METHODS FOR CALCULATING THE FUZZY LIMIT MATRIX IN THE ANALYTIC NETWORK PROCESS

In this paper we suggest a Markov based approach for calculating the fuzzy limit matrix in the analytic network process, where we experimented with several alternative computational methods that accommodate fuzziness in the supermatrix calculations and allow for considering cumulative uncertainty. The methods include constrained fuzzy computations (closed form and numerical methods), interval computations and heuristic based methods. A numerical example is given to demonstrate the ability of the methods and compare the results with supermatrix computations with crisp values.
PREDICTION OF PROTEIN FUNCTION USING TEXT FEATURES EXTRACTED FROM THE BIOMEDICAL LITERATURE

s based on statistical properties. For evaluation, the performance of our two classifiers was compared to that of two baseline classifiers: one that assigns function based solely on the prior distribution of protein function, and one that assigns function based on sequence similarity. The systems were trained and tested using 5-fold cross-validation over a dataset of more than 36,000 proteins. Overall, we show that text features extracted from biomedical literature can be used to predict protein function for any organism. Our results also show that our text-based classifier typically has comparable performance to the sequence-similarity baseline classifier. Based on our results and what previous work had shown, we believe that text features can be integrated with other types of features to provide more accurate predictions for protein function.
Hierarchy in structural brain networks

Whole-brain structural connectivity matrices extracted from Diffusion Weighted Images (DWI) provide a systematic way of representing anatomical brain networks. They are equivalent to weighted graphs that encode both the topology of the network as well as the strength of connection between each pair of region of interest (ROIs). Here, we exploit their hierarchical organization to infer probability of connection between pairs of ROIs. Firstly, we extract hierarchical graphs that best fit the data and we sample across them with a Markov Chain Monte Carlo (MCMC) algorithm to produce a consensus probability map of whether or not there is a connection. We apply our technique in a gender classification paradigm and we explore its effectiveness under different parcellation scenarios. Our results demonstrate that the proposed methodology improves classification when connectivity matrices are based on parcellations that do not confound their hierarchical structure.
Comparison of Visualization Methods of Genome-wide SNP Profiles in Childhood Acute Lymphoblastic Leukaemia

Data mining and knowledge discovery have been applied to datasets in various industries including biomedical data. Modelling, data mining and visualization in biomedical data address the problem of extracting knowledge from large and complex biomedical data. The current challenge of dealing with such data is to develop statistical-based and data mining methods that search and browse the underlying patterns within the data. In this paper, we employ several data reduction methods for visualizing genome--wide Single Nucleotide Polymorphism (SNP) datasets based on state--of--art data reduction techniques. Visualization approach has been selected based on the trustworthiness of the resultant visualizations. To deal with large amounts of genetic variation data, we have chosen to apply different data reduction methods to deal with the problem induced by high dimensionality. Based on the trustworthiness metric we found that neighbour Retrieval Visualizer (NeRV) outperformed other methods. This method optimizes the retrieval quality of Stochastic neighbour Embedding. The quality measure of the visualization (i.e. NeRV) showed excellent results, even though the dataset was reduced from 13917 to 2 dimensions. The visualization results will assist clinicians and biomedical researchers in understanding the systems biology of patients and how to compare different groups of clusters in visualizations.
Hardware Development: Agile and Co-Design

The development of devices that combine hardware and software has created new challenges. The new built devices have a short life cycle and frequently require upgrading. The software industry attends to these requests with agile methods, such as Scrum. Agile methods apply quick iterations and continuous preplanning based on feedback and past iterations, enabling a quick and continuous delivery for those requests. This scenario is being analyzed when applied to hardware or software development and used along with agile methodologies.
Transductive regression piloted by inter-manifold relations

In this paper, we present a novel semisupervised regression algorithm working on multiclass data that may lie on multiple manifolds. Unlike conventional manifold regression algorithms that do not consider the class distinction of samples, our method introduces the class information to the regression process and tries to exploit the similar configurations shared by the label distribution of multi-class data. To utilize the correlations among data from different classes, we develop a cross-manifold label propagation process and employ labels from different classes to enhance the regression performance. The interclass relations are coded by a set of intermanifold graphs and a regularization item is introduced to impose inter-class smoothness on the possible solutions. In addition, the algorithm is further extended with the kernel trick for predicting labels of the out-of-sample data even without class information. Experiments on both synthesized data and real world problems validate the effectiveness of the proposed framework for semisupervised regression.
Communication Processes in the Field Research Interview Setting: A Case Study.

For those involved &n research of any type, the gathering of complete, relevant, and untainted information is the ultimate goal. The collection of valuable information is particularly challenging in the soci0.1 sciences, which often call for qualitative field research. The effective field research interviewer must not only be knowledgeable of the theoretical background of a given study, but must also be well versed and practiced in a wide variety of communication skills. Beginning in 1970, Employee Assistance Programs (EAPs) have emerged as a major vehicle for prevention and treatment of alcohol, drug, and other emotional health problems in the workplace. In 1985, to meet a perceived need for quality education in the core tasks of EAT' work and to study the socialization processes associated with this work, Cornell University together with the New York State Division of Alcoholism and Alcohol Abuse launched the Employee Assistance Education and Research Program (bAERP). For the research program, three researchers conducted interviews with 278 people who had either graduated from EAERP, dropped out, inquired about the program, or obtained jobs in employee assistance. To prepare participants to be EAP :Interviewers, weekly research meetings conducted over a 3-year period served as dress rehearsals. Potential participants were persuaded to participate through appeals to trust, credibility, logic, and emotion. Irterview strategies varied with the participants. Data collected in the study is still being analyzed, and results of the research effort are anticipated by 1991. (SG) Reproductions supplied by EDRS are the best that can be made from the original document. **L. COMMUNICATION PROCESSES IN THE FIELD RESEARCH INTERVIEW SETTING: A CASE STUDY Susan A. Mason, M.A., M.S. Smithers Institute for Alcohol and Workplace Studies School of Industrial and Labor Relations Cornell University Presented April 20, 1990 Eastern Comm inication Association Convention
On the Grassmann condition number

We give new insight into the Grassmann condition of the conic feasibility problem \[ x \in L \cap K \setminus\{0\}. \] Here $K\subseteq V$ is a regular convex cone and $L\subseteq V$ is a linear subspace of the finite dimensional Euclidean vector space $V$. The Grassmann condition of this problem is the reciprocal of the distance from $L$ to the set of ill-posed instances in the Grassmann manifold where $L$ lives. We consider a very general distance in the Grassmann manifold defined by two possibly different norms in $V$. We establish the equivalence between the Grassmann distance to ill-posedness of the above problem and a natural measure of the least violated trial solution to its alternative feasibility problem. We also show a tight relationship between the Grassmann and Renegar's condition measures, and between the Grassman measure and a symmetry measure of the above feasibility problem. Our approach can be readily specialized to a canonical norm in $V$ induced by $K$, a prime example being the one-norm for the non-negative orthant. For this special case we show that the Grassmann distance ill-posedness of is equivalent to a measure of the most interior solution to the above conic feasibility problem.
Ultrasensitive semiautomated chemiluminescent immunoassay for estradiol.

The availability of rapid, non-extraction-based estradiol-17β immunoassays has been important in clinical settings where prompt turnaround time is required, including in vitro fertilization programs, and in large-scale research programs where large numbers of samples are processed. However, the inaccuracy of estradiol-17β assay results has been recognized for some time, as indicated in the Textbook of Reproductive Medicine by L.R. Boots (1), in which he states: “Everyone measures estradiol levels and it is probably assumed that few if any problems exist with this assay…. [T]he most common use of estradiol levels is in relation to levels of estradiol during ovulation stimulation and every assay provides clinically relevant results. These results are clearly inaccurate but usually precise” (1). In the First and Second E2 International Workshops (2), problems associated with the analysis of estradiol-17β were fully discussed. The First E2 International Workshop focused on the need for more specific, precise, and sensitive estradiol-17β assays, and the Second E2 International Workshop explored various means of increasing accuracy in the measurement of estradiol-17β. By consensus, the use of a panel of 22 samples confirmed by isotope dilution–gas chromatography–mass spectrometry (ID-GC-MS) (2) was made available for use in assay development. Indications of a lack of accuracy are also provided by examining survey programs, such as those offered by the College of American Pathologists, that reveal large differences in estradiol-17β values obtained on the same sample. Modern serum assays for estradiol-17β claim detection limits as low as 40 pmol/L or lower, but they lack sufficient sensitivity for pediatric or postmenopausal specimens and are frequently unable to provide adequate analytical precision and accuracy in samples from human males. In postmenopausal women, circulating estradiol-17β is usually 147 pmol/L are rare. In …
Creating awareness and reflection in a large-scale IS lecture - the application of a peer assessment in a flipped classroom scenario

Large-scale lectures are a typical way of teaching university students. However, these lectures often lack interaction elements and do not foster awareness and reflection in the learning process. This results in insufficient learning outcomes such as learning satisfaction and success. Therefore, a new approach to engage interaction in such large-scale lectures is the flipped classroom concept which seeks to overcome these challenges by stimulating self-regulated learning phases and improving interaction as well as awareness and reflection in the presence phases of a lecture. However, it is still unclear how to actually increase reflection and awareness through interaction in such learning scenarios. For this purpose, we propose an application of a technology-enhanced peer assessment that is carried out in large-scale information systems lectures. Preliminary evaluation results suggest the potentials of this approach. Thus, we are able to provide first theoretical and practical implications for the application of a technology-enhanced peer assessment in large-scale lectures.