Miracle-dz/UniFAR-FTUs huggingface dataset:
Interconnect modeling and optimization in deep sub-micron technologies

Interconnect will be a major bottleneck for deep sub-micron technologies in the years to come. This dissertation addresses the communication aspect from a power consumption and transmission speed perspective. A model for the energy consumption associated with data transmission through deep sub-micron technology buses is derived. The model is used to estimate the power consumption of the bus as a function of the Transition Activity Matrix, a quantity generalizing the transition activity factors of the individual lines. An information theoretic framework has been developed to study the relation between speed (number of operations per time unit) and energy consumption per operation in the case of synchronous digital systems. The theory provides us with the fundamental minimum energy per input information bit that is required to process or communicate information at a certain rate. A large class of nonlinear coding schemes is examined that leads to significant power reduction. This class contains all encoding schemes that have the form of connected Finite State Machines. For power reduction, a charge recycling technique appropriate for deep sub-micron buses is developed. It is shown that for large buses power can be reduced by a factor of two. An efficient modular circuit implementation is presented that demonstrates the practicality of the technique and its significant net power reduction. Coding for speed on the bus is introduced. This novel idea is based on the fact that coupling between the lines in a deep sub-micron bus implies that different transitions require different amounts of time to complete. By allowing only “fast” transitions to take place, we can increase the clock frequency of the bus. The combinatorial capacity of such a constrained bus is the theoretical maximum rate that data can be transmitted. While this rate is less than that of the original bus, we can clock the constrained bus faster. It turns out that the net data transmission speed can be significantly higher than that of the original bus, in some cases 120% higher. A methodology to estimate the amount of time each transition requires is introduced. The results are compared to HSPICE and MATLAB simulations and are shown to be conservative. Using these estimates the transitions are classified according to how fast they are completed. The fundamental theoretical limits of Coding For Speed are derived. The increase of the effective speed of the bus is estimated as a function of the number of lines and the coupling between them. Finally, a class of practical coding schemes for speed, termed Differential RLL(1, ∞) schemes, is presented. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) (Abstract shortened by UMI.)
Experimental Study of End Milling Force on Manganese Steel

Cutting force is a key factor influencing the machining deformation of weak rigidity work pieces. In order to reduce the machining deformation and improve the process precision and the surface quality, it is necessary to study the factors influencing the cutting force and build the regression model of cutting forces. This paper discusses the development of the first and second order models for predicting the cutting force produced in end-milling operation of modified manganese steel. The first and second order cutting force equations are developed using the response surface methodology (RSM) to study the effect of four input cutting parameters (cutting speed, feed rate, radial depth and axial depth of cut) on cutting force. The separate effect of individual input factors and the interaction between these factors are also investigated in this study. The received second order equation shows, based on the variance analysis, that the most influential input parameter was the feed rate followed by axial depth, and radial depth of cut. It was found that the interaction of feed with axial depth was extremely strong. In addition, the interactions of feed with radial depth; and feed rate with radial depth of cut were observed to be quite significant. The predictive models in this study are believed to produce values of the longitudinal component of the cutting force close to those readings recorded experimentally with a 95% confident interval.
Matching with Multiple Control Groups , and Adjusting for Group Differences

When estimating causal effects from observational data, it is desirable to replicate a randomized experiment as closely as possible, for example, by obtaining treated and control groups with extremely similar distributions of observed covariates. This goal can often be achieved by choosing a subsample from the original control group that matches the treatment group on the distribution of these covariates, thus reducing bias due to these covariates. However, sometimes the original sample of control units cannot provide adequate matches for the treated units. In these cases, it may be desirable to obtain matched controls from multiple control groups. Multiple control groups have been used to test for hidden biases in causal inference (e.g., Rosenbaum 2002); however, little work has been done on their use in matching or for adjusting for biases, such as potential systematic differences between the original control group and supplemental control groups beyond that which can be explained by observed covariates. Here we present a method that uses matches from multiple control groups and adjusts for potentially unobserved differences between the additional control groups and the original control group in the analysis of the outcome. The method is illustrated and evaluated using simulated data as well as data from an evaluation of a school dropout prevention program, which utilizes both local and non-local matches.
Neural Basis of Repetition Priming during Mathematical Cognition: Repetition Suppression or Repetition Enhancement?

We investigated the neural basis of repetition priming (RP) during mathematical cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning as an alternate model for behavioral facilitation. We examined the neural basis of RP during mathematical problem solving in the context of these two models of learning. Brain imaging and behavioral data were acquired from 39 adults during novel and repeated presentation of three-operand mathematical equations. Despite wide-spread decreases in activation during repeat, compared with novel trials, there was no direct relation between behavioral facilitation and the degree of repetition suppression in any brain region. Rather, RT improvements were directly correlated with repetition enhancement in the hippocampus and the posteromedial cortex [posterior cingulate cortex, precuneus, and retrosplenial cortex; Brodmann's areas (BAs) 23, 7, and 30, respectively], regions known to support memory formation and retrieval, and in the SMA (BA 6) and the dorsal midcingulate (“motor cingulate”) cortex (BA 24d), regions known to be important for motor learning. Furthermore, improvements in RT were also correlated with increased functional connectivity of the hippocampus with both the SMA and the dorsal midcingulate cortex. Our findings provide novel support for the hypothesis that repetition enhancement and associated stimulus-response learning may facilitate behavioral performance during problem solving.
Astrometric Calibration of the Sloan Digital Sky Survey

The astrometric calibration of the Sloan Digital Sky Survey is described. For point sources brighter than r ∼ 20, the astrometric accuracy is 45 mas rms per coordinate when reduced against the USNO CCD Astrograph Catalog and 75 mas rms when reduced against Tycho-2, with an additional 20–30 mas systematic error in both cases. The rms errors are dominated by anomalous refraction and random errors in the primary reference catalogs. The relative astrometric accuracy between the r filter and each of the other filters (u, g, i, z) is 25–35 mas rms. At the survey limit (r ∼ 22), the astrometric accuracy is limited by photon statistics to approximately 100 mas rms for typical seeing. Anomalous refraction is shown to contain components correlated over 2° or more on the sky.
Influence of patient preferences on the cost-effectiveness of screening for Lynch syndrome.

PURPOSE Patients and relatives have varying preferences for genetic testing and interventions related to hereditary cancer syndromes. We examined how the impact of these services on quality of life (QoL) affects the cost-effectiveness of screening for Lynch syndrome among probands newly diagnosed with colorectal cancer and their relatives. METHODS We constructed a state-transition model comparing screening strategies (clinical criteria, prediction algorithms, tumor testing, and upfront germline testing) with no screening to identify Lynch syndrome. The model incorporated individuals' health state utilities after screening, germline testing, and risk-reducing surgeries, with utilities persisting for 12 months in the base case. Outcomes consisted of quality-adjusted lifeyears (QALYs), costs, and cost per QALY gained. Sensitivity analyses assessed how the duration and magnitude of changes in QoL influenced results. RESULTS Multiple screening strategies yielded gains in QALYs at acceptable costs compared with no screening. The preferred strategy-immunohistochemistry of tumors followed by BRAF mutation testing (IHC/BRAF)-cost $59,700 per QALY gained in the base case. The duration and magnitude of decreases in QoL after decisions related to germline testing and surgeries were key determinants of the cost-effectiveness of screening. IHC/BRAF cost >$100,000 per QALY gained when decrements to QoL persisted for 21 months. CONCLUSION Screening for Lynch syndrome in the population is likely to yield long-term gains in life expectancy that outweigh any short-term decreases in QoL, at acceptable costs. Counseling for individuals should aim to mitigate potential negative impact of genetic testing and risk-reducing interventions on QoL.
Convergence Between the Achenbach Youth Self-Report and Structured Diagnostic Interview Diagnoses in ADHD and Non-ADHD Youth

We evaluated the convergence of Achenbach youth self-report (YSR) scales with attention-deficit hyperactivity disorder (ADHD) and psychiatric comorbidity derived from self-report structured interviews. Subjects were 251 youths older than 12 years assessed with YSR scales and directly obtained structured diagnostic interview. We evaluated the strength of association between each YSR scale and structured-interview derived diagnoses using total predictive value (TPV) and odds ratios (OR). Excellent convergence was found between the YSR attention problems with the structured interview derived diagnosis of ADHD, between the YSR delinquent behavior scale and the diagnosis of conduct disorder, and between the YSR anxiety/depression and withdrawn scales and the diagnosis of major depression, and between the YSR social problems and somatic scales and the diagnosis of anxiety. These findings indicate that the YSR could serve as a rapid and cost-effective diagnostic tool to identify major psychopathology in high-risk adolescents.
Impact of birth rate, seasonality and transmission rate on minimum levels of coverage needed for rubella vaccination

SUMMARY Childhood rubella infection in early pregnancy can lead to fetal death or congenital rubella syndrome (CRS) with multiple disabilities. Reduction of transmission via universal vaccination can prevent CRS, but inadequate coverage may increase CRS numbers by increasing the average age at infection. Consequently, many countries do not vaccinate against rubella. The World Health Organization recommends that for safe rubella vaccination, at least 80% coverage of each birth cohort should be sustained. The nonlinear relationship between CRS burden and infection dynamics has been much studied; however, how the complex interaction between epidemic and demographic dynamics affects minimum safe levels of coverage has not been quantitatively evaluated across scales necessary for a global assessment. We modelled 30-year CRS burdens across epidemiological and demographic settings, including the effect of local interruption of transmission via stochastic fadeout. Necessary minimum vaccination coverage increases markedly with birth and transmission rates, independent of amplitude of seasonal fluctuations in transmission. Susceptible build-up in older age groups following local stochastic extinction of rubella increased CRS burden, indicating that spatial context is important. In low birth-rate settings, 80% routine coverage is a conservative guideline, particularly if supplemented with campaigns and vaccination of women of childbearing age. Where birth and transmission rates are high, immunization coverage must be well above 80% and campaigns may be needed. Policy-makers should be aware of the potential negative effect of local extinction of rubella, since heterogeneity in vaccination coverage will shape extinction patterns, potentially increasing CRS burdens.
Enhanced transport across entire length of plasma sheet boundary field lines leading to substorm onset

[1] Recently, images from the THEMIS all-sky imager array were used to suggest that longitudinally localized enhanced plasma transport across the nightside open-closed boundary (separatrix) ∼5.5 min before substorm onset leads to intrusion of new plasma toward the near-Earth plasma sheet and to substorm onset via near-Earth instability. If such reconnection-related flow enhancements precede substorm onsets, they should be seen along the entire length of separatrix field lines within the appropriate longitudinal region as perpendicular flow before onset. Evidence for such flows has been previously reported for four events over a limited radial extent. Based on 13 substorm events with at least one of the THEMIS spacecraft near the outer boundary of the plasma sheet in the premidnight region and simultaneous auroral images, we find here that enhanced flows toward the plasma sheet center are often seen (11 of 13 events) in the premidnight sector a few minutes prior to onset from X ∼ −11 to −26 RE. Previous radar observations within the ionosphere have also shown that such enhanced flows bringing new plasma into the auroral zone from polar cap field lines prior to onset are common. Assuming that the outer boundary of the plasma sheet approximately demarcates the open-closed field line boundary, these results indicate that reconnection-related flow enhancements do indeed precede substorm onset along the entire radial extent of the plasma sheet outer boundary. Consistent with longitudinal localization, enhanced flows toward the plasma sheet center were not observed on all spacecraft for five events where there were more than one spacecraft near the plasma sheet boundary.
DeepBUFS: Deep Learned Biometric User Feedback System

Understanding what is relevant to a user is important for being able to deliver accurate and meaningful search results, engaging user experiences, and an efficient workflow. We present DeepBUFS, a prototype interactive system to support users make relevance decisions. DeepBUFS uses pre-trained deep learning models to classify relevance from user biometric samples. Preliminary results are encouraging, with deep learning models able to classify document topics from electrocardiograph (ECG) and electrodermal activity (EDA) with an average 71% accuracy. This suggests opportunities for new ways of interacting with users, including using biometrics to understand topical relevance and users' information and design needs. Further work is planned to expand these findings.
The Effects of Cognitive-Behavioral Stress Management on Nurses’ Job Stress

Background: Job stress is a prevalent and costly problem in different working conditions. Stress can degrade care quality in nurses and elevate their willingness to leave the job. This study aimed to determine the effects of cognitive-behavioral stress management on nurses' job stress. Materials and Methods: In this quasi-experimental study with a control group, 40 nurses from the educational hospitals in Birjand who had completed a job stress questionnaire with a score of more than 100 were selected and randomly allocated to two groups. The intervention group underwent eight 2-hour sessions training of cognitive-behavioral stress management (one session per week), whereas the control group had no such training. Two months after the training, the two groups completed the hospital job stress questionnaire again. The data were analyzed using Statistical Package for the Social Sciences (version 19) by Chi-square, Fisher exact test, independent t-test, and paired t-tests at 0.05 significance level. Results: Stress mean score and standard deviation before and after the intervention were 3.48 (0.22) and 2.8 (0.2) in the intervention group and 3.48 (0.21) and 3.56 (0.2) in the control group, respectively. In the intervention group, the overall mean scores of stress and its dimensions significantly decreased after the intervention (P < 0.05). Conclusions: Given the significant decrease in the nurses' stress using cognitive-behavioral stress management, this approach can be suggested to be taught to nurses in hospitals in order to decrease their stress and increase their efficiency.
Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs

This work presents an iterative re-alignment approach applicable to visual sequence labelling tasks such as gesture recognition, activity recognition and continuous sign language recognition. Previous methods dealing with video data usually rely on given frame labels to train their classifiers. However, looking at recent data sets, these labels often tend to be noisy which is commonly overseen. We propose an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion. Given a series of frames and sequence-level labels, a deep recurrent CNN-BLSTM network is trained end-to-end. Embedded into an HMM the resulting deep model corrects the frame labels and continuously improves its performance in several re-alignments. We evaluate on two challenging publicly available sign recognition benchmark data sets featuring over 1000 classes. We outperform the state-of-the-art by up to 10% absolute and 30% relative.
In situ integrity assessment of a smart structure based on the local material damping

Integration of functional elements into fibre-reinforced host structures provides the possibility for in situ monitoring of the structural integrity of critical components. In this study, a vibration-based monitoring function has been developed that allows the structural integrity identification of critical components. For this purpose, signal analysis algorithms were developed to enable the estimation of damage-dependent modal damping. The analysed smart structure was a carbon fibre–reinforced epoxy composite plate with an integrated actuating/sensing system. The local material damping is a parameter especially sensitive to different failure modes of composites. In order to characterise the changes of this parameter resulting from impact events, dynamical mechanical analysis on intact and damaged specimens made of the composite material was conducted. Based on the dynamical mechanical analysis results, a finite element model of the structure was developed. Then, modal damping ratios for different sizes and locations of damaged regions were numerically determined, and a relation between modal damping and damage-dependent local damping was identified. The deterministic decision trees describing the reverse relationship between online-measured modal damping and damage condition were determined. That was accomplished through the application of information entropy-based data-mining algorithms to the numerically generated learning dataset obtained using the developed finite element model.
Do "gut feelings" affect insurance demand?

This paper investigates whether an individual’s emotional profile affects their insurance decision making. We explore how the emotional reaction in conditions of ambiguity and the fear of the unknown affect insurance choices. We conducted psychophysiological experiments on a sample of 645 individuals and find evidence that these emotional variables offer contributions by increasing the predictive power of models for insurance demand, alongside traditional socioeconomic variables and psychographic traits. A selective role of emotional influence has been proven to exist when comparing different insurance policies, such as life, health, casualty and indemnity insurance.
Two-Week versus Six-Month Sampling Interval in a Short-Term Natural History Study of Oral HPV Infection in an HIV-Positive Cohort

Background Oral HPV infections detected six-months apart were compared to those detected bi-weekly, in an HIV-positive cohort, during the intervening months to elucidate systematic biases introduced into natural history studies by sampling interval. Methods Fourteen consecutive oral rinse samples were collected every two weeks for six months from an HIV-positive cohort (n = 112) and evaluated for the presence of 37 HPV types. The cumulative probability of type-specific HPV detection at visits 1 through 14 was determined as a function of infection categorized at visits 1 and 14 as persistent, newly detected, cleared or absent. Transition models were used to evaluate the effect of HPV viral load (measured by RT-PCR for HPV 16, 18, 31, 33, 35) on infection persistence. Results The average point prevalence of oral HPV infection was similar at two-week and six-month sampling intervals (45% vs. 47%, p = 0.52), but cumulative prevalence was higher with the former (82% vs. 53%, p<0.001) as was the cumulative prevalence of type-specific infections (9.3% vs 3.8%, p<0.0001). Type-specific infections persistent under a six-month sampling interval had a high probability (0.93, 95%CI 0.83–0.98) of detection at 50% or more of the intervening visits and infections that were absent had a high probability (0.94, 95% CI 0.93–0.95) of no interval detection. The odds of detection at any visit significantly increased for each unit increase in HPV viral load at the previous visit. Conclusions Six-month sampling is appropriate to model factors associated with type-specific oral HPV infection persistence but may misclassify HPV-exposed individuals as unexposed.