Miracle-dz/UniFAR-FTUs huggingface dataset:
Models of process variations in device and interconnect

Variation is the deviation from intended or designed values for a structure or circuit parameter of concern. The electrical performance of microprocessors or other integrated circuits are impacted by two sources of variation. First, environmental factors are those which arise during the operation of a circuit, and include variations in power supply, switching activity, and temperature of the chip or across the chip. These variations depend primarily on architectural and operating decisions such as power grid design and component placement. Time-varying (temporal) variation in these environment parameters can be a signi cant design concern. Circuit robustness to noise, cross-talk, and timeand switching-related aging or reliability factors must be considered carefully during circuit design. In this chapter, we will focus on a second category of variation sources. Physical factors during manufacture result in structural device and interconnect variations which are essentially permanent. These variations arise due to processing and masking limitations, and result in spatially varying deviations from designed parameter values. The dependencies of manufacturing variation can be quite complex, and have been modeled and dealt with in a number of ways. in this chapter we will focus on parametric variation due to continuously varying structural or electrical parameters, as these can signi cantly impact not only yield but also performance in high speed microprocessor and other digital circuits. It should be noted that defect related failures arising due to small particles or other spot failures in manufacturing are also critical in determining ultimate chip yield, and consideration of such defects during circuit design is often needed. A large body of work has developed to deal with such point or particle defect issues [11], and they are not discussed here. This chapter discusses the following four key elements of process variations and statistical modeling necessary for integrated circuit design. First, it is important to have a high level perspective on the ways that parametric variations are typically expressed and categorized. Section 2 thus presents an overview of integrated circuit statistical descriptions ranging from simple lumped distributions, to separation of interdie and intradie variation, and nally to detailed models of spatial variation. Given this perspective, the second element is the set of speci c process variations that
Modeling Evaluation of Tidal Stream Energy and the Impacts of Energy Extraction on Hydrodynamics in the Taiwan Strait

Tidal stream speeds in straits are accelerated because of geographic and bathymetric features. For instance, narrow channels and shallows can cause high tidal stream energy. In this study, water level and tidal current were simulated using a three-dimensional semi-implicit Eulerian-Lagrangian finite-element model to investigate the complex tidal characteristics in the Taiwan Strait and to determine potential locations for harnessing tidal stream energy. The model was driven by nine tidal components (M 2 , S 2 , N 2 , K 2 , K 1 , O 1 , P 1 , Q 1 , and M 4 ) at open boundaries. The modeling results were validated with the measured data, including water level and tidal current. Through the model simulations, we found that the highest tidal currents occurred at the Penghu Channel in the Taiwan Strait. The Penghu Channel is an appropriate location for the deployment of a tidal turbine array because of its deep and flat bathymetry. The impacts of energy extraction on hydrodynamics were assessed by considering the momentum sink approach. The simulated results indicate that only minimal impacts would occur on water level and tidal current in the Taiwan Strait if a turbine array (55 turbines) was installed in the Penghu Channel.
Effect of heterogeneity on decorrelation mechanisms in spiking neural networks

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm .
Application-Managed Database Replication on Virtualized Cloud Environments

One among several patterns that are common for applications being deployed in cloud platforms is to take an existing application designed for a conventional data center, and then port it to the cloud with minimal changes. When this is done, the application tier can easily take advantage of the elasticity and scale provided by the cloud, but the data management layer, being stateful, faces more issues. In this paper, we explore experimentally the limits to scaling for an application that itself manages database replicas each placed in a virtual machine in the cloud (exactly following the design used when the application would be deployed on an in-house cluster). We characterize important limits in the load on the master copy, the workload imposed on each slave copy when processing updates from the master, and also from the increasing staleness of replicas.
Analyzing Item Features for Cold-Start Problems in Recommendation Systems

With the development of web technologies, there is lots of information on the web. For effective web searching, the recommendation systems appear on the web. The recommendation systems provide customized information for the personal users. The conventional processes of the recommendation are generally based on the user preferences for the items. This leads to the cold-start problems for new items in recommending since new items have no user preferences. Although there are some studies to alleviate this problem by utilizing item features such as category information, the studies do not provide the validities of the use of item features. Namely, they just use the item features without analyzing features. If a feature draws meaningful recommendation results, there are some reasons that the feature can draw the results. We try to find these reasons. We calculate the uncertainty of item features by applying entropy in information theory and assume that this uncertainty of item features can show the level of reliability for the recommendation results. We verify our assumption by utilizing some tests in movie domain.
Sex Differences in Stroke Epidemiology: A Systematic Review

Background and Purpose— Epidemiological studies, mainly based on Western European surveys, have shown that stroke is more common in men than in women. In recent years, sex-specific data on stroke incidence, prevalence, subtypes, severity and case-fatality have become available from other parts of the world. The purpose of this article is to give a worldwide review on sex differences in stroke epidemiology. Methods— We searched PubMed, tables-of-contents, review articles, and reference lists for community-based studies including information on sex differences. In some areas, such as secular trends, ischemic subtypes and stroke severity, noncommunity-based studies were also reviewed. Male/female ratios were calculated. Results— We found 98 articles that contained relevant sex-specific information, including 59 incidence studies from 19 countries and 5 continents. The mean age at first-ever stroke was 68.6 years among men, and 72.9 years among women. Male stroke incidence rate was 33% higher and stroke prevalence was 41% higher than the female, with large variations between age bands and between populations. The incidence rates of brain infarction and intracerebral hemorrhage were higher among men, whereas the rate of subarachnoidal hemorrhage was higher among women, although this difference was not statistically significant. Stroke tended to be more severe in women, with a 1-month case fatality of 24.7% compared with 19.7% for men. Conclusions— Worldwide, stroke is more common among men, but women are more severely ill. The mismatch between the sexes is larger than previously described.
Refractory epilepsy: a progressive, intractable but preventable condition?

Intractable seizures are just one manifestation of 'refractory epilepsy', which can be recognized as a distinct condition with multifaceted dimensions, including neurobiochemical plastic changes, cognitive decline and psychosocial dysfunction, leading to dependent behaviour and a restricted lifestyle. The biological basis of 'refractoriness' is likely to be multifactorial, and may include the severity of the syndrome and/or underlying neuropathology, abnormal reorganization of neuronal circuitry, alteration in neurotransmitter receptors, ion channelopathies, reactive autoimmunity, and impaired antiepileptic drug (AED) penetration to the seizure focus. Some of these deleterious changes may be a consequence of recurrent seizures. We hypothesize that 'refractory epilepsy' may be prevented by interrupting this self-perpetuating progression. There is increasing evidence that these patients can be identified early in the clinical course and, thus, be targeted early for effective therapeutic intervention. Failure of two first-line AEDs due to lack of efficacy or poor tolerability should prompt consideration of epilepsy surgery in a patient with a resectable brain abnormality. For the majority not suitable for 'curative' surgery, AEDs should be combined with the aim of achieving 'synergism'. This strategy has the potential to improve outcome by preventing the insidious progression to intractable 'refractoriness' and a downward spiraling quality of life.
Secure Outsourcing of Matrix Operations as a Service

This paper reports the design of a cloud-based service for coordinating secure outsourcing of storage and computation of scientific data, particularly matrices. While this service may support different secure outsourcing protocols and mechanisms (e.g. homomorphic encryption, secret sharing and randomization), we hide all the complexity from end-users and move it to a middleware broker. The broker manages the communication between the client and one or more clouds. The requests submitted by users are automatically translated into WS-BPEL workflows and then executed by the broker workflow engine to coordinate the control flow and the data flow for outsourcing of matrix operations. We detail the architecture of our framework and the design of its key components. Our work facilitates real-world and practical deployment of recently proposed secure outsourcing protocols.
Survivin antisense oligodeoxynucleotide inhibits growth of gastric cancer cells.

AIM To investigate the effect of transfected survivin antisense oligonucleotide (ASODN) on proliferation and apoptosis of gastric cancer cells. METHODS The authors designed ASODNs targeting different regions of survivin mRNA, including surviving ASODN1, ASODN2 and ASODN3. ASODNs were transfected into gastric cancer cell line SGC 7901, cell growth was detected by MTT assay. Cells exposed to the potent oligonucleotide were also examined for apoptosis induction by FCM and fluorescence microscopy. Semiquantitive RT-PCR and Western blot examinations were carried for expression of survivin mRNA and protein. RESULTS ASODN3 caused a statistically significant reduction of cell viability to 60.6% (+/-2.9%) (P<0.01), while ASODN1 and ASODN2 had no such changes (P>0.05). The cell growth was also significantly inhibited by ASODN3, compared with reversal and scrambled sequence. A significant loss of survivin mRNA was presented in ASODN3 treated cells and this was not seen in treatment with sense ODN or scramble ODN. Protein level was significantly decreased 48 h after survivin ASODN trasfected by approximately 2-fold decrease compared with untreated controls. However, ASODN3 did not induce significant apoptosis response until 48 h after transfection (P>0.05). CONCLUSION ASODN3, which targets translation initiation part, can be identified as a most potent antisense compound. Srvivin ASODN3 may provide a novel approach to therapy of gastric cancer.
Vector Operations in Neural Networks Computations

To make clear the mechanism of the visual movement is important in the visual system. The problem is how to perceive vectors of the optic flow in the network. First, the biological asymmetric network with nonlinear ties is analyzed for generating the vector from the point of the network computations. The results are applicable to the V1 and MT model of the neural networks in the cortex. The stimulus with a mixture distribution is applied to evaluate their network processing ability for the movement direction and its velocity, which generate the vector. Second, it is shown that the vector is emphasized in the MT than the V1. The characterized equation is derived in the network computations, which evaluates the vector properties of processing ability of the network. The movement velocity is derived, which is represented in Wiener kernels. The operations of vectors are shown in the divisive normalization network, which will create curl or divergence vectors in the higher neural network as MST area.
Experiments with Latent Dirichlet Allocation

Latent Dirichlet Allocation is a generative topic model for text. In this report, we implement collapsed Gibbs sampling to learn the topic model. We test our implementation on two data sets: classic400 and Psychological Abstract Review. We also discuss the different evaluation of goodness-of-fit of the models how parameter settings interact with the goodness-of-fit.
Spatio-Temporal Modeling of Users' Check-ins in Location-Based Social Networks

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters using an efficient EM algorithm, which distributes over the users. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our model outperforms the other alternatives in the prediction of time and location of check-ins.
Conditioned Seeing as Related to Bidirectional Naming for Unfamiliar Stimuli with Third through Fifth Grade Students Diagnosed with Autism

Educational Information Teachers College, Columbia University Doctor of Philosophy, Applied Behavior Analysis, February 2018 • Dissertation title: Conditioned Seeing as Related to Bidirectional Naming with Unfamiliar Stimuli with Third through Fifth Grade Students Diagnosed with Autism • Dissertation defense: November 2017 • Advisor: Dr. R. Douglas Greer Master of Philosophy, Applied Behavior Analysis, May 2017 Master of Arts, Applied Behavior Analysis, February 2010
A method for the classification of folksonomy resources

The paper presents a method for the automatic classification of the resources of collaborative tagging systems, also called folksonomies. Folksonomies are an easy way of representing knowledge in Web 2.0 because of its simplicity. However, due to their characteristics, the information retrieval in these systems is more difficult than in classical knowledge representation systems. This method has a high degree of precision and recall, and may adjust these values with the use of different modes of classification and thresholds, improving the performances of other resource classification methods.
Bridging the Gap in Model-based V & V Methodology Position Paper Mass

In this paper we suggest that one of the big challenges in development of model-based V&V methodology is bridging the gap between the low-level user guides of V&V tools and the high-level text-book descriptions of model-driven development methodologies. As a step towards mitigating this we suggest a structure for V&V methodology with three layers: a bottom level with usage of specific V&V techniques, a middle level with V&V method patterns, and a top level with overall development methodology. Within this structure we suggest that the middle level is often missing in presentations of model-based V&V and in presentations of model-driven development methodologies, and is where the need for development is the greatest.
Intake of protein, calcium and sodium in public child day care centers

OBJECTIVE: To assess calcium, protein and sodium intake, of children that attend public day-care centers and to compare it with the recommended one. METHODS: Cross-sectional descriptive study in seven public day care centers of São Paulo city, Southeast Brazil, which enrolled 366 children between 12 and 36 months of age. The data collection occurred between September and December 2010. Each day care center was evaluated for three non-consecutive days, totaling 42 days and 210 meals. Dietary intake was assessed by a direct food weighing method. For the nutritional calculation, DietWin(r) Profissional 2.0 was used, and the adequacy was calculated according to the recommendations of the National School Feeding Program for energy, protein, calcium and sodium. The calcium/protein relation was also calculated, as well as calcium density (mg/1,000kcal). RESULTS: The energy (406.4kcal), protein (18.2g) and calcium (207.6mg) consumption did not reach the recommended values in all the evaluated day care centers. Sodium intake exceeded up to three times the recommendation. The calcium/protein ratio of 11.7mg/g was less than the adequate one (20mg/g). CONCLUSIONS: There was inadequacy of calcium, protein and sodium dietary intake, in children attending public day-care centers.
Facial expression recognition system based on a face statistical model and Support Vector Machines

Facial expression recognition is an important research domain in developing human-machine relations. This paper proposes a facial expression recognition system. This system is built using Constrained Local Models to fit the shapes on a face image and on Support Vector Machines (SVMs) to detect the face expressions. Different SVM architectures and trainings have been used including applying outliers filtering to obtain better recognition rates.
Applying Data Mining to Contract Inference

This master thesis report will introduce the techniques that we use to automatically infer contracts for a given class. The report will cover briefly the building of the automatic test suite, the generation of the change profile and then will get into great details on the implication inference. It will focus on the decision tree and linear regression learning techniques to infer contracts. Experiments show that for standard data structure classes such as linked list, array, stack, query and hash table, 75% of the complete contracts can be inferred fully automatically.
Bias in low-multipole cosmic microwave background reconstructions

The large-angle, low-multipole cosmic microwave background (CMB) provides a unique view of the largest angular scales in the Universe. Study of these scales is hampered by the facts that we have only one Universe to observe, only a few independent samples of the underlying statistical distribution of these modes, and an incomplete sky to observe due to the interposing Galaxy. Techniques for reconstructing a full sky from partial-sky data are well known and have been applied to the large angular scales. In this work, we critically study the reconstruction process and show that, in practice, the reconstruction is biased due to leakage of information from the region obscured by foregrounds to the region used for the reconstruction. We conclude that, despite being suboptimal in a technical sense, using the unobscured region without reconstructing is the most robust measure of the true CMB sky. We also show that for noise-free data reconstructing using the usual optimal, unbiased estimator may be employed without smoothing, thus avoiding the leakage problem. Unfortunately, directly applying this to real data with noise and residual, unmasked foregrounds yields highly biased reconstructions requiring further care to apply this method successfully to the real-world CMB.
Optical Coherence Tomography Angiography of the Optic Disc; an Overview

Different diseases of the optic disc may be caused by or lead to abnormal vasculature at the optic nerve head. Optical coherence tomography angiography (OCTA) is a novel technology that provides high resolution mapping of the retinal and optic disc vessels. Recent studies have shown the ability of OCTA to visualize vascular abnormalities in different optic neuropathies. In addition, quantified OCTA measurements were found promising for differentiating optic neuropathies from healthy eyes.
A novel three party key establishment scheme in the context of Internet-of-Things

Three party key establishment schemes are assumed impractical for traditional Wireless Sensor Networks (WSNs), due to unavailability of trusted third party and limitation of communication radio range in sensor nodes. However, with the advent of Internet-Enabled Sensor Network (IESN), which can provide trusted party by the internet, the future of three party techniques as key establishment scheme for IESN looks bright. In this paper we show vulnerability of existing three party schemes against DoS attacks. Then we propose a hierarchical practical three party scheme, which provides DoS resistance along with low communication cost, reliability, independence of prior deployment knowledge and support of node mobility, i.e. establishing secure channel with a node, which is not met before.