Miracle-dz/UniFAR-FTUs huggingface dataset:
Relaxed Fault-Tolerant Hardware Implementation of Neural Networks in the Presence of Multiple Transient Errors

Reliability should be identified as the most important challenge in future nano-scale very large scale integration (VLSI) implementation technologies for the development of complex integrated systems. Normally, fault tolerance (FT) in a conventional system is achieved by increasing its redundancy, which also implies higher implementation costs and lower performance that sometimes makes it even infeasible. In contrast to custom approaches, a new class of applications is categorized in this paper, which is inherently capable of absorbing some degrees of vulnerability and providing FT based on their natural properties. Neural networks are good indicators of imprecision-tolerant applications. We have also proposed a new class of FT techniques called relaxed fault-tolerant (RFT) techniques which are developed for VLSI implementation of imprecision-tolerant applications. The main advantage of RFT techniques with respect to traditional FT solutions is that they exploit inherent FT of different applications to reduce their implementation costs while improving their performance. To show the applicability as well as the efficiency of the RFT method, the experimental results for implementation of a face-recognition computationally intensive neural network and its corresponding RFT realization are presented in this paper. The results demonstrate promising higher performance of artificial neural network VLSI solutions for complex applications in faulty nano-scale implementation environments.
Finite element resolution of the 3D stationary semiconductor device equations on multiprocessors

This paper describes a three-dimensional simulator for semiconductor devices in stationary state developed on a message passing multiprocessor. A Gummel style nonlinear iteration was used, and the Poisson and the continuity equations were discretized using the finite element method. A new iterative scheme was introduced in order to solve the nonlinear Poisson equation. The nonsymmetric continuity equations were solved using a Conjugate Gradient type method, the Bi-CGSTAB. We carried out the parallelization of these approaches and their mapping onto a multiprocessor system. We introduce a new method for the distribution of the finite element sparse matrices over the multiprocessor. This method leads to highly efficient algorithms, because it obtains a good load balance and limits all the communications to length 1. © 1996 John Wiley & Sons, Inc.
FGFR2 Is Amplified in the NCI-H716 Colorectal Cancer Cell Line and Is Required for Growth and Survival

Aberrant kinase activation resulting from mutation, amplification, or translocation can drive growth and survival in a subset of human cancer. FGFR2 is amplified in breast and gastric cancer, and we report here the first characterization of FGFR2 gene amplification in colorectal cancer in the NCI-H716 colorectal cancer cell line. FGFR2 is highly expressed and activated in NCI-H716 cells, and FGFR selective small molecule inhibitors or FGFR2 shRNA strongly inhibited cell viability in vitro, indicating “addiction” of NCI-H716 cells to FGFR2. NCI-H716 growth in a xenograft model was also inhibited by an FGFR small molecule inhibitor. FGFR2 was required for activation of multiple downstream signaling proteins including AKT, ERK, S6RP and NFKB. Inhibition of downstream kinases such as AKT or ERK alone had modest effects on proliferation, whereas combined inhibition of AKT and ERK signaling resulted in a loss of viability similar to FGFR2 inhibition. We identified elevated FGFR2 expression in a small subset of primary colorectal cancer, however FGFR2 amplification was not observed. Although FGFR2 amplification is not common in primary colon cancer or lymph node and liver metastases, other subsets of colorectal cancer such as ascites, from which the NCI-H716 cell line was derived, have yet to be tested. These results suggest that emerging FGFR inhibitor therapeutics may have efficacy in a subset of colon cancer driven by FGFR2 amplification.
Classroom Communities of Practice

Are classrooms preparing students with the literacy skills they need for school, work, and life? When students don't meet expectations, educators tend to seek answers in innovative programs or research-based practices that promise success. The practices teachers use, however, are neither selected nor enacted in a vacuum. To fully understand what is happening in a classroom, one needs to consider not only the instructional practices teachers use but also the context in which teachers select and enact these practices and the effect these practices have on our students. The impetus for this study came from a broader desire to dig deeper into practices that create successful writing communities in secondary classrooms. A teacher’s discourses about writing and about her students plays a crucial role in the development of student practices in a classroom community of practice. This study used discourse analysis and interactional ethnography to focus on teachers’ talk and classroom interactions about writing in a large, suburban middle school. The researcher found clear connections between a teachers’ discourses and the practices that are integral to the classroom writing communities. This study has implications not only for teachers but also for administrators, professional development leaders, or teacher educators. Change in a classroom is not simply a matter of mandating certain programs or practices. A teacher's experiences, beliefs, and values must be addressed in reflective practice, professional development, and teacher preparation because teacher discourses shape student practices.
Engaging with Diversity: How Positive and Negative Diversity Interactions Influence Students’ Cognitive Outcomes

ABSTRACT Although a growing body of research has demonstrated the value of interacting with diverse peers, a number of questions remain about the relationship between the quality of students’ diversity interactions and their cognitive development. In a longitudinal study following 3 cohorts of students from entry into college through the end of their 4th year, we examined how students’ positive and negative diversity interactions were related to 2 different outcomes: need for cognition and critical thinking skills. The results indicated that negative diversity interactions were strongly related to both outcomes, and that was the case for students of color and their white peers. Positive diversity interactions, on the other hand, were related to students’ need for cognition but not their critical thinking skills, and these interactions disproportionately benefitted white students. We conclude by considering the implications for understanding students’ cognitive development and implementing policies and practices that can facilitate positive outcomes on college campuses.
Intracellular Patch Electrochemistry: Regulation of Cytosolic Catecholamines in Chromaffin Cells

Alterations in the cytosolic pool directly affect neurotransmitter synthesis and release and are suggested to be key factors in various neurodegenerative disorders. Although this cytosolic pool is the most metabolically active, it is miniscule compared with the amount of vesicular transmitter and has never been quantified separately. Here, we introduce intracellular patch electrochemistry (IPE), a technique that for the first time provides direct measurements of cytosolic oxidizable molecules in single mammalian cells. In amperometric mode, IPE detects total catechols, whereas in cyclic voltammetric mode, it preferentially measures catecholamines. In cultured chromaffin cells, the total cytosolic catechol concentration was 50–500 μm, of which ∼10% were catecholamines. Reserpine, a vesicular monoamine transporter inhibitor, had no effect on the catecholamine pool but increased total catechols by fourfold to fivefold. Combined with pargyline, a monoamine oxidase inhibitor, reserpine increased catecholamine levels in the cytosol by approximately sixfold. Amphetamine induced a transient approximately fivefold accumulation of cytosolic catecholamines and a slow increase of total catechols. In cells incubated with 3,4-dihydroxy-L-phenylalanine (l-DOPA), catecholamines increased by ∼2.5-fold and total catechols increased by approximately fourfold. Cytosolic catecholamines returned to control levels ≤10 min after l-DOPA withdrawal, whereas total catechols remained approximately twofold elevated even after a 1.5 hr incubation in l-DOPA-free media. Our data indicate that cytosolic catecholamines are strictly maintained at a defined level, and drug-induced increases in their concentrations lead to the accumulation of other catecholamine derivatives, such as DOPAC and 3,4-dihydroxyphenylethyleneglycol. These derivatives reside in the cytosol for hours after treatment and may be an underlying cause of drug-related cytotoxicity.
Transition from Growth Cone to Functional Motor Nerve Terminal inDrosophila Embryos

As a motor axon grows from the CNS to its target muscle, the terminal has the form of a flattened growth cone with a planar central region, lamellipodia, and filopodia. A mature terminal usually has a stereotyped shape that may be elongated with varicosities, as in several invertebrate species, or have short branches with boutons, as in mammals. We examined in Drosophila the developmental changes between growth cone and mature terminal using ultrastructural and immunocytochemical methods. The transition period, which occurs 2–3 hr after the first growth cone reaches its target muscle, is marked by the formation of “prevaricosities,” smoothly contoured enlargements of the axons at the point where the nerve trunk first contacts the muscle fiber (MF). There is a 15–30 min ventral-to-dorsal gradient in the formation of prevaricosities on the individual abdominal MFs. Multineuronal innervation of each MF has occurred by this time, and two or more different axons undergo prevaricosity formation while they are intimately intertwined at the nerve entry point (NEP). Presynaptic active zones, both nerve–nerve and nerve–muscle, occur within the prevaricosities along broad contact regions. Synaptotagmin immunoreactive clusters form concurrently. The first varicosities then develop as a result of constrictions of the larger prevaricosities rather than as enlargement of discrete portions of the filopodia or neurites. The prevaricosity stage therefore may include the key steps that lead to the differentiation of functional differences in terminal subtypes as well as those leading to the formation of a stable neuromuscular junction.
and their relationship with quality of life

The growing interest in the mental health and quality of life of cancer patients, has been the major reason for conducting this study. The aims were to compare advanced cancer patients' responses to Hospital Anxiety and Depression (HAD) scale with those to European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30, version 3.0), as well as the impact of quality of life dimensions (as measured by EORTC QLQ-C30) on the levels of anxiety and depression. The analysis, conducted in 120 advanced cancer patients, showed that the most significant associations were found between emotional functioning and HAD-T (total sum of scores) (r=-0.747; p < 0.0005), HAD-A (anxiety) (r = -0.725; p < 0.0005) and HAD-D (depression) (r = -0.553; p < 0.0005). In the prediction of HAD-T, the contribution of physical, emotional, role, and social functioning along with nausea-vomiting, dyspnea, sleep disturbance and gender is high. For anxiety, the predictor variables were physical, role, cognitive, emotional, and social functioning, followed by dyspnea, sleep disturbance, and appetite loss, while depression was predicted by physical, role, emotional, and social functioning, the symptoms of nausea-vomiting, pain, sleep disturbance, constipation, as well as the variables of age, gender, anticancer treatment and performance status. Concluding, psychological morbidity, in this patient population, was predominantly predicted by the emotional functioning dimension of EORTC QLQ-C30.
Pseudo relevance feedback using semantic clustering in relevance language model

Pseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an interaction of a set of independent hidden topics, we propose a novel semantic clustering technique using independent component analysis. Then within the language modeling framework, we apply the obtained semantic topic clusters into the query sampling process so that the sampling depends on the activated topics rather than on the individual document language model. Therefore, we obtain a semantic cluster based relevance language model, which uses pseudo relevance feedback technique without requiring any relevance training information. We applied the model on five TREC data sets. The experiments show that our model can significantly improve retrieval performance over traditional language models including relevance-based and clustering-based retrieval language models. The main contribution of the improvements comes from the estimation of the relevance model on the semantic clusters that are closely related to the query.
Performance analysis of LTE networks in different transmission modes using 16-QAM under fading channels

3GPP group standardized Long Term Evolution (LTE) and it is designed to have transmission bandwidth up to 20MHz, with low latency and packet optimized radio access technology. The maximum data rate contemplated for LTE is 100 Mbps in downlink and 50 Mbps in the uplink. The 3GPP selected Orthogonal Frequency Division Multiple Access as the radio access technology because it can be simply implemented in receiver with high performance and high spectral efficiency. In LTE-Advanced (LTE-A) various MIMO transmission methods are used to achieve high data rate compliance with the LTE standards. This paper investigates the performance analysis of LTE-A networks in different transmission modes using 16-QAM, under different fading channels with different antenna correlation conditions. This work deals with the study of performance based on Release 10 of the 3GPP standard. Bit Error Rate (BER) is evaluated in terms of Signal to Noise Ratio (SNR) using low and high channel distortion. The BER analysis for Single Input Multiple Output (SIMO),Transmit diversity, Open and Closed Loop Spatial Multiplexing and it is carried out for 2×2 and 4×4 antenna configuration.
Comprehensive modeling and control strategies for a three-phase dual-active bridge

The dual-active bridge (DAB) is a dc-dc converter, which offers several advantages especially in high-power applications. Besides bidirectional power flow and small filter size, the converter provides galvanic isolation via a medium-frequency transformer. The converter's inherent soft-switching capability promises very high efficiency due to the reduction of switching losses. In this paper, different modeling approaches are derived that cover the dynamic behavior of the three-phase dual-active bridge. The first approach utilizes a state-space averaging method in conjunction with state-variable averaging. The second modeling approach applies a first-harmonic approximation. After the comparison of these two different models with a circuit simulation, a control design method is developed based on those models. The control is evaluated for different converter parameters. Finally, the developed control is implemented using a DSP-controlled laboratory prototype of the converter and the results are presented.
Bisphenol A Exposure Disrupts Genomic Imprinting in the Mouse

Exposure to endocrine disruptors is associated with developmental defects. One compound of concern, to which humans are widely exposed, is bisphenol A (BPA). In model organisms, BPA exposure is linked to metabolic disorders, infertility, cancer, and behavior anomalies. Recently, BPA exposure has been linked to DNA methylation changes, indicating that epigenetic mechanisms may be relevant. We investigated effects of exposure on genomic imprinting in the mouse as imprinted genes are regulated by differential DNA methylation and aberrant imprinting disrupts fetal, placental, and postnatal development. Through allele-specific and quantitative real-time PCR analysis, we demonstrated that maternal BPA exposure during late stages of oocyte development and early stages of embryonic development significantly disrupted imprinted gene expression in embryonic day (E) 9.5 and 12.5 embryos and placentas. The affected genes included Snrpn, Ube3a, Igf2, Kcnq1ot1, Cdkn1c, and Ascl2; mutations and aberrant regulation of these genes are associated with imprinting disorders in humans. Furthermore, the majority of affected genes were expressed abnormally in the placenta. DNA methylation studies showed that BPA exposure significantly altered the methylation levels of differentially methylated regions (DMRs) including the Snrpn imprinting control region (ICR) and Igf2 DMR1. Moreover, exposure significantly reduced genome-wide methylation levels in the placenta, but not the embryo. Histological and immunohistochemical examinations revealed that these epigenetic defects were associated with abnormal placental development. In contrast to this early exposure paradigm, exposure outside of the epigenetic reprogramming window did not cause significant imprinting perturbations. Our data suggest that early exposure to common environmental compounds has the potential to disrupt fetal and postnatal health through epigenetic changes in the embryo and abnormal development of the placenta.
High Accuracy Clustering Algorithm for Categorical Dataset

Step by step operations by which we make a group of objects in which attributes of all the objects are nearly similar, known as clustering. So, a cluster is a collection of objects that acquire nearly same attribute values. The property of an object in a cluster is similar to other objects in same cluster but different with objects of other clusters. Clustering is used in wide range of applications like pattern recognition, image processing, data analysis, machine learning etc. Nowadays, more attention has been put on categorical data rather than numerical data. Where, the range of numerical attributes organizes in a class like small, medium, high, and so on. There is wide range of algorithm that used to make clusters of given categorical data. Our approach is to enhance the working on wellknown clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Change of Topics over Time - Tracking Topics by their Change of Meaning

In this paper we present a new approach to the analysis of topics and their dynamics over time. Given a large amount of news text on a daily basis, we have identified “hotly discussed” concepts by examining the contextual shift between the time slices. We adopt the volatility measure from econometrics and propose a new algorithm for frequency-independent detection of topic drift.
DC voltage calibrator based on an array of high-temperature superconductor Josephson junctions

We are using a quantum DC voltage generated on an array of high-temperature superconductor (HTS) junctions in the developed voltage calibrator with an output voltage up to 10 V. The described concept and the demonstrated parameters of the main parts of the instrument show that the measurement uncertainty of the output voltage will be a few parts in 10/sup 8/.
Convergence and divergence in the evolution of aquatic birds

Aquatic birds exceed other terrestrial vertebrates in the diversity of their adaptations to aquatic niches. For many species this has created difficulty in understanding their evolutionary origin and, in particular, for the flamingos, hamerkop, shoebill and pelecaniforms. Here, new evidence from nuclear and mitochondrial DNA sequences and DNA–DNA hybridization data indicates extensive morphological convergence and divergence in aquatic birds. Among the unexpected findings is a grouping of flamingos and grebes, species which otherwise show no resemblance. These results suggest that the traditional characters used to unite certain aquatic groups, such as totipalmate feet, foot-propelled diving and long legs, evolved more than once and that organismal change in aquatic birds has proceeded at a faster pace than previously recognized.
Localization of multiple sources from a binaural head in a known noisy environment

This paper presents a strategy to the localization of multiple sound sources from a static binaural head. The sources are supposed W-Disjoint Orthogonal and their number is assumed known. Their most likely azimuths are computed by means of the Expectation-Maximization algorithm. Application of the method on simulated data is reported, as well as some evaluations of its HARK implementation on experimental data. Two important properties are observed: scattering effects can be coped with, thanks to the required prior knowledge of the (room-independent) head interaural transfer function; the environment noise statistics are handled separately.
Network Latency Estimation for Personal Devices: A Matrix Completion Approach

Network latency prediction is important for server selection and quality-of-service estimation in real-time applications on the Internet. Traditional network latency prediction schemes attempt to estimate the latencies between all pairs of nodes in a network based on sampled round-trip times, through either Euclidean embedding or matrix factorization. However, these schemes become less effective in terms of estimating the latencies of personal devices, due to unstable and time-varying network conditions, triangle inequality violation and the unknown ranks of latency matrices. In this paper, we propose a matrix completion approach to network latency estimation. Specifically, we propose a new class of low-rank matrix completion algorithms, which predicts the missing entries in an extracted “network feature matrix” by iteratively minimizing a weighted Schatten- $p$ norm to approximate the rank. Simulations on true low-rank matrices show that our new algorithm achieves better and more robust performance than multiple state-of-the-art matrix completion algorithms in the presence of noise. We further enhance latency estimation based on multiple “frames” of latency matrices measured in the past, and extend the proposed matrix completion scheme to the case of 3-D tensor completion. Extensive performance evaluations driven by real-world latency measurements collected from the Seattle platform show that our proposed approaches significantly outperform various state-of-the-art network latency estimation techniques, especially for networks that contain personal devices.