Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the impact of architectural parameters on energy efficiency of application-specific block-enabled SRAMs

Application-Specific Block-Enabled (ASBE) SRAMs represent a viable solution for reducing energy consumption in embedded memories. The basic idea behind ASBE architectures is that of partitioning the memory array into a number of non-uniformly sized blocks, such that memory access cost is reduced. The number and sizes of the partitions yielding a minimum power implementation of the SRAM macro is determined by the partitioning algorithm based on the memory access profile obtained as a result of the application (or application mix) executed by the processor. Given the complexity of the design space we are dealing with, there are several degrees of freedom that the partitioning engine may exploit to come up with the most energy-efficient memory architecture. In this paper, we investigate how the quality of the partitioned memory depends on the architectural parameters that define the memory structure (e.g., min and max number of lines per partition, min and max number of words per line, granularity of the partitions); such parameters, in turn, are constrained by the technology and process of choice. We believe that the results presented in this work will provide very useful guidelines for a succesfull adoption of the ASBE approach in practice, as this design paradigm is gaining a lot of attention for the new generations of embedded systems.
Regulated Compartmentalization of Programmed Cell Death-1 Discriminates CD4+CD25+ Resting Regulatory T Cells from Activated T Cells1

More effective discrimination between CD4+CD25+ regulatory T cells (Treg) and activated T cells would significantly improve the current level of purification of Treg and their therapeutic application. We observed that ∼90% of Treg (positive for the nuclear transcription factor Forkhead winged helix protein-3 and able to inhibit naive T cell proliferation) isolated from the spleens or lymph nodes of normal mice did not express significant levels of the inhibitory receptor programmed cell death-1 (PD-1) on their surface, but retained PD-1 intracellularly. An identical phenotype was also identified for human CD4+CD25high T cells isolated from peripheral blood of healthy volunteers. By contrast, activated T cells expressed high levels of surface PD-1 that paralleled up-regulation of CD25 during effector cell expansion. This distinction allowed us to isolate CD4+CD25+PD-1− T cells with suppressive activity from mice immunized with mature allogeneic dendritic cells. Although purification was limited to resting Treg because TCR ligation induced up-regulation of surface PD-1, this strategy nevertheless represents a valuable step toward more definitive characterization of Treg and their improved purification for therapeutic assessment.
Piezoresistivity in Microsystems

This thesis is a contribution to the research in piezoresistive micro electro mechanical system (MEMS) sensors. Today, a wide range of piezoresistive silicon sensors are commercially available. This thesis focuses on experimental characterization of piezoresistive materials all based on silicon micro and nanotechnology fabrication techniques. With a basis in the characterization of silicon, the piezoresistive properties of other materials are explored in order to search for materials for use in highly sensitive sensors. The piezoresistive properties of silicon are described by the three piezocoefficients, π11, π12, and π44. In this thesis the fundamental theory of piezoresistivity is explained and different experimental piezoresistance characterization structures are suggested. In order to realize the experimental characterization a four point bending fixture is designed and fabricated. The setup includes heaters embedded in the housing and thermocouples in order to allow for precise temperature control. The setup applies a uniaxial stress to the inserted chip and provides a high precision measurement of π44 in p-type silicon with an uncertainty of 1.8%. By using the four point bending setup the piezocoefficients of n-type silicon are experimentally determined as a function of doping concentration ND and temperature T : π11 =−97 ·10−11 Pa−1, π12 = 43 ·10−11 Pa−1, and π44 =−12 ·10−11 Pa−1 (ND = 5.1 ·1017 cm−3, T = 30◦C). In p-type silicon the experimental characterization gives: π44 = 118 ·10−11 Pa−1 at a doping concentration of NA = 1.5 ·1017 cm−3 and T = 30◦C. The temperature dependency of the piezocoefficients decreases for increasing doping concentration . A theoretical model is developed in order to describe how π44 in p-type silicon depends on the temperature and doping concentration, since this piezocoefficient has a very large technological relevance. A simple analytical fit has been applied to this model. The fit is intended to be used by experimentalists when the performance of a designed piezoresistive MEMS device is to be predicted. In the search for highly sensitive piezoresistive materials biaxially pre-strained silicon and silicon germanium grown by molecular beam epitaxy are characterized experimentally. The results show that the strain of the crystal lattice indeed influence the piezoresistive properties. By compressively straining silicon germanium π66, which is equivalent to π44 in silicon, increases up to 36% compared to π44 in silicon, and in biaxially tensile strained silicon π66 is decreased by 25% compared to π44 in silicon. The temperature dependency of the piezocoefficients is highly influenced by the strain and the experiments show that the temperature dependency of the piezocoefficient in strained silicon, 1 π66 ∂π66 ∂T = 0.1%/ ◦C, is much smaller than that of bulk silicon, 1 π44 ∂π44 ∂T = 0.35%/◦C. Silicon nanowires are characterized experimentally and the decrease in size has a large impact on the piezoresistance properties. An increase in the piezocoefficient π44 of up to approximately
Genetic analysis of coronary artery disease single-nucleotide polymorphisms in diabetic nephropathy.

BACKGROUND Diabetic nephropathy is a leading cause of end-stage renal disease. Premature mortality is common in patients with nephropathy, largely due to cardiovascular disease. Genetic variants implicated in macrovascular disease are therefore excellent candidates to assess for association with diabetic nephropathy. Recent genome-wide association studies have identified a total of 15 single-nucleotide polymorphisms (SNPs) that are reproducibly associated with cardiovascular disease. METHODS We initially assessed these SNPs for association in UK type 1 diabetic patients with (cases; n = 597) and without (controls; n = 502) nephropathy using iPLEX(TM) and TaqMan(R) assays. Replication studies were performed with DNA genotyped in a total of 2668 individuals from the British Isles. RESULTS One SNP (rs4420638) on chromosome 19q13 was found to be significantly associated with diabetic nephropathy before (P = 0.0002) and after correction for multiple testing (P(corrected) = 0.002). We replicated this finding in a phenotypically similar case-control collection comprising 709 individuals with type 1 diabetes (P = 0.002; combined P < 0.00001; OR = 1.54, 95% CI: 1.29-1.84). CONCLUSIONS Our case-control data suggest that rs4420638, or a functional SNP in linkage disequilibrium with this SNP, may be associated with diabetic nephropathy.
The Analysis of the Accuracy of Wax Casting Models for Medical Applications

The article presents an analysis of the dimensional accuracy of wax models, made by Vacuum Casting Method (VC), used in the manufacturing process of bone defects implants of the skull vault. Silicone molds were made on the basis of Rapid Prototypes (RP) models. The RP prototypes were created from 3D-CAD (CATIA V5) developed during studies of the shape of the cranial vault bone defects. The technology used in the RP process was stereolithography (SLA). During the researches also were used an additional tested models. These models were designed on the basis of the dome with cutouts of different shapes. The measurement process was conducted by optical coordinate measuring system. The research was carried out using an Atos Triple Scan scanner. The analysis presents that there is a possibility of using the technology of Vacuum Casting wax models in silicon molds, as the indirect method in the process of implementation of the implant-bone of the skull cap.
Rigorous Analysis of Kronecker Graph Algorithms

Real world graphs have been observed to display a number of surprising properties. These properties include heavy-tails for inand out-degree distributions, small diameters, and a densification law [3]. These features do not arise from the classical Erdos-Renyi random graph model [2]. To address these difficulties, Kronecker Graphs were first introduced in [3] as a new method of generating graphs which match network data. This work mathematically analyzes the convergence and runtime guarantees of generating and fitting algorithms for Kronecker Graphs. Existing references for Kronecker graphs [4] give heuristic descriptions of core algorithms, but suppress crucial mathematical details. There is source code available for these algorithms, but we don’t wish to ensnare ourselves in language dependent syntax. Thus, we start by giving clean, detailed pseudocode for the algorithms we wish to analyze. From these descriptions, we derive a number of convergence, approximation, and lower bound results.
Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data

Navigation when running is exploratory, characterised by both starting and ending in the same location, and iteratively foraging the environment to find areas with the most suitable running conditions. Runners do not wish to be explicitly directed, or refer to navigation aids that cause them to stop running, such as maps. Such undirected navigation is also common in other 'on-foot' scenarios, but how to support it is under-investigated. We contribute a novel method that uses crowd-sourced venue databases to rate a geographical area on its suitability to run in using linear regression. Our regression model is able to accurately predict the suitability of an area to run in (Pearson r=0.74) with a low mean error (RMSE=1.0). We outline how our method can support runners, and can be applied to other undirected navigation scenarios.
Composites with pineapple-leaf fibers manufactured by layered compression molding

Se utilizo el proceso de moldeo por compresion para la fabricacion de compuestos de matriz polimerica reforzados con fibra de hoja de pina. Para la fabricacion de las muestras se utilizaron cuatro laminas de polipropileno de 0.76 mm de espesor y las fibras se distribuyeron igualmente entre estas, generando un apilamiento alternado del material de la matriz y las fibras, donde las fibras de las capas mas externas se encuentran orientadas transversales a las de la capa interna. Dos tipos de fibras de hoja de pina se usaron como refuerzo: fibras sin tratamiento y fibras modificadas con un tratamiento alcalino en solucion de 10% de NaOH. Se evaluaron tambien dos porcentajes de fibra en el compuesto para determinar su efecto en las propiedades mecanicas del material. Se midio la resistencia a la tension, el modulo de elasticidad, la deformacion a la fractura, resistencia a la flexion y el modulo de flexion. La resistencia a la tension, el modulo de elasticidad, resistencia a la flexion y el modulo de flexion aumentaron con el incremento del contenido de fibra. Sin embargo, con el tratamiento alcalino no se observo ninguna mejora en dichas propiedades. Las superficies de fractura fueron examinadas mediante microscopia electronica. Se observaron algunos mecanismos de falla como separacion de las fibras, fallas en la intercara de la matriz y deformacion de la matriz.
Hybrid 3D Mass-Spring System to simulate isotropic materials

Cubic symmetric Mass-Spring Systems (MSS) for simulating isotropic elastic materials are known to be intrinsically limited by the Cauchy constraints (rari-constant theory) involving Poisson ratio of exactly ν = 1/4. To mitigate the impact of this restriction, we propose a new solution based on correction forces, which authorizes to use MSS for materials with ν ∈]0; 0.5[. The validation of our model is evaluated by comparing its response to various deformations with Finite Element Model (FEM). The results show that the proposed model is relevant to simulate any compressible isotropic elastic material, and in particular nearly incompressible (Poisson ratio ≃ 0.5) biological soft tissues for which it is dedicated.
HadoopPerceptron: a Toolkit for Distributed Perceptron Training and Prediction with MapReduce

We propose a set of open-source software modules to perform structured Perceptron Training, Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing, the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties, thus guaranties same accuracy performances as the serial Perceptron. The presented modules can be executed as stand-alone software or easily extended or integrated in complex systems. The execution of the modules applied to specific NLP tasks can be demonstrated and tested via an interactive web interface that allows the user to inspect the status and structure of the cluster and interact with the MapReduce jobs.
Three-dimensional motion tracking of coronary arteries in biplane cineangiograms

A three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69(/spl plusmn/0.06) mm over eight temporal phases and four different biplane orientations.
Nature and biology inspired approach of classification towards reduction of bias in machine learning

Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other words, classification tasks involve predictions of the values of discrete attributes. Some popular classification algorithms include Naïve Bayes and K Nearest Neighbour. The above type of classification algorithms generally involves voting towards classifying unseen instances. In traditional ways, the voting is made on the basis of any employed statistical heuristics such as probability. In Naïve Bayes, the voting is made through selecting the class with the highest posterior probability on the basis of the values of all independent attributes. In K Nearest Neighbour, majority voting is usually used towards classifying test instances. This kind of voting is considered to be biased, which may lead to overfitting. In order to avoid such overfitting, this paper proposes to employ a nature and biology inspired approach of voting referred to as probabilistic voting towards reduction of bias. An extended experimental study is reported to show how the probabilistic voting can manage to effectively reduce the bias towards improvement of classification accuracy.
Vertically oriented few-layered HfS2 nanosheets: growth mechanism and optical properties

For the first time, large-area, vertically oriented few-layered hafnium disulfide (V- HfS 2 ) nanosheets have been grown by chemical vapor deposition. The individual HfS 2 nanosheets are well [001] oriented, with highly crystalline quality. Far different from conventional van der Waals epitaxial growth mechanism for two-dimensional transition metal dichalcogenides, a novel dangling-bond-assisted self-seeding growth mechanism is proposed to describe the growth of V- HfS 2 nanosheets: difficult migration of HfS 2 adatoms on substrate surface results in HfS 2 seeds growing perpendicularly to the substrate; V- HfS 2 nanosheets inherit the growth direction of HfS 2 seeds; V- HfS 2 nanosheets further expand in the in-plane direction with time evolution. Moreover, the V- HfS 2 nanosheets show strong and broadened photons absorption from near infrared to ultraviolet; the V- HfS 2 -based photodetector exhibits an ultrafast photoresponse time of 24 ms, and a high photosensitivity ca. 103 for 405 nm laser.
Derivation of Knowledge Structures for Distributed Learning Objects

Knowledge space theory (Doignon & Falmagne, 1985; Albert & Lukas, 1999; Doignon & Falmagne, 1999) offers a rigorous and efficient formal framework for the construction, validation, and application of e-assessment and e-learning adaptive systems. This theory is at the basis of some existing e-learning and e-assessment adaptive systems in the U. S. and in Europe. Such systems are based on a fixed and local domain of knowledge, where fixed means that the domain does not change in time, and local refers to the fact that the items are stored and available locally. In this paper we present some theoretical notes on the efficient construction and application of knowledge spaces for knowledge domains that are both dynamic and distributed in space. This goes in the direction of an exploitation of new technologies like the GRID for building the next generation of learning environments.
Entropy Coding of Neuron Firings at Hippocampus CA1 for Memory Dysfunctional Mice

The aim of this research is to investigate the characterization of firing pattern of neuron at CA1 for dysfunctional memory mice via entropy coding. The spike trains were recorded at CA1 of hippocampus slice for two groups: senescence-accelerated-prone (SAM) -P/8 type (SAM-P/8) mice group and normal control mice group. Shannon entropy based on inter spike intervals (ISIs) histogram was used to measure the code of neuron activity at CA1 of hippocampus slice for two groups (10 samples for each group). The different of the entropy codes for two groups was tested by t-test. The results show that Shannon entropy forSAM-P/8 mice group was 9.30plusmn0. 44 bit, which is apparently greater than that for normal mice group, which was 7.26plusmn0. 33 bit.The conclusion is that the higher entropy value for SAM-P/8 mice group is revealed lower information level than the normal group, which suggests the dysfunction of synaptic plasticity for senescence-accelerated-prone mice. The results might support the research of memory dysfunction from the view of neural coding pattern.
Should all steps count when using a pedometer as a measure of physical activity in older adults?

PURPOSE The aim of this study was to determine if feedback on step counts from a pedometer encourages participants to increase walking. METHODS Randomly recruited older adults (n = 105) were asked to wear a pedometer for 2 wk. Half the participants were asked to monitor and record daily step counts during week 1 (feedback), then seal the pedometer shut during week 2 (no feedback). Half completed the study in reverse order. Self-reported walking was assessed via telephone interviews. RESULTS Significantly more steps were recorded per day (approximately 400 steps per day) when participants (n = 103, 63% women; mean BMI 25 +/- 4) monitored their daily step count [t(102) = -2.30, P = 0.02)] compared to the no feedback condition. There was no statistically significant difference in self-reported walking (P = 0.31) between feedback conditions. CONCLUSION The difference in daily step counts observed between conditions, while statistically significant, may not be considered clinically significant. Further, the non-significant difference in self-reported walking between conditions suggests that feedback on daily step counts from a pedometer does not encourage participants to increase their walking.
Placental Matrix Metalloproteinase—1 Expression Is Increased in Labor

Matrix metalloproteinases (MMPs) are now known to process a broad spectrum of cell surface molecules and to function in several important biological processes. Testing for differences in gene expression in human placental chorionic villi in the absence or presence of labor, using cDNA microarray analysis, revealed that labor was associated with increased expression of MMP-1 gene expression in 5 placentas collected after term normal spontaneous deliveries compared with 5 placentas collected after term nonlaboring cesarean deliveries. Fibronectin 1 and collagen XVII, 2 other proteins involved in the homeostasis of the extracellular matrix, were also found to be upregulated in labor. MMP-1 was further tested in individual samples and found to be consistently overexpressed in labor. While previous microarray analyses have focused on either uterine tissue or the fetal membranes, the data presented here indicate for the first time that placental chorionic villus genes are likely to affect the initiation of parturition through altered processing of cell surface molecules by MMP-1.
Modern optical astronomy: technology and impact of interferometry

The present ``state of the art'' and the path to future progress in high-spatial-resolution imaging interferometry is reviewed. The review begins with a treatment of the fundamentals of stellar optical interferometry, the origin, properties, and optical effects of turbulence in the Earth's atmosphere, the passive methods such as speckle interferometry that are applied on a single telescope to overcome atmospheric image degradation, and various other techniques. These topics include differential speckle interferometry, speckle spectroscopy and polarimetry, phase diversity, wave-front shearing interferometry, phase-closure methods, dark speckle imaging, as well as the limitations imposed by the detectors on the performance of speckle imaging. A brief account is given of the technological innovation of adaptive optics to compensate for atmospheric effects on the image in real time. A major advancement involves the transition from single-aperture to dilute-aperture interferometry using multiple telescopes. Therefore the review deals with recent developments involving ground-based and space-based optical arrays. Emphasis is placed on the problems specific to delay lines, beam recombination, polarization, dispersion, fringe tracking, bootstrapping, coherencing and cophasing, and recovery of the visibility functions. The role of adaptive optics in enhancing visibilities is also discussed. The applications of interferometry, such as imaging, astrometry, and nulling, are described. The mathematical intricacies of the various ``postdetection'' image-processing techniques are examined critically. The review concludes with a discussion of the astrophysical importance and the prospects of interferometry.