Miracle-dz/UniFAR-FTUs huggingface dataset:
A deterministic dynamic element matching approach for testing high-resolution ADCs with low-accuracy excitations

Dynamic element matching (DEM) is capable of providing good average linearity performance in matching critical circuits in the presence of major component mismatch, but the approach has received minimal industrial adoption outside of Sigma-Delta structures because of challenges associated with implementation of a required randomizer and because of the time-local nonstationarity. This paper presents a DEM approach to analog-to-digital converter (ADC) testing in which low-precision DEM digital-to-analog converters (DACs) are used to generate stimulus signals for ADCs under test. It is shown that in a testing environment, this approach provides very high precision test results, and time-local nonstationarity is of no concern. In addition to traditional random DEM techniques, a deterministic DEM (DDEM) strategy that eliminates the need for a randomizer is introduced. The performance of the DDEM method is established mathematically and validated with detailed simulation results. Furthermore, the DDEM method requires far fewer samples to achieve the same level of average linearity than the random DEM approach. It is demonstrated that both the random DEM and DDEM methods can be used to accurately test ADCs with linearity that far exceeds that of the DAC used as a signal generator. This technique of using imprecise excitations and DEM to test much more accurate ADCs offers potential for use in both production test and built-in self-test environments where high linearity test sources are difficult to implement
A novel low power energy recovery full adder cell

A novel low power and low transistor count static energy recovery full adder (SERF) is presented in this paper. The power consumption and general characteristics of the SERF adder are then compared against three low powerful adders; the transmission function adder (TFA) the dual value logic (DVL) adder and the fourteen transistor (14 T) full adder. The proposed SERF adder design was proven to be superior to the other three designs in power dissipation and area, and second in propagation delay only to the DVL adder. The combination of low power and low transistor count makes the new SERF cell a viable option for low power design.
Stem Cell Therapy and Congenital Heart Disease

For more than a decade, stem cell therapy has been the focus of intensive efforts for the treatment of adult heart disease, and now has promise for treating the pediatric population. On the basis of encouraging results in the adult field, the application of stem cell-based strategies in children with congenital heart disease (CHD) opens a new therapy paradigm. To date, the safety and efficacy of stem cell-based products to promote cardiac repair and recovery in dilated cardiomyopathy and structural heart disease in infants have been primarily demonstrated in scattered clinical case reports, and supported by a few relevant pre-clinical models. Recently the TICAP trial has shown the safety and feasibility of intracoronary infusion of autologous cardiosphere-derived cells in children with hypoplastic left heart syndrome. A focus on preemptive cardiac regeneration in the pediatric setting may offer new insights as to the timing of surgery, location of cell-based delivery, and type of cell-based regeneration that could further inform acquired cardiac disease applications. Here, we review the current knowledge on the field of stem cell therapy and tissue engineering in children with CHD, and discuss the gaps and future perspectives on cell-based strategies to treat patients with CHD.
Targeting RNA by Small Molecules: Comparative Structural and Thermodynamic Aspects of Aristololactam-β-D-glucoside and Daunomycin Binding to tRNAphe

Background Interaction of aristololactam-β-D-glucoside and daunomycin with tRNAphe was investigated using various biophysical techniques. Methodology/Principal Findings Absorption and fluorescence studies revealed that both the compounds bind tRNAphe non-cooperatively. The binding of daunomycin was about one order of magnitude higher than that of aristololactam-β-D-glucoside. Stronger binding of the former was also inferred from fluorescence quenching data, quantum efficiency values and circular dichroic results. Results from isothermal titration calorimetry experiments suggested that the binding of both compounds was predominantly entropy driven with a smaller but favorable enthalpy term that increased with temperature. A large favorable electrostatic contribution to the binding of daunomycin to tRNAphe was revealed from salt dependence data and the dissection of the free energy values. The electrostatic component to the free energy change for aristololactam-β-D-glucoside-tRNAphe interaction was smaller than that of daunomycin. This was also inferred from the slope of log K versus [Na+] plots. Both compounds enhanced the thermal stability of tRNAphe. The small heat capacity changes of -47 and -99 cal/mol K, respectively, observed for aristololactam-β-D-glucoside and daunomycin, and the observed enthalpy-entropy compensation phenomenon confirmed the involvement of multiple weak noncovalent interactions. Molecular aspects of the interaction have been revealed. Conclusions/Significance This study presents the structural and eneregetic aspects of the binding of aristololactam-β-D-glucoside and daunomycin to tRNAphe.
Thinned Planar Array Design Using Boolean PSO With Velocity Mutation

The design of thinned planar microstrip arrays under specific constraints concerning the impedance-matching condition of the array elements and the radiation pattern is presented. The radiation characteristics of the structure are extracted by applying the method-of-moments. The array design is based on a novel optimization method, which is a modified version of the boolean particle swarm optimization that employs velocity mutation (BPSO-vm). Apart from the optimization of the array geometry, the proposed method is applicable to other discrete-variable optimization problems. Moreover, the planar array design is coped with by means of other techniques, namely, a binary coded Genetic Algorithm, the binary Particle Swarm Optimization, and the Boolean PSO. The comparison of the above methods and the BPSO-vm shows the efficiency of the proposed technique.
Time frequency signatures of micro-Doppler phenomenon for feature extraction

In this paper, we introduce the concept of micro-Doppler phenomenon. Micro-Doppler induced by mechanical vibrating or rotating of structures in a target is potentially useful for detection, classification and recognition of targets. While the Doppler frequency due to target body is constant, the micro-Doppler due to vibration or rotating structures of a target is a function of the dwell time. This time-varying Doppler signature in the time-frequency domain may provide further information for target detection, classification and recognition.
Análisis integral como sistema de medición y gestión de capitales intangibles en organizaciones y territorios

In the knowledge society, it is necessary to have new approaches of measurement and management for all the intangible ones that generate value, not only for companies but also for the national, regional or local territories. The most widespread methods have begun in the business area; these are support to the macro systems implemented in the last years, under the hypothesis of the knowledge as newly generator of wealth. In this context, let's sense beforehand the method of Integral Analysis as valid system for the measurement of the intangible capitals in the organizations, with advantages on his promoter: the Skandia Navigator; also, as tool for the management, planning and strategic control, advancing on the Balanced Scorecard; and finally, from a macroeconomic perspective, as generator of competitiveness indicators and territorial factors of growth En la sociedad del conocimiento, resulta necesario disponer de nuevos instrumentos de medicion y de gestion para todos los intangibles que generan valor, no solamente para las organizaciones empresariales sino tambien para los territorios nacionales, regionales o locales. Los metodos mas extendidos se han iniciado en el ambito empresarial, sirviendo de soporte a los modelos macro implementados en los ultimos anos, bajo la hipotesis del conocimiento como nuevo generador de riqueza. En este marco, presentamos el metodo de Analisis Integral como sistema valido para la medicion de capitales intangibles en las organizaciones, con ventajas sobre su promotor: el Navegador de Skandia. Tambien, como herramienta para la gestion, planificacion y auditoria de la estrategia, avanzando sobre el Cuadro de Mando Integral. Por ultimo, desde una perspectiva macroeconomica, como generador de indicadores de competitividad y factores de crecimiento para los territorios.
A NEUTROPHIL-DEPENDENT PATHWAY FOR THE GENERATION OF A NEUTRAL PEPTIDE MEDIATOR

A biologically active neutral peptide mediator is cleaved from a plasma protein substrate by an α-1-antitrypsin-inhibitable serine protease apparently residing on the membrane of the human neutrophil. The peptide mediator has an approximate mol wt of 1,000, and is distinguished from the kinin peptides by a neutral isoelectric point, susceptibility to inactivation by trypsin as well as chymotrypsin and activity on the isolated, atropinized, and antihistamine-treated guinea pig ileum with relatively little action on the estrous rat uterus. The neutrophil protease is fully inhibitable by DFP, trypsin inhibitors from lima or soy bean, and α-1-antitrypsin and is associated with the high mol wt fragments of the neutrophil and not the nuclear, lysosomal, or cytoplasmic subcellular fraction. The substrate has an approximate mol wt of 90,000 and is chromatographically separable from kininogen. The exquisite sensitivity of the neutrophil protease to α-1-antitrypsin was established both by inhibition with highly purified α-1-antitrypsin and by the inability of the protease to generate detectable neutral peptide in a homozygous (ZZ) α-1-antitrypsin-deficient patient without heat inactivation of the residual inhibitor. On the other hand, plasma from a (null) α-1-antitrypsin-deficient patient supported neutral peptide generation and revealed an additional factor which inactivated neutral peptide.
Error rate performance of coded free-space optical links over strong turbulence channels

Error control coding can be used over free-space optical (FSO) links to mitigate turbulence-induced fading. We present error rate performance bounds for coded FSO communication systems operating over atmospheric turbulence channels, which are modeled as a correlated K distribution under strong turbulence conditions. We derive an upper bound on the pairwise error probability (PEP) and then apply the union-bound technique in conjunction with the derived PEP to obtain upper bounds on the bit error rate. Simulation results are further demonstrated to verify the analytical results.
Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning

Human activity recognition is one of the important research topics in computer vision and video understanding. It is often assumed that high quality video sequences are available for recognition. However, relaxing such a requirement and implementing robust recognition using videos having reduced data rates can achieve efficiency in storing and transmitting video data. Three-dimensional video scalability, which refers to the possibility of reducing spatial, temporal, and quality resolutions of videos, is an effective way for flexible representation and management of video data. In this paper, we investigate the impact of the video scalability on multi-view activity recognition. We employ both a spatiotemporal feature extraction-based method and a deep learning-based method using convolutional and recurrent neural networks. The recognition performance of the two methods is examined, along with in-depth analysis regarding how their performance vary with respect to various scalability combinations. In particular, we demonstrate that the deep learning-based method can achieve significantly improved robustness in comparison to the feature-based method. Furthermore, we investigate optimal scalability combinations with respect to bitrate in order to provide useful guidelines for an optimal operation policy in resource-constrained activity recognition systems.
Classifying French Verbs Using French and English Lexical Resources

We present a novel approach to the automatic acquisition of a Verbnet like classification of French verbs which involves the use (i) of a neural clustering method which associates clusters with features, (ii) of several supervised and unsupervised evaluation metrics and (iii) of various existing syntactic and semantic lexical resources. We evaluate our approach on an established test set and show that it outperforms previous related work with an F-measure of 0.70.
Lightweight Indexing of Observational Data in Log-Structured Storage

Huge amounts of data are being generated by sensing devices every day, recording the status of objects and the environment. Such observational data is widely used in scientific research. As the capabilities of sensors keep improving, the data produced are drastically expanding in precision and quantity, making it a write-intensive domain. Log-structured storage is capable of providing high write throughput, and hence is a natural choice for managing large-scale observational data. In this paper, we propose an approach to indexing and querying observational data in log-structured storage. Based on key traits of observational data, we design a novel index approach called the CR-index (Continuous Range Index), which provides fast query performance without compromising write throughput. It is a lightweight structure that is fast to construct and often small enough to reside in RAM. Our experimental results show that the CR-index is superior in handling observational data compared to other indexing techniques. While our focus is scientific data, we believe our index will be effective for other applications with similar properties, such as process monitoring in manufacturing.
Bioinformatics Applications Note Systems Biology Genssi: a Software Toolbox for Structural Identifiability Analysis of Biological Models

Mathematical modeling has a key role in systems biology. Model building is often regarded as an iterative loop involving several tasks, among which the estimation of unknown parameters of the model from a certain set of experimental data is of central importance. This problem of parameter estimation has many possible pitfalls, and modelers should be very careful to avoid them. Many of such difficulties arise from a fundamental (yet often overlooked) property: the so-called structural (or a priori) identifiability, which considers the uniqueness of the estimated parameters. Obviously, the structural identifiability of any tentative model should be checked at the beginning of the model building loop. However, checking this property for arbitrary non-linear dynamic models is not an easy task. Here we present a software toolbox, GenSSI (Generating Series for testing Structural Identifiability), which enables non-expert users to carry out such analysis. The toolbox runs under the popular MATLAB environment and is accompanied by detailed documentation and relevant examples. Availability: The GenSSI toolbox and the related documentation are available at
First observation of 2S1 and study of the splitting of the football mode 0S2 after the June 2001 Peru earthquake of magnitude 8.4

The recent Peru earthquake with a moment magnitude Mw = 8.4 on June 23, 2001 presents a good opportunity for the observation of the seismic gravest normal modes. For the longest periods, below 1 mHz Superconducting Gravimeters (SGs) have been shown to be less noisy than long‐period seismometers. A stack based on the spatial and temporal properties of the modes of degree one in a spherical harmonic development applied to 164 h of common records at the Cantley, Canberra, Strasbourg, Sutherland and Vienna SG stations has led to the first clear observation of the 2S1 long‐period seismic modes with a central frequency close to 0.4 mHz. Moreover the spectrum of the SG record at the Strasbourg station confirms the benefit SGs can provide to normal mode seismology with the clear observation of the splitting of the fundamental mode 0S2 into 5 fairly well resolved singlets with only one record.
Efficiently Finding Top-K Items from Evolving Distributed Data Streams

The problem of efficiently finding top-k frequent items has attracted much attention in recent years. Storage constraints in the processing node and intrinsic evolving feature of the data streams are two main challenges. In this paper, we propose a method to tackle these two challenges based on space-saving and gossip-based algorithms respectively. Our method is implemented on SAMOA, a scalable advanced massive online analysis machine learning framework. The experimental results show its effectiveness and scalability.
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector w* e Rd that represents the "qualities" of the d items being compared; this class of models includes the two most widely used parametric models|the Bradley-Terry-Luce (BTL) and the Thurstone models. Working within a standard minimax framework, we provide tight upper and lower bounds on the optimal error in estimating the quality score vector w* under this class of models. The bounds depend on the topology of the comparison graph induced by the subset of pairs being compared, via the spectrum of the Laplacian of the comparison graph. Thus, in settings where the subset of pairs may be chosen, our results provide principled guidelines for making this choice. Finally, we compare these error rates to those under cardinal measurement models and show that the error rates in the ordinal and cardinal settings have identical scalings apart from constant pre-factors.
Common Failings: How Corporate Defaults are Correlated

We develop, and apply to data on U.S. corporations from 1979-2004, tests of the standard doubly-stochastic assumption under which firms'default times are correlated only as implied by the correlation of factors determining their default intensities. This assumption is violated in the presence of contagion or "frailty" (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the time-series properties of default intensities. The data do not support the joint hypothesis of well specified default intensities and the doubly-stochastic assumption. There is also some evidence of default clustering in excess of that implied by the doubly-stochastic model with the given intensities.
Secure communication in multiple relay networks through decode-and-forward strategies

In this paper, we study the role of cooperative relays to provide and improve secure communication rates through decode-and-forward (DF) strategies in a full-duplex multiple relay network with an eavesdropper. We consider the DF scheme as a basis for cooperation and propose several strategies that implement different versions of this scheme suited for cooperation with multiple relays. Our goal is to give an efficient cooperation paradigm based on the DF scheme to provide and improve secrecy in a multiple relay network. We first study the DF strategy for secrecy in a single relay network. We propose a suboptimal DF with zero forcing (DF/ZF) strategy for which we obtain the optimal power control policy. Next, we consider the multiple relay problem. We propose three different strategies based on DF/ZF and obtain their achievable secrecy rates. The first strategy is a single hop strategy whereas the other two strategies are multiple hop strategies. In the first strategy, we show that it is possible to eliminate all the relays' signals from the eavesdropper's observation (full ZF), however, the achievable secrecy rate is limited by the worst source-relay channel. Our second strategy overcomes the drawback of the first strategy, however, with the disadvantage of enabling partial ZF only. Our third strategy provides a reasonable compromise between the first two strategies. That is, in this strategy, full ZF is possible and the rate achieved does not suffer from the drawback of the first strategy. We conclude our study by a set of numerical results to illustrate the performance of each of the proposed strategies in terms of the achievable rates in different practical scenarios.
Programmable deterministic Built-In Self-Test

In this paper, we propose a new programmable deterministic built-in self-Test (BIST) method that requires significantly lower storage for deterministic patterns than existing programmable methods and provides high flexibility for test engineering in both internal and external test. Theoretical analysis suggests that significantly more care bits can be encoded in the seed of a linear feedback shift register (LFSR), if a limited number of conflicting equations is ignored in the employed linear equation system. The ignored care bits are separately embedded into the LFSR pattern. In contrast to known deterministic BIST schemes based on test set embedding, the embedding logic function is not hardwired. Instead, this information is stored in memory using a special compression and decompression method. Experiments for benchmark circuits and industrial designs demonstrate that the approach has considerably higher overall coding efficiency than the existing methods.
Subsurface monitoring using low frequency wireless signal networks

The Wireless Signal Networks (WSNs) use the signal strength variation of wireless nodes in the host medium as the main indicator of events or physical property changes in the medium. With the concept and application of the Underground Wireless Signal Networks (UWSNs) where the host medium is a soil, the real-time global subsurface monitoring of wireless underground sensor networks can be achieved, whereas traditional wireless sensor networks provide point sensing and monitoring. In this paper, we present two representative experiments of subsurface monitoring such as leakage monitoring and earthquake monitoring using wireless sensor nodes and UWSNs. However, the deployment of underground wireless signal networks has more constraint than aboveground deployment, because the radio signal attenuation is much higher than above ground communication. To realize UWSNs for practical subsurface monitoring applications, it is necessary to extend the underground communication radius. In the paper, we provide the estimations of extending underground communication radius of underground wireless nodes using the underground radio propagation model [1]. Based on the estimations, we introduce a concept of Low Frequency Wireless Signal Networks (LFWSNs) for subsurface monitoring applications as well as experimental platforms for the low frequency wireless signal networks.