Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity of Neural Responses in the Brainstem during Smooth Pursuit Eye Movements Constrains the Circuit Mechanisms of Neural Integration

Neural integration converts transient events into sustained neural activity. In the smooth pursuit eye movement system, neural integration is required to convert cerebellar output into the sustained discharge of extraocular motoneurons. We recorded the expression of integration in the time-varying firing rates of cerebellar and brainstem neurons in the monkey during pursuit of step-ramp target motion. Electrical stimulation with single shocks in the cerebellum identified brainstem neurons that are monosynaptic targets of inhibition from the cerebellar floccular complex. They discharge in relation to eye acceleration, eye velocity, and eye position, with a stronger acceleration signal than found in most other brainstem neurons. The acceleration and velocity signals can be accounted for by opponent contributions from the two sides of the cerebellum, without integration; the position signal implies participation in the integrator. Other neurons in the vestibular nucleus show a wide range of blends of signals related to eye velocity and eye position, reflecting different stages of integration. Neurons in the abducens nucleus discharge homogeneously in relation mainly to eye position, and reflect almost perfect integration of the cerebellar outputs. Average responses of neural populations and the diverse individual responses of large samples of individual neurons are reproduced by a hierarchical neural circuit based on a model suggested the anatomy and physiology of the larval zebrafish brainstem. The model uses a combination of feedforward and feedback connections to support a neural circuit basis for integration in monkeys and other species.
Developmental Precursors of Social Brain Networks: The Emergence of Attentional and Cortical Sensitivity to Facial Expressions in 5 to 7 Months Old Infants

Biases in attention towards facial cues during infancy may have an important role in the development of social brain networks. The current study used a longitudinal design to examine the stability of infants' attentional biases towards facial expressions and to elucidate how these biases relate to emerging cortical sensitivity to facial expressions. Event-related potential (ERP) and attention disengagement data were acquired in response to the presentation of fearful, happy, neutral, and phase-scrambled face stimuli from the same infants at 5 and 7 months of age. The tendency to disengage from faces was highly consistent across both ages. However, the modulation of this behavior by fearful facial expressions was uncorrelated between 5 and 7 months. In the ERP data, fear-sensitive activity was observed over posterior scalp regions, starting at the latency of the N290 wave. The scalp distribution of this sensitivity to fear in ERPs was dissociable from the topography of face-sensitive modulation within the same latency range. While attentional bias scores were independent of co-registered ERPs, attention bias towards fearful faces at 5 months of age predicted the fear-sensitivity in ERPs at 7 months of age. The current results suggest that the attention bias towards fear could be involved in the developmental tuning of cortical networks for social signals of emotion.
Signed Chord Length Distribution. II

This paper continues description of applications of signed chord length distribution started in part I (arXiv:0711.4734). It is shown simple relation between equation for some transfer integrals with source and target bodies and different geometrical distributions for union of this bodies. The union of disjoint bodies is always nonconvex object and for such a case derivatives of correlation function (used for definition of signed radii and chord lengths distributions) always produce (quasi)densities with negative values. Many equations used in this part are direct consequences of analogue formulas in part I.
Integrating an Enterprise Architecture Using Domain Clustering

Enterprise Architecture (EA) in the context of enterprise engineering addresses aspects of developing, improving and integrating organizations. The paper introduces an approach to EA proposing Integration Concepts (IC) to reconcile changing business process requirements and information systems. Being process-driven and supporting integration issues the chosen IC is a Service Oriented Architecture (SOA). Therefore the contribution aims at developing a methodology to support service engineering by defining architectural domains in an EA. The paper shows the need for methods in the field of domain engineering supporting the design of a SOA. The main contribution of the paper is an algorithm based modelling approach and a methodology to support service domain clustering. The clustering algorithms are using a model considering business processes, information systems and information system interfaces. The algorithm adopts network-centric approaches used in the field of social network analysis to define and/or identify service domain clusters in complex scenarios. The paper summarizes a case study in a globally operating company and closes with a conclusion. The paper is organized by chapters addressing context, objective, approach, case, results and lessons learned.
Dynamic Contracting Under Imperfect Public Information and Asymmetric Beliefs

We develop a dynamic principal–agent model to show how imperfect public information and asymmetric beliefs about payoff-relevant parameters, agency conflicts, and the agent's implicit incentives to influence the principal's posterior beliefs through his unobservable actions interact to affect optimal dynamic contracts. We make a methodological contribution to the literature by solving the continuous-time contracting problem using a discrete-time approximation approach. We obtain a simple characterization of optimal renegotiation-proof contracts in terms of the solution to a nonlinear ordinary differential equation (ODE). We then exploit the properties of the ODE to derive a number of novel implications for the dynamics of long-term contracts that alter the intuition gleaned from the previous literature. Optimism has a first-order impact on incentives, investment and output that could reconcile the “private equity” puzzle. Consistent with empirical evidence, the interaction between asymmetric beliefs, risk-sharing and adverse selection costs could cause the time-paths of the agent's incentive intensities to be increasing or decreasing. Our results also suggest that the incorporation of imperfect public information and asymmetric beliefs could potentially reconcile empirical evidence of an ambiguous relation between risk and incentives, and a non-monotonic relation between firm value and incentives. Permanent and transitory components of risk have differing effects on incentives, which suggest that empirical investigations of the link between risk and incentives should appropriately account for different components of risk.
Measuring Productivity Growth in Asia: Do Market Imperfections Matter?

Recent research reports contradictory estimates of productivity growth for the newly industrialized economies (NIEs) of Asia. In particular, estimates using real factor prices find relatively rapid TFP growth; estimates using quantities of inputs and output find relatively low TFP growth. The difference is particularly notable for Singapore, where the difference is about 2-1/4 percentage-points per year. We show that about 2/3 of that difference reflects differences in estimated capital payments. We argue that these differences reflect economically interesting imperfections in output and capital markets, including sizeable economic profits in Singapore and government-directed credit. We derive a measure of technology growth, corrected for the imperfections that we quantify.
Monsoon drought over Java, Indonesia, during the past two centuries

Monsoon droughts, which often coincide with El Niño warm events, can have profound impacts on the populations of Southeast Asia. Improved understanding and prediction of such events can be aided by high‐resolution proxy climate records, but these are scarce for the tropics. Here we reconstruct the boreal autumn (October–November) Palmer Drought Severity Index (PDSI) for Java, Indonesia (1787–1988). This reconstruction is based on nine ring‐width chronologies derived from living teak trees growing on the islands of Java and Sulawesi, and one coral δ18O series from Lombok. The PDSI reconstruction correlates significantly with El Niño‐Southern Oscillation (ENSO)‐related sea surface temperatures and other historical and instrumental records of tropical climate, reflecting the strong coupling between the climate of Indonesia and the large scale tropical Indo‐Pacific climate system.
Personalizing User Interfaces for Environmental Decision Support Systems

The quality of the natural environment has become one of the primary concerns in present society. In Canada, we have been asked to take on the "One Tonne Challenge" to reduce personal household emissions by 1 tonne. However, very little has been done to illuminate the various connections between our household purchases and the effect they can have on the quality of our health and environment. Several decision support systems are available to assist consumers compare alternatives. However, these systems do little to enhance the consumer's experience. Correct clustering of consumers in terms of their product attribute preferences would enable the construction of personalized user interfaces thus increase consumer satisfaction when interacting with the system and increase the chance of inspiring greener purchasing habits. This paper analyzes a clus- tering technique that uses methods from multivariate statistics, rough set theory, and machine learning to cluster users in a web- based environmental decision support system and test the success of the clustering. Results from our analysis are discussed. I. INTRODUCTION The Kyoto accord for reduction of greenhouse gas (GHG) emissions represents a step toward a cleaner environment. Although Canadians have been asked to take on the "One Tonne Challenge" to reduce personal household emissions by 1 tonne, very little has been done to illuminate the various connections between our household purchases and the effect they can have on the quality of our health and environment. The environmental soundness of many of these purchases goes unquestioned. If we consider a life cycle assessment approach, it becomes clear that many of these purchases have potential to create a negative impact, however indirectly. Several environmental decision support systems (EDSS) are available to assist consumers compare alternatives. However, these systems seldom attempt to enhance the consumer's expe- riences when interacting with the system. Correct clustering of consumers in terms of their product attribute preferences would allow the construction of personalized user interfaces and increase consumer satisfaction by emphasizing those items with attributes that meet their specific values. By allowing consumers to also compare their current choices with possible alternatives, some of which may be more eco-effective 1 , the chance of inspiring greener purchasing habits is great.
Latino, Asian, and black segregation in U.S. metropolitan areas: Are multiethnic metros different*

This study examines 1990 residential segregation levels and 1980–1990 changes in segregation for Latinos, Asians, and blacks in U.S. metropolitan areas. It also evaluates the effect of emerging multiethnic metropolitan area contexts for these segregation patterns. While black segregation levels are still well above those for Latinos and Asians, there is some trend toward convergence over the decade. More than half of the areas increased their Latino segregation levels over the 1980s, and almost three-fourths increased their Asian segregation levels. In contrast, black segregation levels decreased in 88%ofmetropolitan areas. Multiethnic metropolitan area context is shown to be important for internal segregation dynamics. Black segregation levels are lower, and were more likely to decline in multiethnic metropolitan areas and when other minority groups grew faster than blacks. Latino segregation was also more likely to decline in such areas, and declines in both Latino and Asian segregation were greater when other minority groups were growing. These findings point up the potential for greater mixed-race and mixed-ethnicity coresidence in the neighborhoods of multiethnic metropolitan areas.
High-Performance Cluster-Fault Tolerance Scheme for Hybrid Nanoelectronic Memories

Error correction codes (ECCs) are common industrial practices for tolerating intermittent and transient faults in semiconductor memories. They have been also proposed for emerging hybrid nanoelectronic memories. However, this solution comes at higher cost in terms of performance penalty and area overhead. This paper proposes a high performance cluster-fault correction scheme for hybrid nanoelectronic memories. The scheme, referred to as Double Three-Residue Code (D3R), is based on combining the advantages of N-tuple Modular Redundancy (NMR) and Redundant Residue Number System (RRNS). Experimental results show that D3R decodes 10 to 28 times faster as compared to RRNS variants and Reed-Solomon (RS) while achieving similar cluster-fault correction capability.
Combined SAGE II–GOMOS ozone profile data set for 1984–2011 and trend analysis of the vertical distribution of ozone

Abstract. We have studied data from two satellite occultation instruments in order to generate a high vertical resolution homogeneous ozone time series of 26 yr. The Stratospheric Aerosol and Gas Experiment (SAGE) II solar occultation instrument and the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument measured ozone profiles in the stratosphere and mesosphere from 1984–2005 and 2002–2012, respectively. Global coverage, good vertical resolution, and the self-calibrating measurement method make data from these instruments valuable for the detection of changes in vertical distribution of ozone over time. As both instruments share a common measurement period from 2002–2005, it is possible to inter-calibrate the data sets. We investigate how well these measurements agree with each other and combine all the data to produce a new stratospheric ozone profile data set. Above 55 km, SAGE II measurements show much less ozone than the GOMOS nighttime measurements as a consequence of the well-known diurnal variation of ozone in the mesosphere. Between 35–55 km, SAGE II sunrise and sunset measurements differ from GOMOS' measurements to different extents. Sunrise measurements show 2% less ozone than GOMOS, whereas sunset measurements show 4% more ozone than GOMOS. Differences can be explained qualitatively by the diurnal variation of ozone in the stratosphere recently observed by SMILES and modeled by chemical transport models. Between 25–35 km, SAGE II sunrise and sunset measurements and GOMOS measurements agree within 1%. The observed ozone bias between collocated measurements of SAGE II sunrise/sunset and GOMOS night measurements is used to align the two data sets. The combined data set covers the time period 1984–2011, latitudes 60° S–60° N, and the altitude range of 20–60 km. Profile data are given on a 1 km vertical grid, and with a resolution of 1 month in time and 10° in latitude. The combined ozone data set is analyzed by fitting a time series model to the data. We assume a linear trend with an inflection point (so-called "hockey stick" form). The best estimate for the point of inflection was found to be the year 1997 for ozone between altitudes 35 and 45 km. At all latitudes and altitudes from 35 to 50 km we find a clear change in ozone trend before and after the inflection time. From 38 to 45 km, a negative trend of 4% per decade (statistically significant at 95% level) at the equator has changed to a small positive trend of 0–2% per decade. At mid-latitudes, the negative trend of 4–8% per decade has changed to to a small positive trend of 0–2% per decade. At mid-latitudes near 20 km, the ozone loss has still increased whereas in the tropics a recovery is ongoing.
The decapping activator Edc3 and the Q/N-rich domain of Lsm4 function together to enhance mRNA stability and alter mRNA decay pathway dependence in Saccharomyces cerevisiae

ABSTRACT The rate and regulation of mRNA decay are major elements in the proper control of gene expression. Edc3 and Lsm4 are two decapping activator proteins that have previously been shown to function in the assembly of RNA granules termed P bodies. Here, we show that deletion of edc3, when combined with a removal of the glutamine/asparagine rich region of Lsm4 (edc3Δ lsm4ΔC) reduces mRNA stability and alters pathways of mRNA degradation. Multiple tested mRNAs exhibited reduced stability in the edc3Δ lsm4ΔC mutant. The destabilization was linked to an increased dependence on Ccr4-mediated deadenylation and mRNA decapping. Unlike characterized mutations in decapping factors that either are neutral or are able to stabilize mRNA, the combined edc3Δ lsm4ΔC mutant reduced mRNA stability. We characterized the growth and activity of the major mRNA decay systems and translation in double mutant and wild-type yeast. In the edc3Δ lsm4ΔC mutant, we observed alterations in the levels of specific mRNA decay factors as well as nuclear accumulation of the catalytic subunit of the decapping enzyme Dcp2. Hence, we suggest that the effects on mRNA stability in the edc3Δ lsm4ΔC mutant may originate from mRNA decay protein abundance or changes in mRNPs, or alternatively may imply a role for P bodies in mRNA stabilization. Summary: A strain mutated in two decapping activators, previously implicated in P body assembly, has reduced mRNA stability and increased dependence on decapping and Ccr4-dependent deadenylation for mRNA degradation.
Econometric Methods with Applications in Business and Economics

Nowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations). DT Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical questions in modern business and economic management. DT Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics. DT Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions. DT Derivations and theory exercises are clearly marked for students in advanced courses. This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied econometrics.
Bionic Control of Cheetah Bounding with a Segmented Spine

A cheetah model is built to mimic real cheetah and its mechanical and dimensional parameters are derived from the real cheetah. In particular, two joints in spine and four joints in a leg are used to realize the motion of segmented spine and segmented legs which are the key properties of the cheetah bounding. For actuating and stabilizing the bounding gait of cheetah, we present a bioinspired controller based on the state-machine. The controller mainly mimics the function of the cerebellum to plan the locomotion and keep the body balance. The haptic sensor and proprioception system are used to detect the trigger of the phase transition. Besides, the vestibular modulation could perceive the pitching angle of the trunk. At last, the cerebellum acts as the CPU to operate the information from the biological sensors. In addition, the calculated results are transmitted to the low-level controller to actuate and stabilize the cheetah bounding. Moreover, the delay feedback control method is employed to plan the motion of the leg joints to stabilize the pitching motion of trunk with the stability criterion. Finally, the cyclic cheetah bounding with biological properties is realized. Meanwhile, the stability and dynamic properties of the cheetah bounding gait are analyzed elaborately.
A 4-Bits Trimmed CMOS Bandgap Reference with an Improved Matching Modeling Design

Component tolerances and mismatches due to process variations severely degrade the performance of bandgap reference (BGR) circuits. In this paper, the authors describe the design of a BGR considering the Pelgrom's mismatch model. The main purpose of our methodology is to convey the design to reach a good trade-off between area and mismatch. Implemented in standard 0.35mum CMOS technology, the circuit also includes a straightforward 4-bits trimming circuit to achieve more process variations independence. Its Monte Carlo temperature coefficient average is 40-ppm/degC and the reference output voltage average is 1.230V. The area of the BGR is 400x350mum2 due to our design matching requirements.
Variable-component deep neural network for robust speech recognition

In this paper, we propose variable-component DNN (VCDNN) to improve the robustness of context-dependent deep neural network hidden Markov model (CD-DNN-HMM). This method is inspired by the idea from variable-parameter HMM (VPHMM) in which the variation of model parameters are modeled as a set of polynomial functions of environmental signal-to-noise ratio (SNR), and during the testing, the model parameters are recomputed according to the estimated testing SNR. In VCDNN, we refine two types of DNN components: (1) weighting matrix and bias (2) the output of each layer. Experimental results on Aurora4 task show VCDNN achieved 6.53% and 5.92% relative word error rate reduction (WERR) over the standard DNN for the two methods, respectively. Under unseen SNR conditions, VCDNN gave even better result (8.46% relative WERR for the DNN varying matrix and bias, 7.08% relative WERR for the DNN varying layer output). Moreover, VCDNN with 1024 units per hidden layer beats the standard DNN with 2048 units per hidden layer with 3.22% WERR and a half computational/memory cost reduction, showing superior ability to produce sharper and more compact models.
Genetic algorithm based mobility aware clustering for energy efficient routing in wireless sensor networks

Clustering in wireless sensor networks improves lifetime of the network by load distribution, data aggregation and use of clusterheads to efficiently route messages to the central gateway. Parameters governing creation of optimal clusters are often numerous and domain-specific. The challenges increase with mobile nodes due to faster temporal change in one or more parameter values like speed and distance to the gateway. We propose a Genetic Algorithm based algorithm GAROUTE that performs energy-efficient mobility-aware clustering for wireless sensor networks. GAROUTE uses mobility awareness of each node to adaptively create stable clusters for energy-efficient routing of information.