Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and implementation of an efficient and large dynamic range hybrid digital AGC for burst communication systems

In digital receiver, automatic gain control (AGC) is usually adopted to solve the problem of large dynamic range of received signals. This paper provides a hybrid AGC algorithm which takes advantage of both feedback AGC and feed forward AGC for burst communication systems. A new efficient signal power estimation method is proposed, which can work in high sample frequency and consumes less resource. The proposed AGC has been hardware implemented and tested with TETRA system. Test results show that it can cope with large dynamic range input signals and has a short settling time. The AGC algorithm proposed by this paper can also be used for other wireless communication systems.
The Role of Dissonance and Schema: An Exploration of Florida Public Perception after the DWH Oil Spill

The agricultural and natural resources industries are no stranger to crises, particularly, large-scale crises such as the Deepwater Horizon (DWH) oil spill in 2010. Crises have an impact on how consumers view a product, and ultimately, can impact their decisions to buy or not buy products from an industry that has experienced a crisis. This qualitative study sought to evaluate consumer perceptions of Gulf seafood after the DWH oil spill, and explored the influence of schema and cognitive dissonance on consumers’ intent to consume or purchase Gulf seafood after the DWH oil spill. Focus group discussion with seafood consumers revealed participants had a pre-existing schema associated with local seafood, seafood origin, health benefits of seafood, and the safety of seafood. Dissonance was observed when participants balanced their pre-existing schemas with knowledge of the DWH oil spill as the participants discussed buying seafood, not buying seafood, and buying seafood with fear. From the findings of this study, researchers and communicators may have a better understanding of the decision-making process associated with buying a product after a crisis. Recommendations were made for agricultural communicators to develop personal messages and stories to help consumers overcome any remaining fear or dissonance associated with Gulf seafood.
TCS-TRA-1369 TCS Technical Report Efficient Top-Down ZDD Construction Techniques Using Recursive Specifications by

Many important problems of graph enumeration and indexing can be solved by frontier-based methods, which construct ZDDs in a breadth-first manner from the top to the bottom. We present new techniques toward an efficient framework to deal with the frontier-based methods. Ad hoc parts of the algorithm are encapsulated using the recursive specifications that represent properties to be compiled into a ZDD. In this framework, we can apply the ZDD node deletion rule on the fly, while conventional methods does not take it into account. Operations on the recursive specifications, which allow us to combine multiple properties without constructing ZDD structure for each property, are also introduced. These techniques are applicable to existing frontier-based methods and accelerates even Knuth’s sophisticated path enumeration algorithm doubly.
Discrimination of amygdala response predicts future separation anxiety in youth with early deprivation.

BACKGROUND Significant disruption in caregiving is associated with increased internalizing symptoms, most notably heightened separation anxiety symptoms during childhood. It is also associated with altered functional development of the amygdala, a neurobiological correlate of anxious behavior. However, much less is known about how functional alterations of amygdala predict individual differences in anxiety. Here, we probed amygdala function following institutional caregiving using very subtle social-affective stimuli (trustworthy and untrustworthy faces), which typically result in large differences in amygdala signal, and change in separation anxiety behaviors over a 2-year period. We hypothesized that the degree of differentiation of amygdala signal to trustworthy versus untrustworthy face stimuli would predict separation anxiety symptoms. METHODS Seventy-four youths mean (SD) age = 9.7 years (2.64) with and without previous institutional care, who were all living in families at the time of testing, participated in an fMRI task designed to examine differential amygdala response to trustworthy versus untrustworthy faces. Parents reported on their children's separation anxiety symptoms at the time of scan and again 2 years later. RESULTS Previous institutional care was associated with diminished amygdala signal differences and behavioral differences to the contrast of untrustworthy and trustworthy faces. Diminished differentiation of these stimuli types predicted more severe separation anxiety symptoms 2 years later. Older age at adoption was associated with diminished differentiation of amygdala responses. CONCLUSIONS A history of institutional care is associated with reduced differential amygdala responses to social-affective cues of trustworthiness that are typically exhibited by comparison samples. Individual differences in the degree of amygdala differential responding to these cues predict the severity of separation anxiety symptoms over a 2-year period. These findings provide a biological mechanism to explain the associations between early caregiving adversity and individual differences in internalizing symptomology during development, thereby contributing to individualized predictions of future clinical outcomes.
Validating rt-MRI Based Articulatory Representations via Articulatory Recognition

The large corpus of real time magnetic resonance image sequences of the vocal tract during speech production that was recently acquired and can be referred to as MRI-TIMIT, provides us with a unique platform for systematically studying articulatory dynamics. Compared to previously collected articulatory datasets, e.g., using articulography or X-rays, MRI-TIMIT is a rich source of information for the entire vocal tract and not only for certain articulatory landmarks and further has the potential to continue increasing in size covering a large variety of speakers and speaking styles. In this work, we investigate an articulatory representation based on full vocal tract shapes. We employ an articulatory recognition framework in MRI-TIMIT to analyze its merits and drawbacks. We argue that articulatory recognition can serve as a general validation tool for real-time MRI based articulatory representations. Index Terms: vocal tract shape, articulation, real-time MRI, articulatory recognition
Evaluation of Lexical Resources and Semantic Networks on a Corpus of Mental Associations

When a user cannot find a word, he may think of semantically related words that could be used into an automatic process to help him. This paper presents an evaluation of lexical resources and semantic networks for modelling mental associations. A corpus of associations has been constructed for its evaluation. It is composed of 20 low frequency target words each associated 5 times by 20 users. In the experiments we look for the target word in propositions made from the associated words thanks to 5 different resources. The results show that even if each resource has a useful specificity, the global recall is low. An experiment to extract common semantic features of several associations showed that we cannot expect to see the target word below a rank of 20 propositions.
Face authentication across age progression using shifted block matching

A method of personal authentication using face images is proposed for the situation that the person's face image was registered in his/her childhood and a large change exists between the captured face image and the registered one because of the person's growth. Here, the positions of the facial elements are different between the two images, but still some similarity exists for the same person. In order to avoid such influence from physical growth, authors proposed before a block matching method which can search a similar facial area in the captured face image, although its position is a little different. However, the performance sometimes gets degraded when the characteristic parts of the face are not appropriately contained in each template block. In this paper, a method of face recognition using shifted block matching is proposed in order to solve the problem of the previous method. The proposed method adjusts the position of the template blocks, so that the characteristic facial parts can be appropriately contained in each block. The experiment results show advantage of the proposed method to the conventional ones.
Extending the H-Tree Layout Pedigree: An Evaluation

Visualizing large family structures is becoming increasingly important, as more genealogical data becomes available. A space-filling h-tree layout pedigree has been recently proposed to make better use of the available space than traditional representations. In a previous paper we applauded the technique's usage of available space but remarked that it makes generation identification difficult and does not allow navigating to descendants of represented individuals. A set of extensions was proposed to help overcome these limitations and a preliminary evaluation suggested that those extensions enhance the original technique. This paper presents a more thorough evaluation carried out to assess if and how the proposed extensions improve the original h-tree layout pedigree technique. Results suggest that these extensions improve user performance on some tasks, effectively provide new functionality, and generally enhance user experience.
Counseling over Time as a Correlate of Non-Urgent Undergraduate Institutional Commitment

The general purpose of this study was to examine counseling services as a correlate of institutional commitment and related variables over time on a sample of non-urgent undergraduates. Data for non-urgent clients at a University Counseling Center (UCC) were collected using on-line surveys over four time-periods. Within-time correlations generally showed that mental health concerns was negatively related to institutional commitment, while counseling help belief was positively related. Institutional commitment is defined as a student feeling that he or she selected the right institution to attend Using a smaller sample, i.e., n = 15, of complete-data clients matched-over-time, overall level of mental health concerns significantly declined, while institutional commitment significantly increased. Counseling help belief decreased from Time 1 to Time 2 but then increased over time. Scientifically demonstrating to higher-level University administration that counseling over time can positively influence undergraduates’ institutional commitment can help the UCC to increase its allocation of university-based resources to keep pace with non-urgent client demands.
A Distributed Cache Management Model for P2P VoD System

Aiming at the problem of how to efficiently manage and use cache content in P2P VoD system, a distributed cache management model (DCMM) for P2P VoD is proposed. In the model, peers distributed cache management model for P2P VoD systems whose caching media segments integrate a full program are organized into a ring. Any segment of a specific program can be found quickly within a ring. With the concept of upper and lower neighbor, all peers that cache same segment are organized together and can be found. Analysis and results show that DCMM effectively manage and use cache content in P2P VoD system supporting VCR operations.
3D Indoor Activity Monitoring Navigation Viewer

A decentralized architecture of indoor location tracking system and its intuitive 3D activity monitoring viewer - 3D Navigation Viewer (3DNV) were developed. This paper concentrates on the integration of indoor location tracking system with virtual reality modeling language (VRML) to facilitate the indication of user's spatial information in virtual indoor environments, synchronized with physical world. The developed indoor location tracking system employs beacons as active transmitters and a listener as passive receiver which carried by users. Distance information between RF and Ultrasonic signals is exploited to determine user's physical location that essential in supporting third parties like doctors and caregivers to identify the activities and status of a particular individual through 3DNV. 3DNV serves as an unified user interface for indoor location tracking system, showing viewpoint and position of the target in the virtual indoor environments. It was implemented using VRML to provide a better visualization of target's spatial information.
Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective Variable Length PSO-Based Approach

Identifying relevant genes which are responsible for various types of cancer is an important problem. In this context, important genes refer to the marker genes which change their expression level in correlation with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. Gene expression profiling by microarray technology has been successfully applied to classification and diagnostic prediction of cancers. However, extracting these marker genes from a huge set of genes contained by the microarray data set is a major problem. Most of the existing methods for identifying marker genes find a set of genes which may be redundant in nature. Motivated by this, a multiobjective optimization method has been proposed which can find a small set of non-redundant disease related genes providing high sensitivity and specificity simultaneously. In this article, the optimization problem has been modeled as a multiobjective one which is based on the framework of variable length particle swarm optimization. Using some real-life data sets, the performance of the proposed algorithm has been compared with that of other state-of-the-art techniques.
Improving dynamic stability of power system using derivative power system stabilizer

The main function of power system stabilizer (PSS) is suppressing the low-frequency oscillation of the power system to improve the dynamic stability of power system. In this paper, the pole-placement approach and full phase-compensation method are used to design the parameters of a derivative power system stabilizer (DPSS) base on electrical torque components. The small-signal model of the power system is used to study the dynamic performances under different system conditions. Also, the effect of a transient gain reduction (TGR) on the damping performance is examined. The tracking control law is obtained with a pole placement technique. Finally, the advantage of the proposed method with the numerical simulation is show.
Decaying DC offset removal operator using mathematical morphology for phasor measurement

Phasor measurement is an essential component of protection relays and phasor measurement units. Phasor measurement as performed by the Fourier Transform is adversely affected by the decaying DC offset which occurs following a fault. Hence an efficient mathematical pre-processor is proposed to remove this offset in a half cycle plus an additional sample. Following this it is possible to use a new phasor measurement technique which can estimate the phasor using a quarter cycle window. The results show that the mathematical preprocessor can virtually remove the DC offset, and that the new phasor measurement technique out performs the Fourier Transform due to its significantly shorter delay.
Class-level risk factors for bullying and victimization in Portuguese middle schools

The aim of the present study was to examine whether class-level characteristics, such as class size and gender ratio, could contribute to explaining different levels of bullying, victimization, and perception of defending between classes. Seven-hundred-and-sixty-three students (M age = 12.96, SD = 1.22; 54.4% boys) from 42 Portuguese middle school classes participated in the current study. Bullying, victimization, and perception of defending were assessed through a self-report questionnaire. In order to account for the clustered nature of the data with students nested within classes, multilevel models were used. After controlling for age and gender at the individual level, gender ratio contributed to explaining between-class variance in bullying and victimization, while class size was a significant predictor of the perception of defending. Thus, the results suggested that both bullying and victimization are more frequent in classes with a high concentration of boys, while in larger classes there is a higher perception of defending. The results of the present study support the importance of taking into account class-level characteristics when developing anti-bullying interventions, and of establishing gender balanced classes in middle school. The results also highlight the relevance of using multilevel modelling when conducting research on bullying and victimization in schools.
Implementation of the typed call-by-value λ-calculus using a stack of regions

We present a translation scheme for the polymorphically typed call-by-value λ-calculus. All runtime values, including function closures, are put into regions. The store consists of a stack of regions. Region inference and effect inference are used to infer where regions can be allocated and de-allocated. Recursive functions are handled using a limited form of polymorphic recursion. The translation is proved correct with respect to a store semantics, which models as a region-based run-time system. Experimental results suggest that regions tend to be small, that region allocation is frequent and that overall memory demands are usually modest, even without garbage collection.
A New Learning Algorithm for Stochastic Feedforward Neural Nets

Multilayer perceptrons (MLPs) or artificial neural nets are popular models used for nonlinear regression and classification tasks. As regressors, MLPs model the conditional distribution of the predictor variables y given the input variables x. However, this predictive distribution is assumed to be unimodal (e.g. Normal distribution). For tasks such as structured prediction problems, the conditional distribution should be multi-modal, or one-to-many mappings. By turning the hidden variables in a MLP into stochastic nodes rather than deterministic ones, Sigmoid Belief Nets can induce a rich multimodal distribution in the output space. Learning Sigmoid Belief Nets is very slow and modeling real-valued data is difficult. In this paper, we propose a stochastic feedforward network where its hidden layers have both deterministic and stochastic variables. A new Generalized EM training procedure using importance sampling allows us to efficiently learn complicated conditional distributions. We demonstrate the superiority of our model to conditional Restricted Boltzmann Machines and Mixture Density Networks on 3 synthetic datasets and modeling facial expressions. Moreover, we show that latent features of our model improves classification and provide additional qualitative results on color images.
Adaptive Load Balanced Routing for 2-Dilated Flattened Butterfly Switching Network

High-radix networks such as folded-Clos outperform other low radix networks in terms of cost and latency. The 2-dilated flattened butterfly (2DFB) network is a nonblocking high-radix network with better path diversity and reduced diameter compared to the folded-Clos network. In this paper, we introduce an adaptive load balanced routing algorithm that is designed to exploit all the positive topological properties of a 2DFB network. The proposed algorithm achieves load balance by allowing one non minimal forwarding in each dimension in case of network congestion. This algorithm provides high throughput on adversarial traffic patterns and provides better latency on benign traffic patterns. We have compared the performance of our algorithm on a 2DFB network with an Adaptive Clos algorithm on a folded-Clos network and a Minimal routing algorithm on a 2DFB network for different traffic patterns. We observed that 2DFB network with the proposed algorithm provides the same throughput with reduced latency compared to the folded-Clos network with an Adaptive Clos algorithm for all the traffic patterns. Keywords-Routing; adaptive; switching architecture;
RotationNet: Learning Object Classification Using Unsupervised Viewpoint Estimation

The recent popularization of depth sensors and the availability of large-scale 3D model databases such as ShapeNet have drawn increased attention to 3D object recognition. Despite the convenience of using 3D models captured offline, we are allowed to observe only a single view of an object at once, with the exception of the use of special environments such as multi-camera studios. This impedes the recognition of diverse objects in a real environment. If a mechanical system (or a robot) has access to multi-view models of objects and is able to estimate the viewpoint of a currently observed object, it can rotate the object to a better view for classification. In this paper, we propose a novel method to learn a deep convolutional neural network that both classifies an object and estimates the rotation path to its best view under the predicted object category. We conduct experiments on a 3D model database as well as a real image dataset to demonstrate that our system can achieve an effective strategy of object rotation for category classification.
Design and application of identify algorithm for coupling process structure in Business Process Management

Considering complicated coupling process structure in the Business Process Management, this paper proposes a joint modeling strategy combining Petri net and Design Structure Matrix, and established the relevant process identify algorithm based on SDPSO to DSM modeling. Meanwhile Petri network graph model built by Anylogic software would be automatically transformed into DSM by Matlab software, and then achieve simulation results.
Hervé Roche Asset Pricing in a Lucas Economy with Recursive Utility Heterogeneous Agents

We extend the Lucas economy (1978) to the case of preferences a la Kreps-Porteus (1978) when the dividend (fruit) process follows a geometric Brownian Motion. In the representative agent case, the equilibrium price reveals a two-stage mechanism. First, the risk averse agent adjusts downward the average growth rate of dividends to incorporate uncertainty. Then, the e¤ect of the intertemporal elasticity of substitution (I.E.S.) depends on the sign of this adjusted growth rate of dividends. In agreement with Hall (1988), the paper illustrates the key role played by I.E.S. on the equilibrium price. The extension to the heterogeneous agents case allows us to analyze the wealth distribution and its e¤ects on the equilibrium price. Individuals who either highly value the future or who are both willing to substitute consumption over time and display low risk aversion will asymptotically accumulate all the wealth in the economy. Wealth concentration leads to an increase in the equilibrium price. JEL classi.cation: G12, C68, D81.
On the secrecy capacity of the space-division multiplexed fiber optical communication systems

Recent developments in the field of space-division multiplexing (SDM) for fiber-optic communication systems suggest that the spatial diversity offered by SDM can be used not only to increase system capacity, but also to achieve provable security against physical layer attacks. We examine the information-theoretic security of SDM with a focus on frequency-selective and rapidly-varying frequency-flat channels. We analytically evaluate the outage-free secrecy capacity - the maximal data rate that guarantees perfect security within a class of channel realizations. Using analysis and simulations, we evaluate the impact of key system parameters on the secrecy capacity of SDM systems. Our results show that, with a proper code design that balances the trade-off between data rate and confidentiality, we can take full advantage of SDM to offer a secure information rate that could be orders of magnitude higher than what can be achieved through quantum key distribution (QKD).