Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproduction and modulation of the stress response: an experimental test in the house sparrow

The stress response is highly variable among individuals, but the causes of this variation remain largely unknown. In response to stressors, vertebrates secrete elevated levels of glucocorticoids which enhance survival, but concurrently interfere with reproduction. We tested the hypothesis that individuals flexibly modulate their stress response with respect to the reproductive value of their brood in free-living house sparrows (Passer domesticus). We experimentally increased or decreased clutch size during the nestling period and found that parents tending enlarged clutches responded less strongly to a stressor than those tending reduced clutches. In addition, we examined whether individuals responded less strongly to a stressor as the breeding season progressed and future reproductive opportunities declined. We found that the stress response decreased with breeding date during the birds' first breeding attempt, but it remained constant during their second breeding attempt. Within-individual variability in the stress response was related to the brood size manipulations the birds received in their two consecutive breeding attempts. These results provide the first experimental support for the hypothesis that individuals actively modulate their stress response with respect to the value of current reproduction.
Giant graviton correlators from dual SU(N) super Yang-Mills theory

Certain correlation functions are computed exactly in the zero coupling limit of = 4 super Yang-Mills theory with gauge group SU(N). A set of linearly independent operators that are in one-to-one correspondence with the half-BPS representations of the SU(N) gauge theory is given. These results are used to study giant gravitons in the dual AdS5 × S5 string theory. In addition, for the U(N) gauge theory, we explain how to systematically identify contributions coming from the boundary degrees of freedom.
Predictors of Future Cognitive Decline in Persons with Mild Cognitive Impairment

This study sought first to identify individual items of the Mini-Mental State Examination (MMSE) and demographic variables at baseline that predicted the trajectories of cognitive change among patients with mild cognitive impairment (MCI), and second to quantify the risk of cognitive decline in such patients based on their pattern of failure of MMSE items. 187 MCI patients were evaluated serially with the MMSE for up to 3.5 years. Patients who followed a declining cognitive trajectory differed from the stable reference group in their baseline profile of MMSE test performance. Patient age and performance on delayed recall, constructional praxis, attention, and orientation to time and floor predicted future cognitive decline with good accuracy (79.9%) and specificity (86.4%), and moderate sensitivity (67.2%). These results are presented in the form of a simple clinical tool for quantifying risk of future cognitive decline in MCI.
Consensus control of network connected mobile robots with input delay

This study investigates the consensus control problem of linear multi-agent systems with input time delay. By exploring the certain features of Laplacian matrix, we transform the consensus problem into an asymptotic stability problem. The key element in our work is introducing the semi-discretization method to identify the optimal consensus control conditions and develops a mapping of the system response in a finite dimensional state space. Consensus region and consensus boundary can be obtained by comparing the maximum absolute value of the mapping's eigenvalues with 1. By minimizing the maximum absolute value of the eigenvalues, the optimal convergence speed can be obtained. Simulations and experiments with Amigobots experimental environment validate the proposed method with convergence and formation control test. The results show the performance with our optimal method.
Machine Comprehension of Spoken Content: TOEFL Listening Test and Spoken SQuAD

A user can scan through a text easily, but it is not the case for spoken content, because they cannot be directly displayed on-screen. As a result, accessing large collections of spoken content is much more difficult and time-consuming than doing so for the text content. It would therefore be helpful to develop machines that understand spoken content. In this paper, we propose two new tasks for machine comprehension of spoken content. The first is a listening comprehension test for TOEFL, a challenging academic English examination for English learners who are not the native English speakers. We show that the proposed model outperforms the naive approaches and other neural network based models by exploiting the hierarchical structures of natural languages and the selective power of attention mechanism. For the second listening comprehension task – spoken SQuAD – we find that speech recognition errors severely impair machine comprehension; we propose the use of subword units to mitigate the impact of these errors.
Novel techniques in cooperative wireless networks using network coding

In this paper two novel methods for using network coding in the cooperative networks have been proposed. In the first scheme, network coding generates some redundancy in addition to the redundancy that is generated by the channel code. These redundancies are used in an iterative decoding system at the destination. In the second scheme, the output of the channel encoder in each source node is shortened and transmitted. The relay, by use of the network code, sends a compressed version of the parts missing from the original transmission. This facilitates the decoding procedure at the destination. Simulation based optimizations have been developed and results indicate that in case of the sources with non-identical power level, both scenarios outperform the non-relay case with 3 to 5dB gain.
Fermiophobic and other non-minimal neutral Higgs bosons at the LHC

The phenomenology of neutral Higgs bosons from non-SUSY, extended Higgs sectors is studied in the context of the LHC, with particular attention given to the case of a fermiophobic Higgs. It is found that enhanced branching ratios to and are possible and can provide clear signatures, while detection of a fermiophobic Higgs will be problematic beyond a mass of 130 GeV.
Locating facial feature points using support vector machines

Recent emergent face-related applications, such as face recognition and face animation, normally demand robust and accurate extraction of facial feature points as a preliminary step. In this paper, we propose an accurate facial feature point extraction algorithm that combines the good generalization ability of support vector machine (SVM) as well as the binarization techniques with color criterion. In the proposed algorithm, we first utilize the SVM detector to find the regions that contain the desired features such as eyes and mouths. Then, by properly binarizing these feature patterns and using the color information in the detected local regions, the facial feature points can be successfully located. Images from CMUPIE face database together with four kernel types of SVM and three different types of image representation are tested in the experiments for comparison. Experimental results demonstrate the performance of the proposed algorithm.
Launching the new era

middle-out strategy of ICOT, the choice of the kernel programming language was crucial to all other developments, downward to-the hardware architecture and upward to the operating system, programming environment, and applications. GHC, defined in 1984, has proved to be a sound basis for KL1, the kernel language used for the development of PIMOS, the PIM operating system, and the PIM applications. Ueda describes the intellectual history of GHC and its role in the Fifth Generation project. Ken Kahn and his group at Xerox PARC have pioneered several research directions in concurrent logic programming. First, they offered a method for objectoriented programming within a high-level language on top of a concurrent logic language. Second, they have investigated the use of concurrent logic programming for programming distributed open systems. Third, Vijay Saraswat, a member of the group, has integrated the approaches of concurrent logic programming and constraint logic programming in the cc framework for concurrent constraint programming. Fourth, Kahn and Saraswat have invented a method for the complete visualization of concurrent logic programs and their computations, which might signal a breakthrough in visual programming. Kahn describes the research carried out by his group and their interaction with colleagues at ICOT and at the Weizmann Institute. Takashi Chikayama is responsible, more than any other person, for ICOT's success in realizing innovative computer systems that were actually working and usable. Chikayama was chief architect and chief programmer of all the software systems developed at ICOT, including ESP (ICOT's object-oriented version of Prolog), SIMPOS (the operating system and programming environment of ICOT's Prolog workstation, their main workhorse during most of the project), KL1 (the kernel programming language for PIM), and PIMOS (the PIM operating system). Chikayama shares with us his experience in designing, implementing, and using these software systems. Evan Tick's Ph.D. dissertation was on the design and performance evaluation of Prolog architectures. Tick was the first U.S. postdoctoral participant at ICOT, visiting under the ICOT-NSF exchange agreement. Tick investigated shared-memory implementations of both Prolog and FGHC while at ICOT. Following his postdoctoral work at ICOT, Tick spent another year in Japan as a faculty member of Tokyo University, still maintaining close contacts with ICOT. Among the "outsiders," Tick i~ perhaps the most intimately familiar with the inner workings of the Fifth Generation project, which he describes in his contribution to this special section. We conclude this special section with a short epilogue which briefly assesses the material presented in the articles and offers some general assessments of the Fifth Generation Computer Systems project. •
Shape optimization for low Neumann and Steklov eigenvalues

We give an overview of results on shape optimization for low eigenvalues of the Laplacian on bounded planar domains with Neumann and Steklov boundary conditions. These results share a common feature: they are proved using methods of complex analysis. In particular, we present modernized proofs of the classical inequalities due to Szegö and Weinstock for the first nonzero Neumann and Steklov eigenvalues. We also extend the inequality for the second nonzero Neumann eigenvalue, obtained recently by Nadirashvili and the authors, to nonhomogeneous membranes with log‐subharmonic densities. In the homogeneous case, we show that this inequality is strict, which implies that the maximum of the second nonzero Neumann eigenvalue is not attained in the class of simply connected membranes of a given mass. The same is true for the second nonzero Steklov eigenvalue, as follows from our results on the Hersch–Payne–Schiffer inequalities. Copyright © 2009 John Wiley & Sons, Ltd.
Towards the On-line Identification of Peer-to-peer Flow Patterns

The number and variety of IP applications have hugely increased in the last few years. Among them, peer-to-peer (P2P) file-sharing applications have become very popular: more users are continuously joining such systems and more objects are being made available, seducing even more users to join. An accurate mapping of traffic to applications is important for a wide range of network management tasks. Besides, traditional mapping approaches have become increasingly inaccurate because many applications use non-default or ephemeral port numbers, use well-known port numbers associated with other applications, change application signatures or use traffic encryption. This paper proposes a framework to identify Internet applications that can be mainly used in situations where existing identification frameworks are not efficient or can not be used at all. The core block of the identification tool is based on neural networks and is able to identify different flow patterns generated by various Internet applications. Neural network based identification relies on a previous identification of the different IP applications that can be obtained offline using any reliable method. In this way, the paper also presents a module to process IP traffic flows and identify the underlying applications using payload analysis techniques. The identification results obtained from this tool are used in the training phase of the neural network identification framework. The accuracy of the identification framework was evaluated by performing a set of intensive tests and the results obtained show that, when conveniently trained, neural networks constitute a valuable tool to identify Internet applications while being, at the same time, immune to the most important disadvantages presented by other identification methods.
Might as Well Jump: Sound Affects Muscle Activation in Skateboarding

The aim of the study is to reveal the role of sound in action anticipation and performance, and to test whether the level of precision in action planning and execution is related to the level of sensorimotor skills and experience that listeners possess about a specific action. Individuals ranging from 18 to 75 years of age - some of them without any skills in skateboarding and others experts in this sport - were compared in their ability to anticipate and simulate a skateboarding jump by listening to the sound it produces. Only skaters were able to modulate the forces underfoot and to apply muscle synergies that closely resembled the ones that a skater would use if actually jumping on a skateboard. More importantly we showed that only skaters were able to plan the action by activating anticipatory postural adjustments about 200 ms after the jump event. We conclude that expert patterns are guided by auditory events that trigger proper anticipations of the corresponding patterns of movements.
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5

Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the majority of these features are relevant for classification, and that the performance of text categorization with support vector machines peaks when no feature selection is performed. We describe a class of text categorization problems that are characterized with many redundant features. Even though most of these features are relevant, the underlying concepts can be concisely captured using only a few features, while keeping all of them has substantially detrimental effect on categorization accuracy. We develop a novel measure that captures feature redundancy, and use it to analyze a large collection of datasets. We show that for problems plagued with numerous redundant features the performance of C4.5 is significantly superior to that of SVM, while aggressive feature selection allows SVM to beat C4.5 by a narrow margin.
Bonding strength evaluation of Al foil/AlN junctions by surface activated bonding

An Al foil/AlN junction is fabricated by bonding a 30-μm Al foil and a 650-μm AlN plate at room temperature and 473 K. Their bonding strength, which is measured by the 180° peel test, is estimated to be ~30 and ~60 N/m for junctions fabricated by the room-temperature and 473-K bonding, respectively.
Continuous evolutionary one-step-ahead testing

The traditional software development life cycle considers testing to be an activity that occurs between the implementation phase of development and software release [4]. With this approach any testing subsequent to release is done in reaction to failures reported by software users. The realities of software in operation however causes questions about this approach to arise. Adams [1] showed that organizations developing significant software applications often provide several fixes after their software has been released as the result of errors found in the field. This work also showed that the most serious and frequently recurring errors are usually found by users soon after a product has been released. These are referred to by Adams [1] as virulent errors. The negative effects of remaining defects implies that post-release activities should be proactive. These post-release activities must include continued testing by the vendor to find errors even after release. This paper proposes a solution to this requirement.
7. Multilevel Latent Class Models

The latent class (LC) models that have been developed so far assume that observations are independent. Parametric and non-parametric random-coefficient LC models are proposed here, which will make it possible to modify this assumption. For example, the models can be used for the analysis of data collected with complex sampling designs, data with a multilevel structure, and multiple-group data for more than a few groups. An adapted EM algorithm is presented that makes maximum-likelihood estimation feasible. The new model is illustrated with examples from organizational, educational, and cross-national comparative research.
Design of Longitudinal Auto-landing Guidance and Control System Using Linear Controller-based Adaptive Neural Network

this paper, we will design an auto-landing guidance and control system in adaptive nonlinear control technique proposed in this study, which is based on linear controller combined with neural network. In this approach, first of all, an auto-landing linear tracking controller satisfying the handling quality is designed in classical design technique. In the next the adaptive neural network with single hidden layer is introduced to compensate for model error between the reference tracking model and the outputs of the feedback control system coming out of the nonlinear aircraft model. To satisfy the overall closed-loop stability, Lyapunov stability concept is applied to the closed-loop system based on the linear feedback controller and the adaptive neural network. From this result, a set of on-line weight update rules for the neural network, whose activation function is sigmoidal function, will be obtained. This proposed design technique for longitudinal auto-landing maneuver is evaluated in the nonlinear aircraft simulation model: initial flight conditions are 1500 ft altitude and 250 ft/se velocity in level flight and the associated aircraft is about to the approach landing. In section 2, overall picture of linear controller-based neural network compensation technique is briefly explained, and the multiplayer neural network to be used in this paper is discussed in details in section 3. In section 4, this technique is applied to auto-landing guidance and control system design, and conclusion is followed in section 5. Abstract: We proposed a design technique for auto-landing guidance and control system. This technique utilizes linear controller and neural network. Main features of this technique is to use conventional linear controller and compensate for the error coming from the model uncertainties and/or reference model mismatch. In this study, the multi-perceptron neural network with single hidden layer is adopted to compensate for the errors. Glide-slope capture logic for auto-landing guidance and control system is designed in this technique. From the simulation results, it is observed that the responses of velocity and pitch angle to commands are fairly good, which are directly related to control inputs of throttle and elevator, respectively.
Optimum Placement of Additional Sink Nodes in a Wireless Sensor Network

—A new heuristic algorithm for solving the conditional p-center problem is described. In the new algorithm, the initial locations of the p additional sink nodes are selected based on the greedy algorithm. Then the clusters are grown iteratively around the sink nodes which act as the seeds. In successive iterations, the locations of the p additional sink nodes are updated to minimize the maximum weighted distance from a sensor node to its nearest sink node. When all the sensor nodes are included in their respective clusters and after the final update of the p sink node locations, the iteration process is over. I. INTRODUCTION The sensor nodes of a Wireless Sensor Network (WSN) forward their collected data to their respective sink nodes for further processing and transmission to the base station. Each sink node has a cluster of sensor nodes assigned to it. Each sensor node communicates with its nearest sink node. The power consumed by a sensor node depends on the transmission distance between it and its sink node. In a cluster, the sensor node which is farthest away from its sink node, is worst placed. To mitigate the worst case scenario, the sink nodes are so placed as to minimize the maximum weighted distance between the sink node and its farthest away sensor node in its cluster. This maximum weighted distance is the objective function. Here, the sink nodes are placed at selected sensor node locations among the existing ones to form the appropriate clusters. This is basically a p-center problem [1], [2], [3]. In certain situations, because of historical and administrative reasons, q (number of) sink nodes have already been placed. Then because of expansion and modifications we want to place p additional sink nodes without disturbing the locations of the original sink nodes. Thus the problem is to find the best locations for the additional p sink nodes and to determine the formation of new clusters for minimizing the objective function without disturbing the original q sink nodes. This is the conditional p-center problem [4], [5], [6]. Drezner's algorithm [4] solves this problem by solving the unconditional O(log n) p-center sub-problem. In [5], the unconditional p-center sub-problem has to be solved at most n times. In our proposed solution to this problem, we use a different approach. Here we use the cluster grow and sink relocate algorithm as will be described in section III.
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

Electroencephalography (EEG) headsets are the most commonly used sensing devices for Brain-Computer Interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.
Minimum sum-power design of a cooperative system using the decode and forward protocol

The aim of this paper is to design both transmitters and receiver of a cooperative network, such that the equivalent communication link achieves a given target rate using the minimal overall transmit sum power; the decode-and-forward protocol is considered. Successively, we compare, in terms of consumed energy, the cooperative communication and the direct communication. From this comparison, we determine the condition that makes cooperative communications more convenient than direct communications.