Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation and trajectory tracking of discrete-time multi-agent systems using block control

The design of a decentralized discrete-time block control scheme, for a multi-agent system with a fixed topology, to achieve formation and trajectory tracking is proposed in this work. Each agent dynamics is represented either by a first-order or second-order discrete-time system, only the position of each agent's neighbors is measured and their velocity and acceleration are unknown, and exclusively the agents that are connected to the virtual agent have full information of the reference. Each agent is provided with discrete-time state observers for the velocity and acceleration of its neighbors. It is proved that formation and trajectory tracking can be achieved applying the proposed block control with a consensus scheme. The stability of the controlled system with the state observer is proved.
A 3-min all-out cycling test is sensitive to a change in critical power.

PURPOSE The aim of this investigation was to test the hypothesis that a 3-min all-out cycling test would detect a change in critical power (CP) after a 4-wk interval training intervention. METHODS Nine habitually active subjects completed a ramp test, two 3-min all-out tests to establish the end power (EP) and the work done above EP (WEP), and three predicting trials to establish CP and W' using the work-time model (W = CPt + W'). After 12 supervised high-intensity interval training sessions over 4 wk, subjects repeated the testing procedures. RESULTS The CP increased in all subjects after training (pretraining: 230 +/- 53 W; posttraining: 255 +/- 50 W; t8 = 7.47, P < 0.001), with no statistically significant effect on the W' (pretraining: 17.2 +/- 4.2 kJ; posttraining: 15.5 +/- 3.8 kJ; t8 = 2.03, P = 0.08). The all-out test EP was increased after training from 225 +/- 52 W to 248 +/- 46 W (t8 = 6.26, P < 0.001). The EP and CP estimates before and after training were not different and were highly correlated (pretraining: r = 0.96, P < 0.001; posttraining: r = 0.95, P < 0.001). In addition, the increase in EP was correlated with (r = 0.77, P = 0.016) and not different from (t8 = 0.60, P = 0.57) the increase in CP. There was no change in the WEP from pretraining to posttraining (t8 = 1.89, P = 0.10). CONCLUSIONS The present study shows that the 3-min all-out test closely estimates CP across a wide range of aerobic fitness and is sensitive to training-induced changes in CP.
Performance evaluation of the blocking windows algorithm in blocking multicast log2(N, 0, p) switching networks

The Blocking Windows Algorithm (BWA) with the fixed-size windows was introduced by Lea in [1] and was used in nonblocking multicast banyan type switching networks. Later, Danilewicz and Kabaciński extended BWA to the variable window sizes variant in [2]. Some authors used this concept for different architectures of nonblocking multicast switching networks (Danilewicz, Hwang) [3], [4]. In this paper, performance evaluation of BWA in blocking multicast log2(N, 0, p) switching networks is presented. Different settings of BWA have an impact on a performance of the algorithm. In this paper, we investigate which parameters of BWA give better results in terms of blocking probability in blocking multicast log2(N, 0, p) switching networks.
Mechanical response of infant brain to manually inflicted shaking

Abstract Shaken baby syndrome (SBS) is a contentious issue on both biomechanical and medical fronts, primarily due to a lack of understanding of the loading—injury relationship of infant shaking and the parameters that are deterministic to its nature. In order to address this lack, a finite element (FE) representation of a three month infant head was developed to apply kinematics derived from physical testing with an anthropomorphic infant surrogate. The FE mesh was derived from a three-dimensional geometric basis, allowing for mesh size grading in regions of high importance, and future patient-specific adaptation. Cerebrospinal fluid (CSF) was represented through static pressure equilibration in combination with a locally based squeezing resistance. The results of the simulation indicate that anteroposterior shaking will lead to specific patterns of brain matter motion, increased likelihood of focal axonal injury at contact locations and deep brain structures, and a capacity for the development of subdural hematomas (SDH) due to rupture of central bridging veins.
The impact of anticipated stigma on psychological and physical health problems in the unemployed group

Previous research has demonstrated that the unemployed suffer increased psychological and physical health problems compared to their employed counterparts. Further, unemployment leads to an unwanted new social identity that is stigmatizing, and stigma is known to be a stressor causing psychological and physical health problems. However, it is not yet known whether being stigmatized as an unemployed group member is associated with psychological and physical health in this group. The current study tested the impact of anticipated stigma (AS) on psychological distress (PD) and physical health problems, operationalized as somatic symptoms (SSs), in a volunteer sample of unemployed people. Results revealed that AS had a direct effect on both PD and SSs, such that greater AS significantly predicted higher levels of both. Moreover, the direct effect on SSs became non-significant when PD was taken into account. Thus, to the extent that unemployed participants anticipated experiencing greater stigma, they also reported increased PD, and this PD predicted increased SSs. Our findings complement and extend the existing literature on the relationships between stigmatized identities, PD and physical health problems, particularly in relation to the unemployed group. This group is important to consider both theoretically, given the unwanted and transient nature of the identity compared to other stigmatized identities, but also practically, as the findings indicate a need to orient to the perceived valence of the unemployed identity and its effects on psychological and physical health.
Detecting frame duplication based on spatial and temporal analyses

A passive scheme is developed to achieve frame duplication detection for videos in the paper. The proposed scheme is a coarse-to-fine approach and composed of candidate clip selection, spatial correlation calculation, and frame duplication classification. To screen duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in RGB color space is adopted. To evaluate the similarity of image content, a block-based algorithm is used to measure spatial correlation of each corresponding frame between the query clip and the candidate one. Based on the spatial and temporal analyses in the temporal and spatial domains, we can localize the duplicated clips. The experimental results show that our scheme can not only detect but also localize duplicated clips well. In addition, the proposed scheme outperforms an existing method in terms of false alarm, missed detection, and accuracy.
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus

The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy.
Artificial MicroRNAs Highly Accessible to Targets Confer Efficient Virus Resistance in Plants

ABSTRACT Short-hairpin RNAs based on microRNA (miRNA) precursors to express the artificial miRNAs (amiRNAs) can specifically induce gene silencing and confer virus resistance in plants. The efficacy of RNA silencing depends not only on the nature of amiRNAs but also on the local structures of the target mRNAs. However, the lack of tools to accurately and reliably predict secondary structures within long RNAs makes it very hard to predict the secondary structures of a viral genome RNA in the natural infection conditions in vivo. In this study, we used an experimental approach to dissect how the endogenous silencing machinery acts on the 3′ untranslated region (UTR) of the Cucumber mosaic virus (CMV) genome. Transiently expressed 3′UTR RNAs were degraded by site-specific cleavage. By comparing the natural cleavage hotspots within the 3′UTR of the CMV-infected wild-type Arabidopsis to those of the triple dcl2/3/4 mutant, we acquired true small RNA programmed RNA-induced silencing complex (siRISC)-mediated cleavage sites to design valid amiRNAs. We showed that the tRNA-like structure within the 3′UTR impeded target site access and restricted amiRNA-RISC-mediated cleavage of the target viral RNA. Moreover, target recognition in the less-structured area also influenced siRISC catalysis, thereby conferring different degrees of resistance to CMV infection. Transgenic plants expressing the designed amiRNAs that target the putative RISC accessible target sites conferred high resistance to the CMV challenge from both CMV subgroup strains. Our work suggests that the experimental approach is credible for studying the course of RISC target recognition to engineer effective gene silencing and virus resistance in plants by amiRNAs.
Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems

We develop practical operations research models to support decision making in the design and management of public bicycle-sharing systems. We develop a network flow model with proportionality constraints to estimate the flow of bicycles within the network and the number of trips supported, given an initial allocation of bicycles at each station. We also examine the effectiveness of periodic redistribution of bicycles in the network to support greater flow, and the impact on the number of docks needed.We conduct our numerical analysis using transit data from train operators in Singapore. Given that a substantial proportion of passengers in the train system commute a short distance---more than 16% of passengers alight within two stops from the origin---this forms a latent segment of demand for a bicycle-sharing program. We argue that for a bicycle-sharing system to be most effective for this customer segment, the system must deploy the right number of bicycles at the right places, because this affects the utilization rate of the bicycles and how bicycles circulate within the system. We also identify the appropriate operational environments in which periodic redistribution of bicycles will be most effective for improving system performance.
Recent Results in Universal and Non-Universal Induction

We present and relate recent results in prediction based on countable classes of either probability (semi-)distributions or base pre- dictors. Learning by Bayes, MDL, and stochastic model selection will be considered as instances of the first category. In particular, we will show how analog assertions to Solomono's universal induction result can be obtained for MDL and stochastic model selection. The second category is based on prediction with expert advice. We will present a recent construction to define a universal learner in this framework.
Moving refractive optical low pass filter for digital cameras

As with any sampling process, image acquisition is subject to spatial domain aliasing. Many designs of optical filters have been proposed that reduce the amount of aliasing in image acquisition. Still, the most common systems are based on birefringent optical materials. Unfortunately, these filters are expensive and the actual filter response curve can only partially be influenced. We propose a new refractive optical low-pass filter (ROLPF) based on refraction at a structured glass plate. The transparent plate features a single surface with a defined structure. This leads to refraction and will lead to a modified light distribution on the sensor. The plate is placed in front of the sensor and is moved in the image sensor plane during an exposure. This causes an averaging process and results in a spatial domain filtering. The response curve of this setup can be controlled by designing the gradient of the surface. This filter will be easy to manufacture, it offers a true low-pass characteristic which can be specifically designed and the bandwidth can be adjusted at runtime by variation of the distance to the sensor.
Optimal Identification of Mismeasured Individuals.

Optimal appropriateness measurement statistically provides the most powerful methods for identifying individuals who are mismeasured by a standardized psychological test or scale. These methods use a likelihood ratio test to compare the hypothesis of normal responding versus the alternative hypothesis that an individual's responses are aberrant in some specified way. According to the Neyman-Pearson Lemma, no other statistic computed from an individual's item responses can achieve a higher rate of detection of the hypothesized measure- ment anomaly at the same false positive rate. Use of optimal methods requires a psychometric model for normal responding, which can be readily obtained from the item response theory literature, and a model for aberrant responding. In this article, several concerns about measurement anomalies are described and transformed into quantitative models. We then show how to compute the likeli- hood of a response pattern u* for each of the aberrance models.
Proteins Associated with the Myxococcus xanthus Extracellular Matrix

ABSTRACT Fruiting body formation of Myxococcus xanthus, like biofilm formation of many other organisms, involves the production of an extracellular matrix (ECM). While the polysaccharide component has been studied, the protein component has been largely unexplored. Proteins associated with the ECM were solubilized from purified ECM by boiling with sodium dodecyl sulfate and were identified by liquid chromatography-tandem mass spectrometry of tryptic fragments. The ECM is enriched in proteins of novel function; putative functions were assigned for only 5 of the 21 proteins. Thirteen putative ECM proteins had lipoprotein secretion signals. The genes for many ECM proteins were disrupted in the wild-type (WT), fibA, and pilA backgrounds. Disruption of the MXAN4860 gene had no effect in the WT or fibA background but in the pilA background resulted in a 24-h delay in aggregation and sporulation compared to its parent. The results of this study show that the M. xanthus ECM proteome is diverse and novel.
The Check, Connect, and Expect Program: A Targeted, Tier 2 Intervention in the Schoolwide Positive Behavior Support Model

ABSTRACT The Schoolwide Positive Behavior Support model emphasizes varying levels of supports for students and staff to enhance positive social behavior and decrease problematic behavior among students. The authors present an example of a targeted Tier 2 intervention that has been effective at producing positive social outcomes for students who are at risk of developing emotional or behavioral disabilities. In a Tier 2 intervention, a school-based coach works with teachers and students on a daily basis to set social goals, check students' progress, provide reinforcement when students meet goals, and communicate students' progress to their parents. Students have a daily progress report card to assess their social behavior and receive feedback and ratings from teachers. Additional supports are available in the form of social-skills instruction and problem-solving instruction when students do not meet daily social expectations. Results from the past several years show that this type of intervention can reduc.
Semantic and Pragmatic Aspects of Context Effects in Social and Psychological Research

The development of the field of cognitive psychology which stresses the importance of social processes for comprehension and production of linguistics utterances sharply contrasts with the direction of social cognition research. An exploration of the “autonomous” versus the “collaborative” view of language understanding is discussed. Two studies are presented which illustrate how subjects conceptualize information which is (1) ambiguous or (2) not semantically ambiguous. Findings show that principles of social cognition and communication can be applied to both natural conversation and data collection in social and psychological research.
Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels

Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach.
University of Pennsylvania ScholarlyCommons Center for Human Modeling and Simulation Department of Computer & Information Science September 1994 Sight and Sound : Generating Facial Expressions and Spoken Intonation from Context

This paper presents a model for automatically producing prosodically appropriate speech and corresponding facial expression for agents that respond to simple database queries in a 3D graphical representation of the world. This work addresses two major issues in human-machine interaction. First, proper intonation is necessary for conveying information structure, including important distinctions of contrast and focus. Second, facial expressions and lip movements often provide additional information about discourse structure, turn-taking protocols and speaker attitudes. Comments Copyright 1994 IEEE. Reprinted from Proceedings of the second ESCA/AAAI/IEEE Workshop on Speech Synthesis, 5 pages. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This conference paper is available at ScholarlyCommons: http://repository.upenn. edu/hms/40 ! "# $ %'&'(*) + , , ,. )/ 01
Excluding the included: A reconsideration of inclusive education

This article examines how the development of inclusive education policies has been constrained by the adhesion of traditional regular and special education imperatives. The fragmentation of educational policy-making presses us towards exclusion; and the protection of professional interests reinforces individual pathologies and creates further exclusionary pressures. The authors contend that inclusive education is not a linear progression from 'special educational needs' and we must endeavour to understand the very different nature of these knowledge bases. Deconstruction is presented as a way of exposing exclusion as it is inscribed within inclusive education policies. The article ends with a series of openings for dialogue about inclusion which address the relationship between ideas and politics; a new politics of research; envisioning forms of schooling which eschew the modernist blueprint; reflexivity; and the teaching of inclusion.
[The Boratav Depression Screening Scale (Bordepta): a sensitive scale for recognizing depression in epidemiologic studies and primary health care].

OBJECTIVE To develop a sensitive scale able to recognize depression in epidemiological surveys and primary health care. METHOD The research was designed as an initial preliminary investigation followed by two subgroup studies. The preliminary study involved the selection of queries that would constitute the scale, based on the rule of brief reply. The first sub-study was intended to test the reliability and validity of the developed scale in a group of patients previously diagnosed with depressive disorder (n=60) and/or generalized anxiety disorder (n=61). The second sub-study aimed to determine the sensitivity, specificity and appropriate cut-off points of the scale, based on results obtained from non-psychiatric patients (n=96) and normal subjects (n=7). RESULTS Item analysis showed a significant correlation varying from 0.501 to 0.729 between each item and total scale score. The Cronbach alpha value of the scale was 0.9005. Clinician-oriented vs. self report rating reliability correlation coefficient values were 0.534 to 0.850 (Kappa) and 0.856 (Pearson) for items and total scale score respectively. The concurrent validity of the scale as correlated with The Beck Depression Inventory and Trait Anxiety Scale were 0.812 and 0.763 respectively. The discriminant validity of items in terms of P values determined by chi-square testing was 0.021 to 0.0001. The sensitivity and specificity of the scale were 97.6% and 82% respectively at a cut-off point of 6.5, and 85.7% and 95.1% at 7.5. CONCLUSION Boratav depression screening scale is valid and reliable, including optimal sensitivity and specificity for recognizing depression in epidemiologic studies and primary health care.
A LLE-based approach to sensor fault detection

Feature extraction has been widely used in sensor fault detection. Commonly used feature extraction methods such as PCA and MDS involve signal process of liner time-invariant systems, which are less effective in dealing with the nonlinear systems. In this paper, we will present that Local Linear Embedding (LLE) concept is adopted to solve the fault detection problems and that certain enhancement have been made to make LLE approach more efficient and robust in the extraction of signal features. Test results are given to demonstrate the effectiveness of the enhanced LLE method.