Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Event-Triggered Control for Multi-Agent Systems

Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The controller updates considered here are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation is considered first and then its distributed counterpart, in which agents require knowledge only of their neighbors' states for the controller implementation. The results are then extended to a self-triggered setup, where each agent computes its next update time at the previous one, without having to keep track of the state error that triggers the actuation between two consecutive update instants. The results are illustrated through simulation examples.
Selection of an Optimal Set of Discriminative and Robust Local Features with Application to Traffic Sign Recognition

Today, discriminative local features are widely used in different fields of computer vision. Due to their strengths, discriminative local features were recently applied to the problem of traffic sign recognition (TSR). First of all, we discuss how discriminative local features are applied to TSR and which problems arise in this specific domain. Since TSR has to cope with highly structured and symmetrical objects, which are often captured at low resolution, only a small number of features can be matched correctly. To alleviate these issues, we provide an approach for the selection of discriminative and robust features to increase the matching performance by speed, recall, and precision. Contrary to recent techniques that solely rely on density estimation in feature space to select highly discriminative features, we additionally address the question of features’ retrievability and positional stability under scale changes as well as their reliability to viewpoint variations. Finally, we combine the proposed methods to obtain a small set of robust features that have excellent matching properties.
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification

Histopathological whole slide image (WSI) analysis with deep learning has become a research focus in computational pathology. The current paradigm is mainly based on multiple instance learning (MIL), in which approaches with Transformer as the backbone are well discussed. These methods convert WSI tasks into sequence tasks by representing patches as tokens in the WSI sequence. However, the feature complexity brought by high heterogeneity and the ultra-long sequences brought by gigapixel size makes Transformer-based MIL suffer from the challenges of high memory consumption, slow inference speed, and lack of performance. To this end, we propose a retentive MIL method called RetMIL, which processes WSI sequences through hierarchical feature propagation structure. At the local level, the WSI sequence is divided into multiple subsequences. Tokens of each subsequence are updated through a parallel linear retention mechanism and aggregated utilizing an attention layer. At the global level, subsequences are fused into a global sequence, then updated through a serial retention mechanism, and finally the slide-level representation is obtained through a global attention pooling. We conduct experiments on two public CAMELYON and BRACS datasets and an public-internal LUNG dataset, confirming that RetMIL not only achieves state-of-the-art performance but also significantly reduces computational overhead. Our code will be accessed shortly.
Silence and Voice in the Study of Contentious Politics

Preface 1. Silence and voice in the study of contentious politics: an introduction Sidney Tarrow 2. Emotions and contentious politics Ron Aminzade and Doug McAdam 3. Space in contentious politics William H. Sewell, Jr. 4. Temporality in the study of social movements and revolutions Doug McAdam and William H. Sewell, Jr 5. Leadership dynamics and the dynamics of contention Ron Aminzade, Jack Goldstone and Elizabeth J. Perry 6. The scared, religious, and secular in contentious politics: blurring boundaries Jack Goldstone and Elizabeth J. Perry 7. Threat (and opportunity): popular action and state response in the dynamics of contentious action Jack Goldstone and Charles Tilly 8. Contention in demographic and life-course context Jack Goldstone and Doug McAdam 9. Harmonizing the voices: thematic continuity across the chapters Doug McAdam.
On the Average Runtime of an Open Source Binomial Random Variate Generation Algorithm

The BTPE algorithm (Binomial, Triangle, Parallelogram, Exponential) of Kachitvichyanukul and Schmeiser is one of the faster and more widely utilized algorithms for generating binomial random variates. Cicirello's open source Java library, $\rho\mu$, includes an implementation of BTPE as well as a variety of other random number related utilities. In this report, I explore the average case runtime of the BTPE algorithm when generating random values from binomial distribution $B(n,p)$. Beginning with Kachitvichyanukul and Schmeiser's formula for the expected number of acceptance-rejection sampling iterations, I analyze the limit behavior as $n$ approaches infinity, and show that the average runtime of BTPE converges to a constant. I instrument the open source Java implementation from the $\rho\mu$ library to experimentally validate the analysis.
Variance Reduced Stochastic Gradient Descent with Neighbors

Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its slow convergence can be a computational bottleneck. Variance reduction techniques such as SAG, SVRG and SAGA have been proposed to overcome this weakness, achieving linear convergence. However, these methods are either based on computations of full gradients at pivot points, or on keeping per data point corrections in memory. Therefore speed-ups relative to SGD may need a minimal number of epochs in order to materialize. This paper investigates algorithms that can exploit neighborhood structure in the training data to share and re-use information about past stochastic gradients across data points, which offers advantages in the transient optimization phase. As a side-product we provide a unified convergence analysis for a family of variance reduction algorithms, which we call memorization algorithms. We provide experimental results supporting our theory.
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning

In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i.e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories. The challenge of disentangling spurious correlations lies in the poor controllability of FSCIL. On one hand, an FSCIL model is required to be trained in an incremental manner and thus it is very hard to directly control relationships between categories of different sessions. On the other hand, training samples per novel category are only in the few-shot setting, which increases the difficulty of alleviating spurious relation issues as well. To overcome this challenge, in this paper, we propose a new simple-yet-effective method, called ConTrollable Relation-disentangLed Few-Shot Class-Incremental Learning (CTRL-FSCIL). Specifically, during the base session, we propose to anchor base category embeddings in feature space and construct disentanglement proxies to bridge gaps between the learning for category representations in different sessions, thereby making category relation controllable. During incremental learning, the parameters of the backbone network are frozen in order to relieve the negative impact of data scarcity. Moreover, a disentanglement loss is designed to effectively guide a relation disentanglement controller to disentangle spurious correlations between the embeddings encoded by the backbone. In this way, the spurious correlation issue in FSCIL can be suppressed. Extensive experiments on CIFAR-100, mini-ImageNet, and CUB-200 datasets demonstrate the effectiveness of our CTRL-FSCIL method.
Real-to-Sim Adaptation via High-Fidelity Simulation to Control a Wheeled-Humanoid Robot with Unknown Dynamics

Model-based controllers using a linearized model around the system's equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowledge in object dynamics. This paper presents a framework designed for predicting the new equilibrium point explicitly to control a wheeled-legged robot with unknown dynamics. We estimated the total mass and center of mass of the system from its response to initially unknown dynamics, then calculated the new equilibrium point accordingly. To avoid using additional sensors (e.g., force torque sensor) and reduce the effort of obtaining expensive real data, a data-driven approach is utilized with a novel real-to-sim adaptation. A more accurate nonlinear dynamics model, offering a closer representation of real-world physics, is injected into a rigid-body simulation for real-to-sim adaptation. The nonlinear dynamics model parameters were optimized using Particle Swarm Optimization. The efficacy of this framework was validated on a physical wheeled inverted pendulum, a simplified model of a wheeled-legged robot. The experimental results indicate that employing a more precise analytical model with optimized parameters significantly reduces the gap between simulation and reality, thus improving the efficiency of a model-based controller in controlling a wheeled robot with unknown dynamics.
Water Demand Forecasting Using Multi-layer Perceptron and Radial Basis Functions

Reliable and effective management of an existing water supply entity requires both long-term and short-term water demand forecasts. Conventionally, demographic and statistical models have been employed in modeling water demand forecasts. The technique of artificial neural networks has been proposed as an efficient tool for modeling and forecasting in recent years. The primary objective of this study is to investigate artificial neural networks for forecasting both short-term and long-term water demand in the Gauteng Province, in the Republic of South Africa. Neural network architectures used in this paper are the multi-layer perceptron (MLP) and the radial basis function (RBF). It was observed that the RBF converges to a solution faster than the MLP and it is the most accurate and the most reliable tool in terms of processing large amounts of non-linear, non-parametric data in this investigation.
Using evidence-based practices to enhance transfer of training: assessing the effectiveness of goal setting and behavioural observation scales

Using evidence-based practices we designed goal setting interventions, used in conjunction with behavioural observation scales (BOS), to facilitate transfer from a 2-day performance coaching programme. A total of 210 managers from public sector organisations took part in the study. Transfer was assessed using self-administered surveys and subordinate feedback (in the form of BOS). Transfer was high across all measures; however, our experimental design did not detect any positive effects for learning or behavioural outcome goals relative to being urged to Do Your Best (DYB). Results suggest that providing managers with BOS that outline the key skills covered in the training programme, having them set goals or urging them to do their best to use these skills back at work, and having workplace colleagues assess their performance at work using these BOS, may be sufficient to bring about transfer.
Comparison of right ventricular apex and right ventricular outflow tract septum pacing in the elderly with normal left ventricular ejection fraction: long-term follow-up.

BACKGROUND Whether right ventricular outfow tract septum (RVOTS) pacing is superior to right ventricular apex (RVA) pacing with respect to left ventricular synchrony, cardiac function, and remodelling in the elderly with normal left ventricular ejection fraction (LVEF), is still unknown. AIM To assess the impact of RVOTS vs. RVA pacing on the cardiac performance of the elderly with normal LVEF during a long-term observation. METHODS From 2007 to 2010, 65 patients with standard pacing indications for permanent pacing were recruited and randomised to receive RVA (32 patients) or RVOTS pacing (33 patients). Over a median 28 months' follow-up, available data was summarised, including New York Heart Association (NYHA) functional class, echocardiographic and pacing parameters, axis, QRS duration and plasma B-type natriuretic peptide (BNP) level. Then these values were compared between the RVA group and the RVOTS group, as well as between pacemaker pre- and post-implantation in the RVA group and in the RVOTS group, respectively. RESULTS There were no significant differences in baseline characteristics between the RVA group and the RVOTS group. The median pacing durations did not differ significantly between the groups (31.5 months in the RVA group vs. 28 months in the RVOTS group, p = 0.728). Compared to the baseline values, LVEF decreased with RVA pacing (from 59.5 ± 6.21 to 54.22 ± 8.73, p = 0.001), but LVEF did not markedly vary in the RVOTS group (57.82 ± 6.06 and 56.94 ± 5.54, p = 0.152). The number of patients with moderate tricuspid valve regurgitation remarkably increased in the RVA group, from six (18.75%) patients to 10 (31.3%) patients, preoperatively to postoperatively (p = 0.046), but this change was not statistically significant in the RVOTS group. Compared to the RVOTS group, NYHA functional class had a deteriorated tendency in the RVA group (p = 0.071). After the implantation, the increase of median BNP level was observed in the RVA group (35 pg/mL at preimplantation and 50 pg/mL at the end of follow-up, p = 0.007); No significant change was obtained in the RVOTS group (36.4 pg/mL at pre-implantation vs. 38 pg/ml at the end of follow-up, p = 0.102). Compared to the RVA pacing group, the mean QRS width narrowed substantially in the RVOTS pacing group (from 143.56 ± 12.90 to 105.52 ± 15.21, p = 0.000). In terms of the end diastolic and systolic diameters of the left ventricular, there were no statistical variations observed during the follow-up. CONCLUSIONS Permanent RVA pacing in elderly patients with normal LVEF led to left ventricular systolic function deterioration denoted by lower LVEF and higher BNP level. When compared to RVA pacing, RVOTS pacing had no remarkable benefit in terms of preventing cardiac remodelling.
Maximizing resonant converter efficiency through optimal switch selection

This paper presents the value of completing loss analysis for semiconductor switches in order to maximize the efficiency of any converter topology. A novel constant frequency CLL load resonant converter topology is used as the basis for the presented study. However, the analysis can be extended to other resonant, soft-switched, or hard-switched converters. For the 25 W converter used to present the analysis, it is shown that completing such a study can improve converter efficiency by more than 5%. Comparison between analysis results and switches with the lowest figure of merit (FOM) reveals that analysis is the only way to guarantee the most efficient switches for a given application have been selected
Strategies to Prevent and Heal the Mental Anguish Caused by Cyberbullying

As counselor educators, we are constantly interviewing middle school counselors to ensure current issues are discussed in our courses. During one such interview, a middle school counselor revealed an alarming conversation she had previously had with a student. The student stated, “I’m really scared about the message I got last night on my computer. ” “If it bothers you that much, just don’t log on,” I (interviewed counselor) said. “If I don’t log on, I won’t be able to hang out with my friends,” the girl responded. “Little did I (interviewed counselor) know that this young lady would later attempt suicide due to online bullying. ” Few middle school educators have escaped the numerous warnings regarding face-to-face school bullying. While this threat is certainly real, the more common danger inflicted via technology, cyberbullying, is relatively unknown to teachers and parents. Cyberbullying has moved bullying behaviors from the schoolyard to a worldwide audience. This exposure ensures that students receive the greatest humiliation possible both during and after school. Not surprisingly, research indicates that victims (who are often also bullies) suffer from mental health problems and lowered academic achievement (Hall, 2006; Reiff, 2006; Willard, 2007). The purpose of this article is to enlighten teachers, school administrators, and school counselors about this new technological danger, describe warning signs to help recognize both victims and perpetrators, and detail schooland home-based strategies for preventing cyberbullying and healing those affected by the phenomenon.
An Efficient General Algorithm for SAR Imaging: Complex Approximate Message Passing Combined With Backprojection

Due to the great computation load and memory cost of the matrix-vector multiplication, the sparse reconstruction algorithms are severely limited in the applications of radar imaging with real data. In order to solve this problem, we construct a backprojection-based range-azimuth decoupled operator (BP-RADOp) and combine the complex approximate message passing algorithm (CAMP) with it. We call this algorithm BP-CAMP in this letter. Since BP-RADOp retains the merits of the backprojection method entirely (i.e., perfect motion compensation for any flight path, precise focus for arbitrarily wide bandwidths and integration angles, low artifact levels, unlimited scene size, and strictly local processing), it has universal applicability in comparison with the other decoupled operators deduced from the fast Fourier transform-based image formation algorithms. The theoretical analysis indicates when BP-CAMP and CAMP are both used to reconstruct large-scale observed scenes; the former has lower computation load and memory cost than the latter. Meanwhile, it is demonstrated that BP-CAMP achieves high-quality synthetic aperture radar imaging with undersampled echo data, and it is as robust as CAMP to additive noise by the simulations and real data processing.
Spanning and derivative-security valuation q

This article provides the economic foundations for valuing derivative securities. In particular, it establishes how the characteristic function (of the future uncertainty) is basis augmenting and spans the payo! universe of most, if not all, derivative assets. From the characteristic function of the state-price density, it is possible to analytically price options on any arbitrary transformation of the underlying uncertainty. By di! erentiating (or translating) the characteristic function, limitless pricing and/or spanning opportunities can be designed. The strength and versatility of the methodology is inherent when valuing (1) average-interest options, (2) correlation options, and (3) discretely monitored knock-out options. ( 2000 Elsevier Science S.A. All rights reserved. JEL classixcation: G10; G12; G13
Deformation of n-dimensional objects

This paper presents a new technique for computing space deformations that interpolate a set of user-defined constraints. Constraints are specified by indicating the images of selected points. The deformation is formulated as the product of a polynomial function f of ℝn into a higher-dimensional space, ℝm, with a linear projection from ℝm back to ℝn. The projection matrix is computed using a pseudo-inverse technique. For sufficient m, the degrees of freedom may be used to optimize potential functions controlled by attracting and repulsing points. A prototype implementation is presented, which demonstrates the application of this technique to the interactive design of free-form shapes (when n=3) and of deformation processes in space-time domain (when n=4).
The matching law: A research review.

"The Matching Law" applies a behavioral perspective to its comprehensive coverage of empirical research conducted in the areas of choice and matching as well as within the associated area of behavioral approaches to signal detection. While presenting a quantitative overview of the extensive literature in these areas, "The Matching Law": * summarizes the extensive experimental literature on choice * describes and organizes these data * focuses on the ways in which animals and humans choose between simultaneous and successive alternatives * summarizes and reanalyses research on concurrent, concurrent-chain, and multiple schedules.
Quantum memory at nonzero temperature in a thermodynamically trivial system

Passive error correction protects logical information forever (in the thermodynamic limit) by updating the system based only on local information and few-body interactions. A paradigmatic example is the classical two-dimensional Ising model: a Metropolis-style Gibbs sampler retains the sign of the initial magnetization (a logical bit) for thermodynamically long times in the low-temperature phase. Known models of passive quantum error correction similarly exhibit thermodynamic phase transitions to a low-temperature phase wherein logical qubits are protected by thermally stable topological order. Here, in contrast, we show that constant-rate classical and quantum low-density parity check codes have no $\textit{thermodynamic}$ phase transitions at nonzero temperature, but nonetheless exhibit $\textit{ergodicity-breaking}$ dynamical transitions: below a critical nonzero temperature, the mixing time of local Gibbs sampling diverges in the thermodynamic limit. We conjecture that the circuit complexity of preparing extensive-energy states may diverge without crossing any thermodynamic transition. Fault-tolerant passive decoders, inspired by Gibbs samplers, may be amenable to measurement-free quantum error correction and may present a desirable experimental alternative to conventional quantum error correction based on syndrome measurements and active feedback.
Visual Imaginative Synchrony

Kutatasunkban az imaginativ tevekenyseg szinkronizacios folyamatanak vizualis modalitasat, az interakcios partnerek altal atelt Vizualis Imaginativ Szinkront (roviditve: VIS) vizsgaljuk. A VIS olyan interakcios szinkronjelenseg, mely az egymasra hangolodas egy jellegzetes formajat, a kepzeleti tevekenyseg kulső megitelő altal is azonosithato harmonizaciojat jelenti. A VIS operacionalizalasi folyamata alkalmazhato hipnozis es eber helyzetben egyarant. A Vizualis Imaginativ Szinkron vizsgalatahoz standardizalt eljaras segitsegevel inger-szavakat nyertunk. Az esetleges szinkronjelensegek megismeresehez kialakitottuk a rajz es leiras egyutteset alkalmazo VIS tesztet, melyet a diadok resztvevői minden egyes, interakcios helyzetenkent 5 darab motivum utan kulon kitoltottek. A VIS ertekeket a diadok VIS tesztjeinek fuggetlen megitelők hasonlosag alapjan tortenő megitelesevel hataroztuk meg. A tanulmanyban ismertetjuk a standardizalt keretek kozott zajlo hipnozis es eber vizsgalati helyzetek eredmenyeit, illetve .