Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the Anti-Vaxxers’ Reply Behavior on Social Media

Although the online campaigns of anti-vaccine advocates, or anti-vaxxers, severely threaten efforts for herd immunity, their reply behavior—-the form of directed messaging that can be sent beyond follow-follower relationships–remains poorly understood. Here, we examined the characteristics of anti-vaxxers’ reply behavior on Twitter to attempt to comprehend their characteristics of spreading their beliefs in terms of interaction frequency, content, and targets. Among the results, anti-vaxxers more frequently conducted reply behavior with other clusters, especially neutral accounts. Anti-vaxxers’ replies were significantly more toxic than those from neutral accounts and pro-vaxxers, and their toxicity, in particular, was higher with regard to the rollout of vaccines. Anti-vaxxers’ replies were more persuasive than the others in terms of the emotional aspect, rather than linguistical styles. The targets of anti-vaxxers’ replies tend to be accounts with larger numbers of followers and posts, including accounts that relate to health care or represent scientists, policy-makers, or media figures or outlets. We discussed how their reply behaviors are effective in spreading their beliefs, as well as possible countermeasures to restrain them. These findings should prove useful for pro-vaxxers and platformers to promote trusted information while reducing the effect of vaccine disinformation.
Quality Management: Introduction to Total Quality Management for Production, Processing, and Services

PART ONE. PHILOSOPHY AND CONCEPTS. 1. The Total Quality Approach to Quality Management. 2. Quality and Global Competitiveness. 3. Strategic Management: Planning and Execution. 4. Quality Management, Ethics, and Corporate Social Responsibility. 5. Partnering and Strategic Alliances. 6. Quality Culture: Changing Hearts, Minds and Attitudes. 7. Customer Satisfaction and Retention. 8. Employee Empowerment. 9. Leadership and Change. 10. Team Building and Teamwork. 11. Communication and Interpersonal Relations. 12. Education and Training. 13. Overcoming Politics, Negativity and Conflict in the Workplace. 14. ISO 9000 and Total Quality: The Relationship. PART TWO. TOOLS AND TECHNIQUES. 15. Overview of Total Quality Tools. 16. Problem Solving and Decision Making. 17. Quality Function and Deployment (QFD). 18. Optimizing and Controlling Processes through Statistical Process Control (SPC). 19. Continual Improvement. 20. Benchmarking. 21. Just-in-Time Manufacturing (JIT). 22. Implementing Total Quality Management. Index.
The expectation-maximization algorithm for autoregressive models with normal inverse Gaussian innovations

The autoregressive (AR) models are used to represent the time-varying random process in which output depends linearly on previous terms and a stochastic term (the innovation). In the classical version, the AR models are based on normal distribution. However, this distribution does not allow describing data with outliers and asymmetric behavior. In this paper, we study the AR models with normal inverse Gaussian (NIG) innovations. The NIG distribution belongs to the class of semi heavy-tailed distributions with wide range of shapes and thus allows for describing real-life data with possible jumps. The expectation-maximization (EM) algorithm is used to estimate the parameters of the considered model. The efficacy of the estimation procedure is shown on the simulated data. A comparative study is presented, where the classical estimation algorithms are also incorporated, namely, Yule-Walker and conditional least squares methods along with EM method for model parameters estimation. The applications of the introduced model are demonstrated on the real-life financial data.
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values

Data is of high quality if it is fit for its intended use. The quality of data is influenced by the underlying data model and its quality. One major quality problem is the heterogeneity of data as quality aspects such as understandability and interoperability are impaired. This heterogeneity may be caused by quality problems in the data model. Data heterogeneity can occur in particular when the information given is not structured enough and just captured in data values, often due to missing or non-suitable structure in the underlying data model. We propose a bottom-up approach to detecting quality problems in data models that manifest in heterogeneous data values. It supports an explorative analysis of the existing data and can be configured by domain experts according to their domain knowledge. All values of a selected data field are clustered by syntactic similarity. Thereby an overview of the data values’ diversity in syntax is provided. It shall help domain experts to understand how the data model is used in practice and to derive potential quality problems of the data model. We outline a proof-of-concept implementation and evaluate our approach using cultural heritage data.
Dataset of Philippine Presidents Speeches from 1935 to 2016

Purpose–The dataset was collected to examine and identify possible key topicswithin these texts. Method–Data preparation such as data cleaning, transformation, tokenization, removal of stop wordsfrom both English and Filipino, and word stemmingwas employed in the datasetbefore feeding it to sentiment analysis and the LDA model.Results–The topmost occurring word within the dataset is "development" and there are three (3) likely topics from the speeches of Philippine presidents: economic development, enhancement of public services, and addressing challenges.Conclusion–The datasetwas ableto provide valuable insights contained among official documents. While the study showedthatpresidentshave used their annual address to express their visions for the country. It alsopresentedthat the presidents from 1935 to 2016 faced the same problems during their term.Recommendations–Future researchers may collect other speeches made by presidents during their term;combine them to the dataset used in this studyto furtherinvestigate these important textsby subjecting them to the same methodology used in this study.The dataset may be requested from the authors and it is recommended for further analysis. For example, determine how the speeches of the president reflect the preamble or foundations of the Philippine constitution.
Self-Energy Recycling for Low-Power Reliable Networks: Half-Duplex or Full-Duplex?

Self-energyrecycling (sER), which allows transmit energy reutilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this article, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multiantenna techniques in a communication system. We analyze the tradeoffs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well-known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this article. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.
Bounding Queue Length Violation Probability of Joint Channel and Buffer Aware Transmission

Queue length violation probability, i.e., the tail distribution of the queue length, is a widely used statistical quality-of-service (QoS) metric in wireless communications. Characterizing and optimizing the queue length violation probability have great significance in time sensitive networking (TSN) and ultra reliable and low-latency communications (URLLC). However, it still remains an open problem. In this paper, we put our focus on the analysis of the tail distribution of the queue length from the perspective of cross-layer design in wireless link transmission. We find that, under the finite average power consumption constraint, the queue length violation probability can achieve zero with diversity gains, while it can have a linear-decay-rate exponent according to large deviation theory (LDT) with limited receiver sensitivity. Besides, we find that the queue-length tail distribution with an arbitrary-decay-rate exponent under the finite average power constraint exists in the Rayleigh fading channel. Then, we generalize the sufficient conditions for the communication system belonging to these three scenarios, respectively. Moreover, we apply the above results to analyze the wireless link transmission in the Nakagami-m fading channel. Numerical results with approximation validate our analysis.
Galois groups of random integer polynomials and van der Waerden's Conjecture

Of the (2 H + 1) n monic integer polynomials f ( x ) = x n + a 1 x n − 1 + · · · + a n with max {| a 1 | , . , | a n |} ≤ H , how many have associated Galois group that is not the full symmetric group S n ? There are clearly ≫ H n − 1 such polynomials, as may be obtained by setting a n = 0. In 1936, van der Waerden conjectured that O ( H n − 1 ) should in fact also be the correct upper bound for the count of such polynomials. The conjecture has been known previously for degrees n ≤ 4, due to work of van der Waerden and Chow and Dietmann. The purpose of this paper is to prove van der Waerden’s Conjecture for all degrees n .
Serial powering and signal integrity characterization for the TEPX detector for the Phase-2 CMS Inner Tracker

The entire CMS silicon pixel detector will be replaced to operate at the high-luminosity LHC. In this contribution, the new tracker endcap pixel detector will be presented, focusing on a novel concept to provide both power and data connectivity to the modules through a disk-shaped PCB. As this part of the detector also features the longest serial powering chains in the CMS Phase-2 Inner Tracker, emphasis is put on serial powering results and the characterization of the signal integrity.
Monte Carlo dropout increases model repeatability

The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is given to classification performance without assessing the model repeatability, leading to the development of models that turn out to be unusable in practice. In this work, we evaluate the repeatability of four model types on images from the same patient that were acquired during the same visit. We study the performance of binary, multi-class, ordinal, and regression models on three medical image analysis tasks: cervical cancer screening, breast density estimation, and retinopathy of prematurity classification. Moreover, we assess the impact of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability. Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity

Reinforcement learning algorithms often require finiteness of state and action spaces in Markov decision processes (MDPs) (also called controlled Markov chains) and various efforts have been made in the literature towards the applicability of such algorithms for continuous state and action spaces. In this paper, we show that under very mild regularity conditions (in particular, involving only weak continuity of the transition kernel of an MDP), Q-learning for standard Borel MDPs via quantization of states and actions (called Quantized Q-Learning) converges to a limit, and furthermore this limit satisfies an optimality equation which leads to near optimality with either explicit performance bounds or which are guaranteed to be asymptotically optimal. Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a partially observed Markov decision process (POMDP), (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP. Thus, our paper presents a very general convergence and approximation result for the applicability of Q-learning for continuous MDPs.
Local algorithms for maximum cut and minimum bisection on locally treelike regular graphs of large degree

Given a graph G$$ G $$ of degree k$$ k $$ over n$$ n $$ vertices, we consider the problem of computing a near maximum cut or a near minimum bisection in polynomial time. For graphs of girth 2L$$ 2L $$ , we develop a local message passing algorithm whose complexity is O(nkL)$$ O(nkL) $$ , and that achieves near optimal cut values among all L$$ L $$ ‐local algorithms. Focusing on max‐cut, the algorithm constructs a cut of value nk/4+nP⋆k/4+err(n,k,L)$$ nk/4+n{P}_{\star}\sqrt{k/4}+\mathsf{err}\left(n,k,L\right) $$ , where P⋆≈0.763166$$ {P}_{\star}\approx 0.763166 $$ is the value of the Parisi formula from spin glass theory, and err(n,k,L)=on(n)+nok(k)+nkoL(1)$$ \mathsf{err}\left(n,k,L\right)={o}_n(n)+n{o}_k\left(\sqrt{k}\right)+n\sqrt{k}{o}_L(1) $$ (subscripts indicate the asymptotic variables). Our result generalizes to locally treelike graphs, that is, graphs whose girth becomes 2L$$ 2L $$ after removing a small fraction of vertices. Earlier work established that, for random k$$ k $$ ‐regular graphs, the typical max‐cut value is nk/4+nP⋆k/4+on(n)+nok(k)$$ nk/4+n{P}_{\star}\sqrt{k/4}+{o}_n(n)+n{o}_k\left(\sqrt{k}\right) $$ . Therefore our algorithm is nearly optimal on such graphs. An immediate corollary of this result is that random regular graphs have nearly minimum max‐cut, and nearly maximum min‐bisection among all regular locally treelike graphs. This can be viewed as a combinatorial version of the near‐Ramanujan property of random regular graphs.
Scalable Algorithms for Bicriterion Trip-Based Transit Routing

This paper proposes multiple extensions to the popular bicriterion transit routing approach -- Trip-Based Transit Routing (TBTR). Specifically, building on the premise of the HypRAPTOR algorithm, we first extend TBTR to its partitioning variant -- HypTBTR. However, the improvement in query times of HyTBTR over TBTR comes at the cost of increased preprocessing. To counter this issue, two new techniques are proposed -- a One-To-Many variant of TBTR and multilevel partitioning. Our One-To-Many algorithm can rapidly solve profile queries, which not only reduces the preprocessing time for HypTBTR, but can also aid other popular approaches such as HypRAPTOR. Next, we integrate a multilevel graph partitioning paradigm in HypTBTR and HypRAPTOR to reduce the fill-in computations. The efficacy of the proposed algorithms is extensively tested on real-world large-scale datasets. Additional analysis studying the effect of hypergraph partitioning tools (hMETIS, KaHyPar, and an integer program) along with different weighting schemes is also presented.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs

Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and are often unknown in advance. This occurs in many practical settings, such as hyperparameter tuning of machine learning algorithms or physics-based simulation optimization. Moreover, those few existing methods that acknowledge cost heterogeneity do not naturally accommodate a budget constraint on the total evaluation cost. This combination of unknown costs and a budget constraint introduces a new dimension to the exploration-exploitation trade-off, where learning about the cost incurs the cost itself. Existing methods do not reason about the various trade-offs of this problem in a principled way, leading often to poor performance. We formalize this claim by proving that the expected improvement and the expected improvement per unit of cost, arguably the two most widely used acquisition functions in practice, can be arbitrarily inferior with respect to the optimal non-myopic policy. To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs. Finally, we show that our acquisition function outperforms existing methods in a variety of synthetic and real problems.
Interior epsilon-regularity theory for the solutions of the magneto-micropolar equations with a perturbation term

We develop here a particular version of the partial regularity theory for the Magneto-Micropolar equations (MMP) where a perturbation term is added. These equations are used in some special cases, such as in the study of the evolution of liquid cristals or polymers, where the classical Navier-Stokes equations are not an accurate enough model. The incompressible Magneto-Micropolar system is composed of three coupled equations: the first one is based in the Navier-Stokes system, the second one considers mainly the magnetic field while the last equation introduces the microrotation field representing the angular velocity of the rotation of the fluid particles. External forces are considered and a specific perturbation term is added as it is quite useful in some applications.
Supersonic Expansion of the Bipolar H ii Region Sh2-106: A 3500 Year Old Explosion?

Multi-epoch narrowband Hubble Space Telescope images of the bipolar H ii region Sh2-106 reveal highly supersonic nebular proper motions that increase with projected distance from the massive young stellar object S106 IR, reaching over ∼30 mas yr−1 (∼150 km s−1 at D = 1.09 kpc) at a projected separation of ∼1. ′4 (0.44 pc) from S106 IR. We propose that S106 IR experienced a ∼1047 erg explosion ∼3500 yr ago. The explosion may be the result of a major accretion burst or a recent encounter with another star, or a consequence of the interaction of a companion with the bloated photosphere of S106 IR as it grew from ∼10 through ∼15 M ⊙ at a high accretion rate. Near-IR images reveal fingers of H2 emission pointing away from S106 IR and an asymmetric photon-dominated region surrounding the ionized nebula. Radio continuum and Brγ emission reveal a C-shaped bend in the plasma, indicating either the motion of S106 IR toward the east, or the deflection of plasma toward the west by the surrounding cloud. The H ii region bends around a ∼1′ diameter dark bay west of S106 IR that may be shielded from direct illumination by a dense molecular clump. Herbig–Haro and Molecular Hydrogen Objects tracing outflows powered by stars in the Sh2-106 protocluster such as the Class 0 source S106 FIR are discussed.
Guided Wave Tomography on an Aircraft Wing with Leave in Place Sensors

Leave in place sensor and guided wave tomographic techniques are combined together in structural health monitoring aging aircraft. In a sample problem, eight PZT transducers are mounted on the surface of the wing of an E2 aircraft. An area within a circle of 240 mm diameter is monitored with through transmission guided waves. The occurrence and progression of an artificial defect beside one rivet is clearly detected. Different computed tomography (CT) algorithms such as the back projection method, algebraic reconstruction method (ART) and multi resolution algebraic reconstruction technology (MART) are used in the defect image reconstruction with the feature of signal difference coefficient (SDC).
GaAs bipolar transistors with a Ga/sub 0.5/In/sub 0.5/P hole barrier layer and carbon-doped base grown by MOVPE

GaAs bipolar transistors with a 50-AA-thick lattice matched Ga/sub 0.5/In/sub 0.5/P layer between the emitter and base acting as a hole repelling potential barrier in the valence band were fabricated from films grown by metalorganic vapor phase epitaxy (MOVPE). The 1000-AA-thick base was doped with carbon to 2*10/sup 19/ cm/sup -3/, resulting in a base sheet resistance of 250 Omega / Square Operator . Carbon has been chosen because of its low diffusivity. Using the barrier layer as an etch stop the authors fabricated mesa-type broad-area devices. The output characteristics of the devices are ideal with very small offset voltages and infinite Early voltages. Common emitter current gains of up to 70 at 10/sup 4/ A/cm/sup 2/ collector current density were obtained. The current gain is clearly higher than the one calculated for a bipolar junction transistor with the same doping profile because the base-emitter hole current is suppressed by the Ga/sub 0.5/In/sub 0.5/P potential barrier in the valence band. >
Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environmental information but the compact and precise measurements provided by the environment. In this paper, a vision-based control algorithm is proposed and compared with human player performances under the same conditions in realistic racing scenarios using Gran Turismo Sport (GTS), which is known as a high-fidelity realistic racing simulator. In the proposed method, the environmental information that constitutes part of the observations in conventional state-of-the-art methods is replaced with feature representations extracted from game screen images. We demonstrate that the proposed method performs expert human-level vehicle control under high-speed driving scenarios even with game screen images as high-dimensional inputs. Additionally, it outperforms the built-in AI in GTS in a time trial task, and its score places it among the top 10% approximately 28,000 human players.
Multimodal Virtual Point 3D Detection

Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current Lidar sensors still lag two decades behind traditional color cameras in terms of resolution and cost. For autonomous driving, this means that large objects close to the sensors are easily visible, but far-away or small objects comprise only one measurement or two. This is an issue, especially when these objects turn out to be driving hazards. On the other hand, these same objects are clearly visible in onboard RGB sensors. In this work, we present an approach to seamlessly fuse RGB sensors into Lidar-based 3D recognition. Our approach takes a set of 2D detections to generate dense 3D virtual points to augment an otherwise sparse 3D point cloud. These virtual points naturally integrate into any standard Lidar-based 3D detectors along with regular Lidar measurements. The resulting multi-modal detector is simple and effective. Experimental results on the large-scale nuScenes dataset show that our framework improves a strong CenterPoint baseline by a significant 6.6 mAP, and outperforms competing fusion approaches. Code and more visualizations are available at https://tianweiy.github. io/mvp/