Miracle-dz/UniFAR-FTUs huggingface dataset:
SHOULD STUDENTS FALLING BEHIND IN SCHOOL TAKE DUAL CREDIT COURSES?

THE EFFECT This study examines the effect of dual credit (DC) on college enrollment and degree completion for less academically prepared students who attended public high schools in Texas. We employ a propensity score matching method to reduce selection bias from DC participation and supplement the analysis with a bounds test. The results show that DC students were predicted to have a higher likelihood of entering college immediately after high school and completing a degree within four and eight years of high school graduation, compared to similar students who did not take DC courses. This evidence suggests that that DC programs contributed to a reduction in educational inequities in college access and degree attainment for students at risk of academic failure. On the other hand, students who were racial or ethnic minorities and students from low-income families were not only less likely to participate in DC programs but were also predicted to have smaller participation effects on college degree attainment than their counterparts, stressing the need for higher education institutions and partnering school districts to provide more robust support to these underserved students for participating in DC programs and making a successful transition into college.
Impact of industrial knowledge base on knowledge sourcing: the case of printing industry

Latest researches that investigate the creation, diffusion and utilisation of economically useful knowledge go beyond questions like how much resources need to be invested to these processes by enterprises and what the result of these investments are. The objects of analysis have been shifted to issues like how knowledge creation, diffusion and utilisation actually take place at firm level, what kind of characteristics could be observed in this process and how much do the outputs contribute to the performance of firms, sectors and regions. Several theoretical frameworks have been developed to answer these questions from which the differentiated (analytical, synthetic, symbolic) knowledge bases theory seems a prevailing one. Hitherto the approach has been mostly used in developed countries yet it seems an appropriate framework to analyse the creation, diffusion and utilisation of industry specific knowledge and its spatial aspects at the same time in less developed countries like Hungary. In the present paper we apply this framework to investigate the local printing industry in Kecskemét (Hungary) presumably characterised by synthetic knowledge base mainly. The aim is to unfold and analyse the characteristics of knowledge flows in the industry and to explore whether the revealed pattern is in line with the theory. Therefore the following questions are addressed: what is the main geographical scene of knowledge acquisition, what are the main sources of new knowledge and who are the main partners in the knowledge sourcing process. Evidence proof theory-led expectations, and reveal the relevance of direct knowledge sources, the importance of co-localization and the dominancy of engineering-based knowledge in the industry.
Effect of Zone’s Diet Modification in Level of Oxidative Stress and Performance of Athletes

This study aimed to prove which modification of Zone diet can improve the performance of athletes and may reduce levels of oxidative stress in the body of the athlete, especially a sprinting athlete. Used draft “Randomized Control Group Pretest Posttest Design”. The 20 sample taken from people who has the criteria for inclusion and exclusion of 30 athletes sprint teenagers Persatuan Atletik Seluruh Indonesia (PASI) East Java. This study used an experimental method and observation. Data collected by result of running measurement technique using finish camera and laboratories to determine activity levels of oxidative stress (MDA) in the blood. Data were analyzed with multivariate techniques (Manova) method Hotelling’s (T2). Data analysis included descriptive analysis, data normality, homogeneity of variance and analysis Manova form Paired Comparisons Hotelling’s method (T2). Hypothesis testing using α 0.05. Result and conclusion of the study, that the normal diet had no effect on the variable performance of athletes and level of oxidative stress (MDA). While modification Zone diet can lowering the level of oxidative stress(MDA) minimum 0,0371 μml and an average reduction of level of oxidative stress caused modification Zone diet at least 0,010 μml lower than normal diet but no effect to performance of athletes.
Industry 5.0 is Coming: A Survey on Intelligent NextG Wireless Networks as Technological Enablers

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.
Searching for subgroup-specific associations while controlling the false discovery rate

This paper introduces an innovative method for conducting conditional independence testing in high-dimensional data, facilitating the automated discovery of significant associations within distinct subgroups of a population, all while controlling the false discovery rate. This is achieved by expanding upon the model-X knockoff filter to provide more informative inferences. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern machine learning models. Specifically, our method is able to leverage any model for the identification of data-driven hypotheses pertaining to interesting population subgroups. Then, it rigorously test these hypotheses without succumbing to selection bias. Importantly, our approach is efficient and does not require sample splitting. We demonstrate the effectiveness of our method through simulations and numerical experiments, using data derived from a randomized experiment featuring multiple treatment variables.
Hierarchical Distribution-Aware Testing of Deep Learning

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Input added with adversarial perturbations, i.e. Adversarial Examples (AEs) are easily mis-predicted by the DL model. Despite recent efforts made in detecting AEs via state-of-the-art attack and testing methods, they are normally input distribution agnostic and/or disregard the perceptual quality of adversarial perturbations. Consequently, the detected AEs are irrelevant inputs in the application context or unrealistic that can be easily noticed by humans. This may lead to a limited effect on improving the DL model’s dependability, as the testing budget is likely to be wasted on detecting AEs that are encountered very rarely in its real-life operations. In this paper, we propose a new robustness testing approach for detecting AEs that considers both the feature level distribution and the pixel level distribution, capturing the perceptual quality of adversarial perturbations. The two considerations are encoded by a novel hierarchical mechanism. First, we select test seeds based on the density of feature level distribution and the vulnerability of adversarial robustness. The vulnerability of test seeds are indicated by the auxiliary information, that are highly correlated with local robustness. Given a test seed, we then develop a novel genetic algorithm based local test case generation method, in which two fitness functions work alternatively to control the perceptual quality of detected AEs. Finally, extensive experiments confirm that our holistic approach considering hierarchical distributions is superior to the state-of-the-arts that either disregard any input distribution or only consider a single (non-hierarchical) distribution, in terms of not only detecting imperceptible AEs but also improving the overall robustness of the DL model under testing.
Avoiding a Cluster Catastrophe: Retention Efficiency and the Binary Black Hole Mass Spectrum

The population of binary black hole mergers identified through gravitational waves has uncovered unexpected features in the intrinsic properties of black holes in the universe. One particularly surprising and exciting result is the possible existence of black holes in the pair-instability mass gap, ∼50–120 M ⊙. Dense stellar environments can populate this region of mass space through hierarchical mergers, with the retention efficiency of black hole merger products strongly dependent on the escape velocity of the host environment. We use simple toy models to represent hierarchical merger scenarios in various dynamical environments. We find that hierarchical mergers in environments with high escape velocities (≳300 km s−1) are efficiently retained. If such environments dominate the binary black hole merger rate, this would lead to an abundance of high-mass mergers that is potentially incompatible with the empirical mass spectrum from the current catalog of binary black hole mergers. Models that efficiently generate hierarchical mergers, and contribute significantly to the observed population, must therefore be tuned to avoid a “cluster catastrophe” of overproducing binary black hole mergers within and above the pair-instability mass gap.
BlueSky: Activity Control: A Vision for "Active" Security Models for Smart Collaborative Systems

Cyber physical ecosystem connects different intelligent devices over heterogeneous networks. Various operations are performed on smart objects to ensure efficiency and to support automation in smart environments. An Activity (defined by Gupta and Sandhu) reflects the current state of an object, which changes in response to requested operations. Due to multiple running activities on different objects, it is critical to secure collaborative systems considering run-time decisions impacted due to related activities (and other parameters) supporting active enforcement of access control decision. Recently, Gupta and Sandhu proposed Activity-Centric Access Control (ACAC) and discussed the notion of activity as a prime abstraction for access control in collaborative systems. The model provides an active security approach that considers activity decision factors such as authorizations, obligations, conditions, and dependencies among related device activities. This paper takes a step forward and presents the core components of an ACAC model and compares with other security models differentiating novel properties of ACAC. We highlight how existing models do not (or in limited scope) support 'active' decision and enforcement of authorization in collaborative systems. We propose a hierarchical structure for a family of ACAC models by gradually adding the properties related to notion of activity and discuss states of an activity. We highlight the convergence of ACAC with Zero Trust tenets to reflect how ACAC supports necessary security posture of distributed and connected smart ecosystems. This paper aims to gain a better understanding of ACAC in collaborative systems supporting novel abstractions, properties and requirements.
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Upon automatic identifying logical reasoning phenomena in massive text corpus via detection heuristics, we train language models to predict the masked-out logical statements. Inspired by the facilitation effect of reflective thinking in human learning, we analogically simulate the learning-thinking process with an adversarial Generator-Verifier architecture to assist logic learning. LogiGAN implements a novel sequential GAN approach that (a) circumvents the non-differentiable challenge of the sequential GAN by leveraging the Generator as a sentence-level generative likelihood scorer with a learning objective of reaching scoring consensus with the Verifier; (b) is computationally feasible for large-scale pre-training with arbitrary target length. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets requiring general reasoning abilities, revealing the fundamental role of logic in broad reasoning, as well as the effectiveness of LogiGAN. Ablation studies on LogiGAN components reveal the relative orthogonality between linguistic and logic abilities and suggest that reflective thinking's facilitation effect might also generalize to machine learning.
Understanding Dynamic Tactile Sensing for Liquid Property Estimation

Humans perceive the world by interacting with objects, which often happens in a dynamic way. For example, a human would shake a bottle to guess its content. However, it remains a challenge for robots to understand many dynamic signals during contact well. This paper investigates dynamic tactile sensing by tackling the task of estimating liquid properties. We propose a new way of thinking about dynamic tactile sensing: by building a light-weighted data-driven model based on the simplified physical principle. The liquid in a bottle will oscillate after a perturbation. We propose a simple physics-inspired model to explain this oscillation and use a high-resolution tactile sensor GelSight to sense it. Specifically, the viscosity and the height of the liquid determine the decay rate and frequency of the oscillation. We then train a Gaussian Process Regression model on a small amount of the real data to estimate the liquid properties. Experiments show that our model can classify three different liquids with 100% accuracy. The model can estimate volume with high precision and even estimate the concentration of sugar-water solution. It is data-efficient and can easily generalize to other liquids and bottles. Our work posed a physically-inspired understanding of the correlation between dynamic tactile signals and the dynamic performance of the liquid. Our approach creates a good balance between simplicity, accuracy, and generality. It will help robots to better perceive liquids in different environments such as kitchens, food factories, and pharmaceutical factories.
Deep learning on rail profiles matching

Matching the rail cross-section profiles measured on site with the designed profile is a must to evaluate the wear of the rail, which is very important for track maintenance and rail safety. So far, the measured rail profiles to be matched usually have four features, that is, large amount of data, diverse section shapes, hardware made errors, and human experience needs to be introduced to solve the complex situation on site during matching process. However, traditional matching methods based on feature points or feature lines could no longer meet the requirements. To this end, we first establish the rail profiles matching dataset composed of 46386 pairs of professional manual matched data, then propose a general high-precision method for rail profiles matching using pre-trained convolutional neural network (CNN). This new method based on deep learning is promising to be the dominant approach for this issue. Source code is at https://github.com/Kunqi1994/Deep-learning-on-rail-profile-matching.
Field Distortion Model Based on Fredholm Integral

—Field distortion is widespread in imaging systems. If it cannot be measured and corrected well, it will affect the accuracy of photogrammetry. To this end, we proposed a general ﬁeld distortion model based on Fredholm integration, which uses a reconstructed high-resolution reference point spread function (PSF) and two sets of 4-variable polynomials to describe an imaging system. The model includes the point-to-point positional distortion from the object space to the image space and the deformation of the PSF so that we can measure an actual ﬁeld distortion with arbitrary accuracy. We also derived the formula required for correcting the sampling effect of the image sensor. Through numerical simulation, we verify the effectiveness of the model and reconstruction algorithm. This model will have potential application in high-precision image calibration, photogrammetry and astrometry.
Non-asymptotic confidence bands on the probability an individual benefits from treatment (PIBT)

The premise of this work, in a vein similar to predictive inference with quantile regression, is that observations may lie far away from their conditional expectation. In the context of causal inference, due to the missing-ness of one outcome, it is difficult to check whether an individual's treatment effect lies close to its prediction given by the estimated Average Treatment Effect (ATE) or Conditional Average Treatment Effect (CATE). With the aim of augmenting the inference with these estimands in practice, we further study an existing distribution-free framework for the plug-in estimation of bounds on the probability an individual benefits from treatment (PIBT), a generally inestimable quantity that would concisely summarize an intervention's efficacy if it could be known. Given the innate uncertainty in the target population-level bounds on PIBT, we seek to better understand the margin of error for the estimation of these target parameters in order to help discern whether estimated bounds on treatment efficacy are tight (or wide) due to random chance or not. In particular, we present non-asymptotic guarantees to the estimation of bounds on marginal PIBT for a randomized controlled trial (RCT) setting. We also derive new non-asymptotic results for the case where we would like to understand heterogeneity in PIBT across strata of pre-treatment covariates, with one of our main results in this setting making strategic use of regression residuals. These results, especially those in the RCT case, can be used to help with formal statistical power analyses and frequentist confidence statements for settings where we are interested in inferring PIBT through the target bounds under minimal parametric assumptions.
Meta-Learning Sparse Compression Networks

Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal. When such functions are approximated by neural networks this introduces a compelling alternative to the more common multi-dimensional array representation. Recent work on such Implicit Neural Representations (INRs) has shown that - following careful architecture search - INRs can outperform established compression methods such as JPEG (e.g. Dupont et al., 2021). In this paper, we propose crucial steps towards making such ideas scalable: Firstly, we employ state-of-the-art network sparsification techniques to drastically improve compression. Secondly, introduce the first method allowing for sparsification to be employed in the inner-loop of commonly used Meta-Learning algorithms, drastically improving both compression and the computational cost of learning INRs. The generality of this formalism allows us to present results on diverse data modalities such as images, manifolds, signed distance functions, 3D shapes and scenes, several of which establish new state-of-the-art results.
A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems

This paper tackles the problem of making complex resource-constrained cyber-physical systems (CPS) resilient to sensor anomalies. In particular, we present a framework for checkpointing and roll-forward recovery of state-estimates in nonlinear, hierarchical CPS with anomalous sensor data. We introduce three checkpointing paradigms for ensuring different levels of checkpointing consistency across the hierarchy. Our framework has algorithms implementing the consistent paradigm to perform accurate recovery in a time-efficient manner while managing the tradeoff with system resources and handling the interplay between diverse anomaly detection systems across the hierarchy. Further in this work, we detail bounds on the recovered state-estimate error, maximum tolerable anomaly duration and the accuracy-resource gap that results from the aforementioned tradeoff. We explore use-cases for our framework and evaluate it on a case study of a simulated ground robot to show that it scales to multiple hierarchies and performs better than an extended Kalman filter (EKF) that does not incorporate a checkpointing procedure during sensor anomalies. We conclude the work with a discussion on extending the proposed framework to distributed systems.
Design of metamaterial-based heat manipulators by isogeometric shape optimization

There has been a growing interest in controlled heat flux manipulation to increase the efficiency of thermal apparatus. Heat manipulators control and manipulate heat flow. A key to the effective performance of these heat manipulators is their thermal design. Such designs can be achieved by a periodic assembly of unit cells (known as metamaterials or meta-structure), whose geometry and material properties can be optimized for a specific objective. In this work, we focus on thermal metamaterial-based heat manipulators such as thermal concentrator (which concentrates the heat flux in a specified region of the domain). The main scope of the current work is to optimize the shape of the heat manipulators using Particle Swarm Optimization (PSO) method. The geometry is defined using NURBS basis functions due to the higher smoothness and continuity and the thermal boundary value problem is solved using Isogeometric Analysis (IGA). Often, nodes as design variables (as in Lagrange finite element method) generate the serrate shapes of boundaries which need to be smoothened later. For the NURBS-based boundary with the control points as design variables, the required smoothness can be predefined through knot vectors and smoothening in the post-processing can be avoided. The optimized shape generated by PSO is compared with the other shape exploited in the literature. The effects of the number of design variables, the thermal conductivity of the materials used, as well as some of the geometry parameters on the optimum shapes are also demonstrated.