Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradient Boosted Flows

Normalizing flows (NF) are a powerful framework for approximating posteriors. By mapping a simple base density through invertible transformations, flows provide an exact method of density evaluation and sampling. The trend in normalizing flow literature has been to devise deeper, more complex transformations to achieve greater flexibility. We propose an alternative: Gradient Boosted Flows (GBF) model a variational posterior by successively adding new NF components by gradient boosting so that each new NF component is fit to the residuals of the previously trained components. The GBF formulation results in a variational posterior that is a mixture model, whose flexibility increases as more components are added. Moreover, GBFs offer a wider, not deeper, approach that can be incorporated to improve the results of many existing NFs. We demonstrate the effectiveness of this technique for density estimation and, by coupling GBF with a variational autoencoder, generative modeling of images.
Estimations of Cross-Sections for Photonuclear Reaction on Calcium Isotopes by Artificial Neural Networks

The nuclear reaction induced by photon is one of the important tools in the investigation of atomic nuclei. In the reaction, a target material is bombarded by photons with high-energies in the range of gamma-ray energy range. In the bombarding process, the photons can statistically be absorbed by a nucleus in the target material. Then the excited nucleus can decay by emitting proton, neutron, alpha and light particles or photons. By performing photonuclear reaction on the target, it can be easily investigated low-lying excited states of the nuclei. In the present work, ({\gamma}, n) photonuclear reaction cross-sections on different calcium isotopes have been estimated by using artificial neural network method. The method is a mathematical model that mimics the brain functionality of the creatures. The correlation coefficient values of the method for both training and test phases being 0.99 indicate that the method is very suitable for this purpose.
Mesoscopic spin transport between strongly interacting Fermi gases

We investigate a mesoscopic spin current for strongly interacting Fermi gases through a quantum point contact. Under the situation where spin polarizations in left and right reservoirs are same in magnitude but opposite in sign, we calculate the contribution of quasiparticles to the current by means of the linear response theory and many-body $T$-matrix approximation. For a small spin-bias regime, the current in the vicinity of the superfluid transition temperature is strongly suppressed due to the formation of pseudogaps. For a large spin-bias regime where the gases become highly polarized, on the other hand, the current is affected by the enhancement of a minority density of states due to Fermi polarons. We also discuss the broadening of a quasiparticle peak associated with an attractive polaron at a large momentum, which is relevant to the enhancement.
Policy Evaluation Networks

Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a single policy while generalizing across many states. The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states. This approach opens up the possibility of performing direct gradient ascent in policy space without seeing any new data. The main challenge for this approach is finding a way to represent complex policies that facilitates learning and generalization. To address this problem, we introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements (learned Policy Evaluation Network, policy fingerprints, gradient ascent) can produce policies that outperform those that generated the training data, in zero-shot manner.
Magnetars from Neutron Star–White Dwarf Mergers: Application to Fast Radio Bursts

It is widely believed that magnetars could be born in core-collapse supernovae (SNe), binary neutron star (BNS) or binary white dwarf (BWD) mergers, or accretion-induced collapse (AIC) of white dwarfs. In this paper, we investigate whether magnetars could also be produced from neutron star–white dwarf (NSWD) mergers, motivated by FRB 180924-like fast radio bursts (FRBs) possibly from magnetars born in BNS/BWD/AIC channels suggested by Margalit et al. (2019). By a preliminary calculation, we find that NSWD mergers with unstable mass transfer could result in the NS acquiring an ultra-strong magnetic field via the dynamo mechanism due to differential rotation and convection or possibly via the magnetic flux conservation scenario of a fossil field. If NSWD mergers can indeed create magnetars, then such objects could produce at least a subset of FRB 180924-like FRBs within the framework of flaring magnetars, since the ejecta, local environments, and host galaxies of the final remnants from NSWD mergers resemble those of BNS/BWD/AIC channels. This NSWD channel is also able to well explain both the observational properties of FRB 180924-like and FRB 180916.J0158+65-like FRBs within a large range in local environments and host galaxies.
Exploration-efficient Deep Reinforcement Learning with Demonstration Guidance for Robot Control

Although deep reinforcement learning (DRL) algorithms have made important achievements in many control tasks, they still suffer from the problems of sample inefficiency and unstable training process, which are usually caused by sparse rewards. Recently, some reinforcement learning from demonstration (RLfD) methods have shown to be promising in overcoming these problems. However, they usually require considerable demonstrations. In order to tackle these challenges, on the basis of the SAC algorithm we propose a sample efficient DRL-EG (DRL with efficient guidance) algorithm, in which a discriminator D(s) and a guider G(s) are modeled by a small number of expert demonstrations. The discriminator will determine the appropriate guidance states and the guider will guide agents to better exploration in the training phase. Empirical evaluation results from several continuous control tasks verify the effectiveness and performance improvements of our method over other RL and RLfD counterparts. Experiments results also show that DRL-EG can help the agent to escape from a local optimum.
On Metric DBSCAN with Low Doubling Dimension

The density based clustering method Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method for outlier recognition and has received tremendous attention from many different areas. A major issue of the original DBSCAN is that the time complexity could be as large as quadratic. Most of existing DBSCAN algorithms focus on developing efficient index structures to speed up the procedure in low-dimensional Euclidean space. However, the research of DBSCAN in high-dimensional Euclidean space or general metric spaces is still quite limited, to the best of our knowledge. In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension. We apply a novel randomized k-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBSCAN procedure. Our proposed algorithms do not need to build any complicated data structures and are easy to implement in practice. The experimental results show that our algorithms can significantly outperform the existing DBSCAN algorithms in terms of running time.
Sigma-3: Integration and Analysis of a 6 DOF Robotic Arm Configuration in a Rescue Robot

This paper introduces a rescue robot named Sigma-3 which is developed for potential applications such as helping hands for humans where a human can't reach to have an assessment of the hazardous environment. Also, these kinds of robot can be controlled remotely with an adequate control system. The proposed methodology forces on two issues - 1. Novel mechanism design for measuring rotation, joints, links of Degree of Freedom (DOF) for an arm which is integrated with Sigma-3 2. Precise measuring of end-effector motion control over three dimensions. In the proposed mechanism design, the DOF measurement is presented by a planar and spatial mechanism where 4 types of rigid joints build up each DOF with controlling by six High Torque MG996R servo motors. Rotation and DOF measurement are consisting of different theoretical references of Rotation Matrix, Inverse Kinematics with experimental results. Presented methodology over Oscillation Damping performance exhibits less than 3% error while configuring for on hands testing. Another evaluation of operating time state strongly defends the mechanism of low power consumption ability.
Node Immunization with Non-backtracking Eigenvalues

The non-backtracking matrix and its eigenvalues have many applications in network science and graph mining, such as node and edge centrality, community detection, length spectrum theory, graph distance, and epidemic and percolation thresholds. Moreover, in network epidemiology, the reciprocal of the largest eigenvalue of the non-backtracking matrix is a good approximation for the epidemic threshold of certain network dynamics. In this work, we develop techniques that identify which nodes have the largest impact on the leading non-backtracking eigenvalue. We do so by studying the behavior of the spectrum of the non-backtracking matrix after a node is removed from the graph. From this analysis we derive two new centrality measures: X-degree and X-non-backtracking centrality. We perform extensive experimentation with targeted immunization strategies derived from these two centrality measures. Our spectral analysis and centrality measures can be broadly applied, and will be of interest to both theorists and practitioners alike.
Effective field theory in AdS: Continuum regime, soft bombs, and IR emergence

We consider an effective theory of interacting matter fields in a slice of five-dimensional Lorentzian AdS at arbitrary energies. Using dimensional and quantitative arguments, we determine there exists a transition scale at which the IR brane effectively leaves the theory. In the presence of bulk interactions there are thus two qualitatively different regimes: Kaluza-Klein and continuum. Bulk interactions correspond to finite N in the dual gauge theory; we find the transition scale is consistent with the cutoff scale of a large-N EFT of mesons. We then study the cascade decays of a scalar field with cubic self-interactions, with a focus on the continuum regime. We find that the cascade decay progresses slowly towards the IR region and gives rise to soft spherical final states, in accordance with former results from both gravity and CFT. We identify a recursion relation between integrated squared amplitudes of different leg numbers and thus evaluate the total rate. We find that cascade decays in the continuum regime are exponentially suppressed. This feature completes the picture of the IR brane as an emergent sector as seen from the UV brane. We briefly discuss some implications for holographic dark sector models.
The VMC survey – XXXVI. Young stellar variability in the Large Magellanic Cloud

Studies of young stellar objects (YSOs) in the Galaxy have found that a significant fraction exhibit photometric variability. However, no systematic investigation has been conducted on the variability of extragalactic YSOs. Here we present the first variability study of massive YSOs in a $\sim 1.5\,\mathrm{deg^2}$ region of the Large Magellanic Cloud (LMC). The aim is to investigate whether the different environmental conditions in the metal-poor LMC ($\sim$ 0.4-0.5 Z_sun) have an impact on the variability characteristics. Multi-epoch near-infrared (NIR) photometry was obtained from the VISTA Survey of the Magellanic Clouds (VMC) and our own monitoring campaign using the VISTA telescope. By applying a reduced $\chi^2$-analysis, stellar variability was identified. We found 3062 candidate variable stars from a population of 362 425 stars detected. Based on several Spitzer studies, we compiled a sample of high-reliability massive YSOs: a total of 173 massive YSOs have NIR counterparts ($K_{\mathrm{s}}\sim 18.5\,$mag) in the VMC catalogue, of which 39 display significant ($>3\sigma$) variability. They have been classified as eruptive, fader, dipper, short-term variable and long-period variable YSOs based mostly on the appearance of their $K_{\mathrm{s}}$ band light curves. The majority of YSOs are aperiodic, only five YSOs exhibit periodic lightcurves. The observed amplitudes are comparable or smaller than those for Galactic YSOs (only two Magellanic YSOs exhibit $\Delta K_{\mathrm{s}}>1\,$mag), not what would have been expected from the typically larger mass accretion rates observed in the Magellanic Clouds.
On Biased Stochastic Gradient Estimation

We present a uniform analysis of biased stochastic gradient methods for minimizing convex, strongly convex, and non-convex composite objectives, and identify settings where bias is useful in stochastic gradient estimation. The framework we present allows us to extend proximal support to biased algorithms, including SAG and SARAH, for the first time in the convex setting. We also use our framework to develop a new algorithm, Stochastic Average Recursive GradiEnt (SARGE), that achieves the oracle complexity lower-bound for non-convex, finite-sum objectives and requires strictly fewer calls to a stochastic gradient oracle per iteration than SVRG and SARAH. We support our theoretical results with numerical experiments that demonstrate the benefits of certain biased gradient estimators.
Further Perceptions of Probability: In Defence of Associative Models – A Commentary on Gallistel et al. 2014

Extensive research in the behavioural sciences has addressed people’s ability to learn stationary probabilities, which stay constant over time, but only recently have there been attempts to model the cognitive processes whereby people learn – and track – non-stationary probabilities. In this context, the old debate on whether learning occurs by gradual formation of associations or by occasional shifts between hypotheses representing beliefs about distal states of the world has resurfaced. Gallistel et al. (2014) pitched the two theories against each other in a non-stationary probability learning task. They concluded that various qualitative patterns in their data were incompatible with trial-by-trial associative learning and could only be explained by a hypothesis-testing model. Here, we contest that claim and demonstrate that it was premature. First, we argue that their experimental paradigm consisted of two distinct tasks: probability tracking (an estimation task) and change detection (a decision-making task). Next, we present a model that uses the (associative) delta learning rule for the probability tracking task and bounded evidence accumulation for the change-detection task. We find that this combination of two highly established theories accounts well for all qualitative phenomena and outperforms the alternative model proposed by Gallistel et al. in a quantitative model comparison. In the spirit of cumulative science, we conclude that current experimental data on human learning of non-stationary probabilities can be explained as a combination of associative learning and bounded evidence accumulation and does not require a new model.
Identifying gene-protein interactions in response to environmental estrogenic chemicals using Bayesian networks: its implication in the etiology of pulmonary vascular lesions

Despite the extensive research in the field of vascular proliferative disease, the complex interaction of genes with environmental factors with respect to its impact on this disease is not yet fully understood. To date, results from the literature show no direct studies of vascular gene-environment interactions. Vascular lesions consist of actively proliferating endothelial cells that can lead to a tissue mass that blocks the artery and increases arterial pressure. Severe vascular lesions consequentially lead to right ventricular heart failure and death. This outcome is approximately twice more common in women than in men, but whether it may be attributed to the hormone estrogen is not clear. Based on the limited evidence, it appears that estrogenic environmental chemicals play a role in vascular lesion formation. In this study, Bayesian Networks was used to study which genes are influenced by proteins that we previously identified to be modulated by exposure to the environmental estrogen PCB153 and natural estrogen 17β-estradiol. From the 96 spots obtained in our 2D-DIGE, we selected 13 spots for further protein identification analysis. The analyses identified 11 of the 13 spots of interest with high confidence protein score CI that was greater than 99%. The differentially expressed proteins regulated by PCB153 and estradiol were analyzed using IPA software to identify significant networks. The biological significance of TGFB1 network identified by IPA analysis coupled with the fact that some of the 11 identified proteins are involved in gene transcription lead us to use Bayesian networks to determine whether our identified proteins had any relation to our gene array data from similar estrogenic exposures. While our findings do not reveal any mechanisms involved in estrogen-induced vascular lesion formation, the identified relationships do provide a potential target pathway for future mechanistic studies.
Architectural Principles for Autonomous Microservices

In the last decade architectural patterns like microservices and event-driven architectures have emerged to meet the challenges of distributed computing. This paper explores recent practices and research in microserviceand event-driven architectures to identify the challenges in architecting such systems. With a proof-of-concept study approach, we distilled a set of design principles to address these challenges, creating asynchronous and agnostic microservice architectures. Further, we provide a generic toolkit for implementing such architectures. An example of this architecture was implemented in the company TechnipFMC. Concurrently, an architecture trade-off analysis was performed using a utility-tree based approach, highlighting the impact and importance of our proposed principles and their generalizability. The evaluation provides evidence for the viability of the proposed design principles.
Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance

This article presents a meta-framework for Artificial Intelligence (AI) regulation that encompasses all stages of international public policy-making, from formulation to sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2009 and 2019, a dispersed body of knowledge organized under the label “framework” was identified, containing 15 unique frameworks and several different theories that created a complex scientific scenario for research and practice. Theories and principles as diverse as Agile and Ethics were found. Thus, a structured analytical method was followed to integrate this bulk of knowledge into a cohesive, synthetic, and generic theoretical tool. The resulting “AIR framework” provides a trustworthy lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the novel framework organizes the latest developments in the area in a format that allows future research to be framed in and added to the published literature. The (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds in terms of terminology, governance, and social values.
How to Manage Crowdfunded Projects: Empirical Evidence from a Comparative Study of Game Development Projects

This study expands current understanding of the crowdfunding phenomenon by explaining how crowdfunding initiatives that have succeeded in hitting their financial target— referred to as crowdfunded projects— can continue to succeed by delivering their promise within the schedule, budget, and quality guidelines. The study develops propositions that elaborate the importance of undertaking four broad strategies—namely, project management, communication, community, and open innovation— during crowdfunded projects. It then reports the results of an in-depth, comparative qualitative study of two exemplary game development projects to evaluate the applicability of the propositions and create a better understanding of their underlying concepts. The findings confirm the propositions by demonstrating how the two projects differed greatly in the use of strategies. Furthermore, the exploratory nature of the comparisons enriches the propositions by revealing new concepts that need to be considered in the successful implementation of the identified strategies.
An e-ADR (elaborated Action Design Research) Approach Towards Game-based Learning in Cybersecurity Incident Detection and Handling

The growth of internet has significantly increased the cybersecurity threat instances. Therefore to equip people with skills to mitigate such attacks, this paper provides a Cybersecurity game-based learning artefact designed using the e-ADR approach. The artefact teaches the Incident Detection and Handling procedures that need to be undertaken in the event of a cybersecurity threat. As per NIST’s guide to malware incident prevention and handling, an incident response process has four major phases: preparation, detection and analysis, containment/eradication/recovery, and post-incident activity. Our gaming artefact delves into the detection and containment phase to design a game that teaches users to detect and then perform containment actions on the cybersecurity threat.
Minimizing Response Time of IoT-Based Traffic Information System through Decentralized Server System

Many metropolises seek to relieve traffic congestions and reduce vehicle accidents by implementing Intelligent Traffic Information Systems. These systems manage continuous communication between vehicles, various roadside IoT devices and a central server in real time for traffic control and vehicle navigations. Short response time is critical to the success of these time-sensitive systems. For a small area, a system with centralized server architecture may just work fine. For a larger area with more IoT devices and traffic, however, the system may experience excessive response time as a result of increased network distance and constrained server processing capacity. We propose a decentralized server system to properly manage and reduce service response time. We have also developed a binary nonlinear constrained programming model with Genetic Algorithm for a heuristic solution.
Using Natural Language Processing Techniques to Tackle the Construct Identity Problem in Information Systems Research

The growing number of constructs in behavioral research presents a problem to theory integration, since constructs cannot clearly be discriminated from each other. Recently there have been efforts to employ natural language processing techniques to tackle the construct identity problem. This paper compares the performance of the novel word-embedding model GloVe and different document projection methods with a latent semantic analysis (LSA) used in recent literature. The results show that making use of an advantage in document projection that LSA has over GloVe, performance can be improved. Even against this advantage of LSA, GloVe reaches comparable performance, and adjusted word embedding models can make up for this advantage. The proposed approach therefore presents a promising pathway for theory integration in behavioral research.