Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Real-Time FLIP Fluid Simulation through Machine Learning Approximations

Toward Real-Time FLIP Fluid Simulation Through Machine Learning Approximations Javid Kennon Pack Department of Computer Science, BYU Master of Science Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches.
Gold Nanoparticle Monolayers of Controlled Coverage and Structure

Deposition mechanism of gold nanoparticles on poly(allylamine chloride) modified gold sensor was investigated by the quartz crystal microbalance (QCM) and the scanning electron microscopy (SEM). The influence of the suspension concentration, flow intensity, ionic strength, and pH was systematically studied. It was shown that the deposition kinetics for lower coverage range was characterized by a linear increase of the coverage with the time. A general kinetic equation was formulated for calculating the particle deposition rates pertinent to the linear regime. Extensive measurements were also carried out for longer times at different ionic strengths and pHs. Negligible desorption of particles was observed that enabled one to precisely determine the maximum coverage of monolayers. These results were quantitatively interpreted in terms of the generalized random sequential adsorption (eRSA) model that considers the bulk and surface transfer steps in a rigorous way. It was proven, by comparing the QCM data wit.
Translational Formative Evaluation before Scale-up of a Physical Activity Intervention for Older Men

Despite irrefutable health benefits of physical activity, older adults remain among the least active Canadians. To achieve population health, physical activity interventions that proved effective in controlled research settings must be delivered at scale to reach broader populations of older adults across multiple settings. Formative evaluations are essential, as they identify barriers and enablers to implementation across levels of stakeholder groups and settings. Thus, we conducted a formative evaluation of a choice- and evidence-based physical activity intervention (Men on the Move) designed for scalability. We adopted key elements of two implementation frameworks that place characteristics of the innovation, prevention delivery system, prevention support system, and prevention synthesis and translation system at the core of implementation success. Guided by the Interactive Systems Framework for Dissemination and Implementation, data were collected from delivery partners, including 1 leader from a key provincial recreation organization, 6 recreation directors/coordinators and 3 activity coaches, and 14 participants (older men). This research team participated in prevention support and prevention synthesis and translation systems. Two trained interviewers conducted telephone interviews with delivery partners, and five trained interviewers and a notetaker conducted in-person interviews with participants. Five themes emerged from analyses of delivery partner interviews: support, activity coaches, intervention delivery, Men on the Move continuation, and the absence of men. Two themes emerged from our analyses of participant data: monitoring and connectedness. Lessons learned from this formative evaluation will guide the adaptation of the intervention to context and population for scale-up across British Columbia, Canada. In so doing, we aim to bridge the know–do–scale-up gap, which is imperative as we seek to improve older adult health at the population level.
Analysis of Bias in U . S . History Textbooks Using BERT

U.S. History textbooks have a profound influence on childrens’ social understanding of the United States. This is the reason activists and social scientists analyze textbooks for issues on bias and representation. Computational NLP methods can provide more holistic analyses compared to traditional qualitative studies. Our research supplements prior word2vec analyses of gender word relations in 15 U.S. History textbooks used in Texas by taking advantage of BERT’s versatility with two studies. First, we compare BERT’s embeddings between gender and interest words (related to home, work, and achievement). Second, we perform a gender word prediction study, where we mask out the gender word in each context containing interest words and evaluate BERT’s ability to predict the correct gender in different contexts. We repeat both studies on fine-tuned and pretrained BERT. Our analysis is done with all textbooks taken as a collective, as well as stratified by historical time period discussed. Overall, we find that the textbooks contain idiosyncrasies that tend to associate women with "home" and "work" contexts more strongly than "achievement" contexts and that these trends stay relatively constant over historical time periods discussed. 1 Key Information to include ¢ Mentors: Dorottya Demszky, John Hewitt ¢ External Collaborators (if you have any): N/A e Sharing project: N/A
Identifying malignant cells in effusion fluid is of paramount importance in staging and management of cancers. Differentiating reactive from malignant cases in effusion

IntroductIon Identifying malignant cells in effusion fluid is of paramount importance in staging and management of cancers. Differentiating reactive from malignant cases in effusion fluid is difficult at many times. We need a simple, inexpensive, and reliable tool to identify them. Micronucleus (MN) test is a simple test which is done on routine staining, thus avoiding the need of expensive methods and the use of immunocytochemistry panels and flow cytometry. [1-5]
The COVID-19 and Stock Return Volatility: Evidence from South Korea

This study examines the impact of the number of coronavirus cases on regime-switching in stock return volatility. This study documents the empirical evidence that the COVID-19 cases had an asymmetric effect on the regime of stock return volatility. When the stock return is in the low volatility regime, the probability of switching to the high volatility regime in the next trading day increases as the number of cumulative cases increases. In contrast, in the high volatility regime, the effect of cumulative cases on the transition probability is not statistically significant. This study also documents the evidence that the government measures against the pandemic contribute to promoting the high volatility regime of the KOSPI during the pandemic. Besides, this study projects future stock prices through the Monte Carlo simulation based on the estimated parameters and the predicted number of the COVID-19 new cases. Under a scenario where the number of new cases rapidly increases, stock price indices in Korea are expected to be in a downward trend over the next three months. On the other hand, under the moderate scenario and the best scenario, the stock indices are likely to continue to rise.
Information Systems Sourcing

Information Systems (IS) Sourcing is a broad umbrella term that refers to the contracting or delegating of IS- or IT-related work (e.g., an ongoing service or one-off project) to an internal or external entity (a supplier). It encompasses various sourcing models that are typically based on the distinction between ownership (in-house or third party) and location (domestic, nearshore or offshore), as well as online sourcing models. The significance of this topic for the IS discipline is evident in the number of publications that have addressed different aspects of IS sourcing since 1998 when the first two MISQ articles on this topic were published. We have identified 38 MISQ publications on the topic up to Spring 2020. A Special Issue of MISQ on IS offshoring published in 2008 was an important step in advancing research on the subject.
Tax Enforcement and Firm Investment Efficiency: Evidence from Staggered Implementation of Tax Administration Information System

This paper examines how increased tax enforcement accompanied by the new tax administration system impacts firm investment efficiency, exploiting the staggered implementation of the third phase of the Golden Tax Project (GTP III) in China. Consistent with the monitoring role of tax authorities (Desai et al., 2007), I find that firms improved their investment efficiency after the local tax office adopted the new system. This effect is more pronounced when tax authorities previously suffered from resource constraints, when the tax enforcement is further enhanced through local fiscal pressure, and when firms have a higher ex-ante propensity to avoid taxes. An analysis of the potential mechanisms of this investment efficiency effect reveals that increased tax enforcement serves as a substitute for corporate governance in that it has positive spillover effects on financial reporting quality and that investments are more responsive to market prices.
Sparking Curiosity or Tipping the Scales? Targeted Advertising to Rationally Inattentive Consumers

This paper argues, in the context of targeted advertising, that receivers' rational inattention and ability to independently acquire information have a non-trivial impact on the sender's optimal disclosure strategy. In our model, a monopolist has an opportunity to launch an advertising campaign and chooses a targeting strategy -- which consumers to send its advertisement to. The consumers are uncertain about and heterogeneous in their valuations of the product, and are rationally inattentive in that they must incur a cost if they want to learn their true valuations. We discover that the firm generally prefers to target consumers who are either indifferent between ignoring and investigating the product, or between investigating and buying it unconditionally. If the firm is uncertain about the consumer appeal of its product, it targets these two distinct groups of consumers simultaneously but may ignore all consumers in between.
Hairstyle Transfer between Portrait Images

This thesis proposes a compact solution for high-fidelity hairstyle transfer between portrait images. Given a hair image and a face image, our network produces an output image having the input hair and face seamlessly merged. The architecture consists of two encoders and a tiny mapping network that map the two inputs into the latent space of the pretrained StyleGAN2, which generates a high-quality image. The method needs neither annotated data nor an external dataset; the whole pipeline is trained using only synthetically generated images by the StyleGAN2. We demonstrate additional applications of the proposed framework, e.g., hairstyle manipulation and hair generation for 3D morphable model renderings. The extensive evaluation shows that our network is robust to various challenging conditions, where a head pose, face size, gender, ethnicity, and illumination differ between the inputs. The hairstyle transfer fidelity is assessed by a user study and using a trained hair similarity metric.
Natural Language Processing: My ”grandchild robot”

For many years, the communication competences of a robot have been oversimplified due to its complexity. Over the last few years, there were a couple of breakthroughs in the Machine Learning and Natural Language Processing areas that brought a lot of new possibilities to human-robot interactions. However, much of the research and data made in these fields are in English, with other languages being mostly overlooked. In order to apply these techniques to the Portuguese language, a solution suitable to the available databases is investigated and the differences between both languages, such as the plural and gender endings and the word stemming, are taken into consideration. Here, the robot communicates with humans in Portuguese and the discourse should work in a reactive and rapid manner, considering and inspecting previous dialogues to build a conversational model as natural as possible. Bearing this in mind, the Latent Semantic Analysis, a Natural Language Processing technique, is used intertwined with a Naive Bayes classifier to predict what the robot should respond based on the human utterance. The usage of a stop words list and a keyword extractor, like in the English research, are carefully inspected along with the system parameters to understand their influence on the final performance. A new approach to gather new Portuguese dialogues based on a form is also proposed since the quantity of data available is almost non-existent.
Higher-form symmetry breaking and holographic flavour

Higher-form symmetry breaking and holographic flavour Nabil Iqbal ∗ and Kieran Macfarlane † Centre for Particle Theory, Department of Mathematical Sciences, Durham University, South Road, Durham DH1 3LE, UK Abstract We study the realisation of higher-form symmetries in the holographic dual of gauge theories coupled to probe matter in the fundamental. We particularly focus on the dual of U(N) gauge theory coupled to fundamental matter. We demonstrate the existence of a continuous 1-form symmetry associated with the conservation of magnetic flux and show that this symmetry is spontaneously broken in the IR when the flavour degrees of freedom are gapped. We numerically compute the spectral function of the 2-form current and demonstrate the existence of the associated Goldstone mode. We compare to expectations at weak-coupling.
From dust to planets – I. Planetesimal and embryo formation

Planet formation models begin with proto-embryos and planetesimals already fully formed, missing out a crucial step, the formation of planetesimals/proto-embryos. In this work, we include prescriptions for planetesimal and proto-embryo formation arising from pebbles becoming trapped in short-lived pressure bumps, in thermally evolving viscous discs to examine the sizes and distributions of proto-embryos and planetesimals throughout the disc. We find that planetesimal sizes increase with orbital distance, from ∼10 km close to the star to hundreds of kilometres further away. Proto-embryo masses are also found to increase with orbital radius, ranging from $10^{-6}{\, {\rm M}_{\oplus }}$ around the iceline, to $10^{-3}{\, {\rm M}_{\oplus }}$ near the orbit of Pluto. We include prescriptions for pebble and planetesimal accretion to examine the masses that proto-embryos can attain. Close to the star, planetesimal accretion is efficient due to small planetesimals, whilst pebble accretion is efficient where pebble sizes are fragmentation limited, but inefficient when drift dominated due to low accretion rates before the pebble supply diminishes. Exterior to the iceline, planetesimal accretion becomes inefficient due to increasing planetesimal eccentricities, whilst pebble accretion becomes more efficient as the initial proto-embryo masses increase, allowing them to significantly grow before the pebble supply is depleted. Combining both scenarios allows for more massive proto-embryos at larger distances, since the accretion of planetesimals allows pebble accretion to become more efficient, allowing giant planet cores to form at distances upto $10{\, {\rm au}}$. By including more realistic initial proto-embryo and planetesimal sizes, as well as combined accretion scenarios, should allow for a more complete understanding in the beginning to end process of how planets and planetary systems form.
Formal verification of octorotor flight envelope using barrier functions and SMT solving

This paper introduces an approach for formally verifying the safety of the flight controller of an octorotor platform. Our method involves finding regions of the octorotor's state space that are considered safe, and which can be proven to be invariant with respect to the dynamics. Specifically, exponential barrier functions are used to construct candidate invariant regions near desired commanded states. The proof that these regions are invariant is discovered automatically using the dReal SMT solver, which ensures the accurate command tracking of the octorotor to within a certain margin of error. Rotor failures in which rotor thrusts become stuck at fixed values are considered and accounted for via a pseudo-inverse control allocator. The safety of the control allocator is verified in dReal by checking that the thrusts demanded by the allocator never exceed the capability of the rotors. We apply our approach on a specific octorotor example and verify the desired command tracking properties of the controller under normal conditions and various combinations of rotor failures.
Audiovisual Singing Voice Separation

Separating a song into vocal and accompaniment components is an active research topic, and recent years witnessed an increased performance from supervised training using deep learning techniques. We propose to apply the visual information corresponding to the singers' vocal activities to further improve the quality of the separated vocal signals. The video frontend model takes the input of mouth movement and fuses it into the feature embeddings of an audio-based separation framework. To facilitate the network to learn audiovisual correlation of singing activities, we add extra vocal signals irrelevant to the mouth movement to the audio mixture during training. We create two audiovisual singing performance datasets for training and evaluation, respectively, one curated from audition recordings on the Internet, and the other recorded in house. The proposed method outperforms audio-based methods in terms of separation quality on most test recordings. This advantage is especially pronounced when there are backing vocals in the accompaniment, which poses a great challenge for audio-only methods.
Graph-based machine learning beyond stable materials and relaxed crystal structures

There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the Crystal Graph Convolutional Neural Network (CGCNN) on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima. We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram. Models pre-trained on stable materials do not significantly improve performance, but models trained on similar data transfer very well. We demonstrate how our findings can be utilized to generate phase diagrams with a major reduction in computational effort.
General heavy-flavor mass scheme for charged-current DIS at NNLO and beyond

Incompleteness in current knowledge of neutrino interactions with nuclear matter imposes a pri-mary limitation in searches for leptonic CP violation carried out at long-baseline neutrino experiments. In this paper, we present a new computation that elevates the theoretical accuracy to next-to-next-to-leading order (NNLO) in QCD for charged-current deeply-inelastic scattering (DIS) processes relevant for ongoing and future neutrino programs. Mass-dependent quark contributions are consistently included across a wide range of momentum transfers in the SACOT- χ general-mass scheme. When appropriate, we further include N 3 LO corrections in the zero-mass scheme. We show theoretical predictions for several experiments with neutrinos over a wide range of energies and at the upcoming Electron-Ion Collider. Our prediction reduces perturbative uncertainties to ∼ 1%, suﬃcient for the high-precision objectives of future charged-current DIS measurements, and provides important theoretical inputs to experimental studies of leptonic mixing and CP violations.
Resilient and Latency-Aware Orchestration of Network Slices Using Multi-Connectivity in MEC-Enabled 5G Networks

Network slicing and multi-access edge computing (MEC) are new paradigms which play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide the available network resources into multiple logical network slices (NSs) for providing dedicated virtual networks tailored to the specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services for supporting the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber attacks, power outage, and natural/man-made disaster. Failure of a critical network component disrupts services abruptly and leads to users’ dissatisfaction, which may result in revenue loss for the NOs. In this paper, we present a novel approach based on multi-connectivity in 5G networks to tackle this problem and our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility in regional level. To this end, we formulate the problem as a binary integer programming (BIP) model in order to optimally deploy NSs with the minimum cost, and prove it is NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we propose an efficient genetic algorithm based heuristic to obtain near-optimal solution in polynomial time. By extensive simulations, we show that our proposed approach not only reduces resource wastage, but also improves throughput while providing high resiliency against failures.
Self-consistent state and measurement tomography with fewer measurements

We describe a technique for self consistently characterizing both the quantum state of a singlequbit system, and the positive-operator-valued measure (POVM) that describes measurements on the system. The method works with only ten measurements. We assume that a series of unitary transformations performed on the quantum state are fully known, while making minimal assumptions about both the density operator of the state and the POVM. The technique returns maximum-likely estimates of both the density operator and the POVM. To experimentally demonstrate the method, we perform reconstructions of over 300 state-measurement pairs and compare them to their expected density operators and POVMs. We find that 95% of the reconstructed POVMs have fidelities of 0.98 or greater, and 92% of the density operators have fidelities that are 0.98 or greater.