Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring supermassive black hole physics and galaxy quenching across halo mass in FIRE cosmological zoom simulations

Feedback from accreting supermassive black holes (SMBHs) is thought to be a primary driver of quenching in massive galaxies, but how to best implement SMBH physics into galaxy formation simulations remains ambiguous. As part of the Feedback in Realistic Environments (FIRE) project, we explore the effects of different modeling choices for SMBH accretion and feedback in a suite of ∼500 cosmological zoom-in simulations across a wide range of halo mass (1010 − 1013 M⊙). Within the suite, we vary the numerical schemes for BH accretion and feedback, accretion efficiency, and the strength of mechanical, radiative, and cosmic ray feedback independently. We then compare the outcomes to observed galaxy scaling relations. We find several models satisfying observational constraints for which the energetics in different feedback channels are physically plausible. Interestingly, cosmic rays accelerated by SMBHs play an important role in many plausible models. However, it is non-trivial to reproduce scaling relations across halo mass, and many model variations produce qualitatively incorrect results regardless of parameter choices. The growth of stellar and BH mass are closely related: for example, over-massive BHs tend to over-quench galaxies. BH mass is most strongly affected by the choice of accretion efficiency in high-mass halos, but by feedback efficiency in low-mass halos. The amount of star formation suppression by SMBH feedback in low-mass halos is determined primarily by the time-integrated feedback energy. For massive galaxies, the “responsiveness” of a model (how quickly and powerfully the BH responds to gas available for accretion) is an additional important factor for quenching.
Self-Promoted Supervision for Few-Shot Transformer

The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired. In this work, we empirically find that with the same few-shot learning frameworks, \eg~Meta-Baseline, replacing the widely used CNN feature extractor with a ViT model often severely impairs few-shot classification performance. Moreover, our empirical study shows that in the absence of inductive bias, ViTs often learn the low-qualified token dependencies under few-shot learning regime where only a few labeled training data are available, which largely contributes to the above performance degradation. To alleviate this issue, for the first time, we propose a simple yet effective few-shot training framework for ViTs, namely Self-promoted sUpervisioN (SUN). Specifically, besides the conventional global supervision for global semantic learning SUN further pretrains the ViT on the few-shot learning dataset and then uses it to generate individual location-specific supervision for guiding each patch token. This location-specific supervision tells the ViT which patch tokens are similar or dissimilar and thus accelerates token dependency learning. Moreover, it models the local semantics in each patch token to improve the object grounding and recognition capability which helps learn generalizable patterns. To improve the quality of location-specific supervision, we further propose two techniques:~1) background patch filtration to filtrate background patches out and assign them into an extra background class; and 2) spatial-consistent augmentation to introduce sufficient diversity for data augmentation while keeping the accuracy of the generated local supervisions. Experimental results show that SUN using ViTs significantly surpasses other few-shot learning frameworks with ViTs and is the first one that achieves higher performance than those CNN state-of-the-arts.
A Systematic Study and Analysis of Bengali Folklore with Natural Language Processing Systems

Folklore, a solid branch of folk literature, is the hallmark of any nation or any society. Such as oral tradition; as proverbs or jokes, it also includes material culture as well as traditional folk beliefs, and various customs. Bengali folklore is as rich in-depth as it is amazing. Nevertheless, in the womb of time, it is determined to sustain its existence. Therefore, our aim in this study is to make our rich folklore more comprehensible to everyone in a more sophisticated computational way. Some studies concluded various aspects of the Bengali language with NLP. Our proposed model is to be specific for Bengali folklore. Technically, it will be the first step towards Bengali natural language processing for studying and analyzing the folklore of Bengal.
Response to Intervention: Considerations for Research and Systems Change

This invited commentary on the seriesof articles regarding the state of the extantresearch and practice concerning the imple-mentation of response to intervention (RTI)will focus on the following issues: (a) whatconstitutes a comprehensive service deliverysystem that uses RTI as its driving principle,(b) the dependent measures by which the ef-fects of RTI should be evaluated, (c) howtreatment fidelity can be meaningfully facili-tated in real school environments, (d) what canbe done with students who are not responsiveto even robust interventions, and (e) whichstudents should be identified as having specificlearning disabilities. Throughout the discus-sion of these issues, implications for the prac-tice of using RTI in the schools and researchdirections will be presented.The Multitier Service Delivery ModelA worthwhile place to start is to attemptto offer some clarity about what is being pro-posed as the multitier service delivery modelthat uses RTI as its recurrent scheme to designinterventions, monitor progress, and makeboth small-scale and large-scale decisionsabout students. In many descriptions of RTI,both here (Glover & DiPerna, 2007) and else-where, it appears to be assumed that theprojects that began in the 1990s, includingIowa's problem-solving model (Tilly, 2003),Pennsylvania's instructional support teams(Kovaleski & Glew, 2006), Rosenfield andGravois's (1996) instructional consultationteams, and others are being presented as intactRTI models. However, although these precur-sors used problem solving and progress mon-itoring as core components of their models,none of them embodied all of the componentsof current conceptualizations of a multitierservice delivery system (e.g., Batsche et al.,2006). These pioneering approaches to inter-vention support models, which originated be-fore the advent of the National Reading Panel(2000), standard protocol interventions (Wan-zek & Vaughn, 2007), school-wide positivebehavior supports (Sugai & Homer, 2002), theDynamic Indicators of Basic Early LiteracySkills (DIBELS; Good & Kaminski, 2005),and other research-based initiatives, generallyassumed (or did not address) the effectivenessof the general education program and endeav-ored to customize interventions for individual
Strong to weak topological interacting phase transition of bosons on a lattice

We study hard-core bosons on the honeycomb lattice subjected to anisotropic nearest-neighbor hopping along with anisotropic nearest-neighbor repulsion, using a quantum Monte Carlo technique. At half-filling, we find a transition from strong interacting-topological order to weak interacting-topological order as function of the hopping anisotropy. The strong topological phase is characterized by a finite topological entanglement entropy, while the weak topological order is identified with a non-trivial value of the bipartite entanglement entropy. Some of the order parameters and their derivatives demonstrate abrupt changes when varying the parameters controlling the lattice anistropies, thus revealing the nature of this interacting-topological phase transition.
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations

While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. Due to their view-dependent and light-entangled volume representation, the 3D geometry presents unrealistic quality and the color should be re-rendered for every desired viewpoint. To broaden the 3D applicability from 3D-aware image generation to 3D-controllable object generation, we propose the factorized representations which are view-independent and light-disentangled, and training schemes with randomly sampled light conditions. We demonstrate the superiority of our method by visualizing factorized representations, re-lighted images, and albedo-textured meshes. In addition, we show that our approach improves the quality of the generated geometry via visualization and quantitative comparison. To the best of our knowledge, this is the first work that extracts albedo-textured meshes with unposed 2D images without any additional labels or assumptions.
Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

The introduction of imaging techniques in 1970 is one of the most relevant historical milestones in modern medicine. Medical imaging techniques have dramatically changed our understanding of the Human anatomy and physiology. The ability to non-invasively extract visual information allowed, not only the three-dimensional representation of the internal organs and musculo-skeletal system, but also the simulation of surgical procedures, the execution of computer aided surgeries, the development of more accurate biomechanical models, the development of custom-made implants, among others. The combination of the most advanced medical imaging systems with the most advanced CAD and CAM techniques, may allow the development of custom-made implants that meet patient-specific traits. The geometrical and functional optimization of these devices may increase implant life-expectancy, especially in patients with marked deviations from the anatomical standards. In the implant customization protocol from medical image data, there are several steps that need to be followed in a sequential way, namely: Medical Image Processing and Recovering; Accurate Image Segmentation and 3D Surface Model Generation; Geometrical Customization based on CAD and CAE techniques; FEA Optimization of the Implant Geometry; and Manufacturing using CAD-CAM Technologies. This work aims to develop the necessary procedures for custom implant development from medical image data. This includes the extraction of highly accurate three-dimensional representation of the musculo-skeletal system from the Computed Tomography imaging, and the development of customized implants, given the specific requirements of the target anatomy, and the applicable best practices found in the literature. A two-step segmentation protocol is proposed. In the first step the region of interest is pre-segmented in order to obtain a good approximation to the desired geometry.
Drift Reduced Navigation with Deep Explainable Features

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-of-the-art CARLA simulator indicate our method reduces drift up to 76.76% compared to benchmark approaches.
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes

Generative Adversarial Networks (GANs) learn an implicit generative model from data samples through a two-player game. In this paper, we study the existence of Nash equilibrium of the game which is consistent as the number of data samples grows to infinity. In a realizable setting where the goal is to estimate the ground-truth generator of a stationary Gaussian process, we show that the existence of consistent Nash equilibrium depends crucially on the choice of the discriminator family. The discriminator defined from second-order statistical moments can result in non-existence of Nash equilibrium, existence of consistent non-Nash equilibrium, or existence and uniqueness of consistent Nash equilibrium, depending on whether symmetry properties of the generator family are respected. We further study empirically the local stability and global convergence of gradient descent-ascent methods towards consistent equilibrium.
A smart electric bike for smart cities

There are a number of problems in the area of transportation in cities around the world today. Problems relating to air pollution, energy sustainability and energy security have initiated a widespread transition to implementing e-mobility solutions. Electric bikes have the potential to contribute to a number of transport solutions. The objective of this project is to research and implement different services that an electric bike can provide to help and protect people in cities. A commercially available electric bike is modified by equipping it with sensors to enable it to gather data from its surroundings. The electric bike is then connected to a smartphone which provides the electric bike with more data. It is demonstrated that these insights can be used to modify the behaviour of the electric bike in a responsive way. The modified electric bike has the potential to provide a number of different services in the areas of energy management, health connectivity, smart navigation and reducing the harmful effects of air pollution. In this project, a new method which reduces a cyclist’s inhalation of air pollutants while cycling through a polluted area is proposed and validated. This method relies heavily on an electric bike energy model that is derived here. The implementation is based on closed loop control which regulates the proportion of power that a cyclist is providing to move the wheel of the bike. It is shown that a specific cyclist’s breathing rate can be approximately halved while cycling through a polluted area by implementing this method. As such, the cyclist’s inhalation of harmful air pollutants will be reduced, which will have a positive impact on their health. This work was in part supported by SFI grant 11/PI/1177.
WFI J161953.3+031909: eclipsing ER UMa-type and Z Cam-type star in the period gap

WFI J161953.3+031909 was considered to be an eclipsing novalike object in the period gap. Using the Zwicky Transient Facility (ZTF) public database, I found that this object is the first eclipsing Z Cam star in the period gap, and is also most likely an ER UMa star with supercycles of 60–80 d. The longest outburst (most likely a superoutburst) comprised 35–46% of the supercycle in the extreme case. If superhumps are confirmed, this becomes the second object showing both ER UMa and Z Cam states after NY Ser. These objects have anomalously high mass-transfer rates despite that they are in the period gap. I refined the orbital period to be 0.099419808(8) d. We can expect to learn from WFI J161953.3+031909 using eclipses what is actually happening in the ER UMa-type and Z Cam-type disk. I also provide a revised classification of an eclipsing IW And star for BMAM-V383 = IPHAS J200822.55+300341.6 by detecting an IW And-type standstill.
Earthquake-induced displacement of cohesive-frictional slopes subject to cracks

The upper bound theorem of limit analysis together with Newmark's method are employed to evaluate the displacement of soil slopes subject to cracks. The pseudo static approach has been routinely used in the literature to estimate the seismic displacement of soil slopes. However, the effect of cracks on the slope displacement has yet to be tackled. In this paper, a new technique is proposed to estimate the horizontal displacement at the slope toe due to a given earthquake postulating rough estimation of real time crack formation. Rotational failure mechanisms for intact slopes exhibiting the formation of cracks as part of the failure process and the case of cracks which are pre-existing in the slope were considered. On the basis of Newmark's method, the seismic-induced displacement is calculated by incorporating a stepwise yield acceleration corresponding to the cracks occurring in the slope. Results of the proposed technique can reasonably bridge the gap between the conservatism of assuming the slopes subject to the most detrimental cracks, and the overestimation of slope stability resulted from the neglect of crack formation. An example illustrating the procedure for a given earthquake is presented. Also, charts providing the values needed to calculate the stepwise yield accelerations are proposed.
Optimizer Amalgamation

Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theoretical and empirical approaches; however, none can offer a universal advantage over other competitive optimizers. We are thus motivated to study a new problem named Optimizer Amalgamation: how can we best combine a pool of"teacher"optimizers into a single"student"optimizer that can have stronger problem-specific performance? In this paper, we draw inspiration from the field of"learning to optimize"to use a learnable amalgamation target. First, we define three differentiable amalgamation mechanisms to amalgamate a pool of analytical optimizers by gradient descent. Then, in order to reduce variance of the amalgamation process, we also explore methods to stabilize the amalgamation process by perturbing the amalgamation target. Finally, we present experiments showing the superiority of our amalgamated optimizer compared to its amalgamated components and learning to optimize baselines, and the efficacy of our variance reducing perturbations. Our code and pre-trained models are publicly available at http://github.com/VITA-Group/OptimizerAmalgamation.
An improved approach to manufacture CNT reinforced magnesium AZ91 composites with increased strength and ductility

Multiwalled carbon nanotubes (MWCNTs) are decorated with Pt nanoparticles by a ”layer-by-layer” approach using poly (sodium 4-styrene sulfonate) (PSS) and poly (diallyl dimethylammonium chloride) (PDDA). Transmission electron microscopy (TEM) images and Energy Dispersive X-Ray (EDX) analysis of the samples confirm Pt deposition on surfaces of CNTs. Dispersibility and dispersion stability of MWCNTs in the solvents are enhanced when MWCNTs are coated with Pt nanoparticles. Mg AZ91 composites reinforced with MWCNTs are then produced by a melt stirring process. Compression tests of the composites show that adding 0.05% wt Pt-coated MWCNTs in AZ91 improves the composite’s mechanical properties compared to the pure AZ91 and pristine MWCNT/AZ91. Fracture surface analysis of the composite using a scanning electron microscope (SEM) shows individuals pulled out MWCNTs in the case of the Pt-coated MWCNT/AZ91 composites. We attribute this finding to the uniform dispersion of Pt-coated MWCNTs in Mg due to the improved wettability of Pt-coated MWCNTs in Mg melts. Molecular dynamics (MD) simulations of the interaction between Pt-coated MWCNTs and Mg support this interpretation.
$R$ measurement and QCD studies at future super $\tau-c$ factory

We review status of $R$ measurement and QCD studies at low energy range, discuss prospects for a super $\tau-$charm factory in $2-7$ GeV. With a high-luminosity $e^+e^-$ collider, statistics are no longer problem for $R$ measurement and a precision of $2\%$ or even better is foreseen, that will lead to bring down the uncertainty of hadronic contribution to the QED running coupling constant $\Delta\alpha_{had}$ and the anomalous magnetic moment of the muon ($a_{\mu}$); measure the strong coupling constant $\alpha_s$ and the charm quark mass; improve the measurement of the resonance parameters of heavy charmonia. Huge data samples in $2-3$ GeV will make it possible to study excited states of $\rho$, $\omega$ and $\phi$, or exotic $Y(2175)$; measure electromagnetic form factor of mesons and baryons; and measure fragmentation functions of hadrons.
Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

This paper demonstrates how an efficient repre-sentation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up both the inference and training of a DNN-based motion planner. We build upon our recent work on learning local car maneuvers from past experience using a DNN architecture, introducing a novel B-spline path construction method, making it possible to generate local maneuvers in almost constant time of about 11 ms, respecting a number of constraints imposed by the environment map and the kinematics of a car-like vehicle. We evaluate thoroughly the new planner employing the recent Bench-MR framework to obtain quantitative results showing that our method outperforms state-of-the-art planners by a large margin in the considered task.
Rethinking Document-level Neural Machine Translation

This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words. We evaluate this model and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. Experiments show that document-level Transformer models outperforms sentence-level ones and many previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.
Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense

We study how to enhance text representation via textual commonsense. We point out that commonsense has the nature of domain discrepancy. Namely, commonsense has different data formats and is domain-independent from the downstream task. This nature brings challenges to introducing commonsense in general text understanding tasks. A typical method of introducing textual knowledge is continuing pre-training over the commonsense corpus. However, it will cause catastrophic forgetting to the downstream task due to the domain discrepancy. In addition, previous methods of directly using textual descriptions as extra input information cannot apply to large-scale commonsense.In this paper, we propose to use large-scale out-of-domain commonsense to enhance text representation. In order to effectively incorporate the commonsense, we proposed OK-Transformer (Out-of-domain Knowledge enhanced Transformer). OK-Transformer effectively integrates commonsense descriptions and enhances them to the target text representation. In addition, OK-Transformer can adapt to the Transformer-based language models (e.g. BERT, RoBERTa) for free, without pre-training on large-scale unsupervised corpora. We have verified the effectiveness of OK-Transformer in multiple applications such as commonsense reasoning, general text classification, and low-resource commonsense settings.