Miracle-dz/UniFAR-FTUs huggingface dataset:
On the origin of red spirals: does assembly bias play a role?

The formation of the red spirals is a puzzling issue in the standard picture of galaxy formation and evolution. Most studies attribute the colour of the red spirals to different environmental effects. We analyze a volume limited sample from the SDSS to study the roles of small-scale and large-scale environments on the colour of spiral galaxies. We compare the star formation rate, stellar age and stellar mass distributions of the red and blue spirals and find statistically significant differences between them at 99.9% confidence level. The red spirals inhabit significantly denser regions than the blue spirals, explaining some of the observed differences in their physical properties. However, the differences persist in all types of environments, indicating that the local density alone is not sufficient to explain the origin of the red spirals. Using an information theoretic framework, we find a small but non-zero mutual information between the colour of spiral galaxies and their large-scale environment that are statistically significant (99.9% confidence level) throughout the entire length scale probed. Such correlations between the colour and the large-scale environment of spiral galaxies may result from the assembly bias. Thus both the local environment and the assembly bias may play essential roles in forming the red spirals. The spiral galaxies may have different assembly history across all types of environments. We propose a picture where the differences in the assembly history may produce spiral galaxies with different cold gas content. Such a difference would make some spirals more susceptible to quenching. In all environments, the spirals with high cold gas content could delay the quenching and maintain a blue colour, whereas the spirals with low cold gas fractions would be easily quenched and become red.
A New Compendium of Soil Respiration Data for Africa

The objective of this paper is to present to the scientific community a new dataset derived from existing literature on soil respiration in Africa. The data has thus been obtained by searching for records in peer review papers and grey literature. The main search engines used are: Scientific Citation Index (SCI) database, ISI Science web and Google scholar. This data description paper has greatly advanced the number of data points on soil respiration in Africa from 4 in 2010 to 62 in 2014. The new data points are culled from 47 peer review publications and grey literature reports. The data lends its self to a lot of possible analytical methods such as correlation analysis, multiple linear regressions, artificial neural network analysis and process base modeling. The overall conclusion that can be drawn here is that this paper has greatly advanced the availability of soil respiration data in Africa by presenting all the available records that before now were only reported in different studies.
Three-dimensional, rotational flamelet closure model with two-way coupling

Abstract A new flamelet model is developed to be used for subgrid modelling and coupled with the resolved flow description for turbulent combustion. The model differs from current models in several critical ways. (i) Non-premixed flames, premixed flames or multi-branched flame structures are determined rather than prescribed. (ii) The effects of shear strain and vorticity on the flames are determined. (iii) The strain rates and vorticity applied at the subgrid level are directly determined from the resolved-scale strain rates and vorticity without the use of a contrived progress variable. (iv) The flamelet model is three-dimensional without need for assuming axisymmetry or planar geometry. (v) The effect of variable density is addressed in the flamelet model. Solutions to the Navier–Stokes equations and the associated scalar equations governing the flamelet model are obtained without the boundary-layer approximation. By appropriate coordinate transformation, a similar solution is found for the rotational flamelet model, reducing it to a system of ordinary differential equations. Vorticity is shown to create a centrifugal force on the subgrid counterflow that modifies the molecular transport rates and burning rate. Sample computations of the rotational flamelet model without coupling to the resolved flow are presented first to demonstrate the importance of the new features of the model. Scaling laws are presented for relating strain rates and vorticity at the subgrid level to those quantities at the resolved-flow level for coupling with large-eddy simulations or the time-averaged mean-flow level for Reynolds-averaged flows. The time-averaged behaviour of a simple turbulent flow is resolved with coupling to the rotational flamelet model. Specifically, a two-dimensional, multicomponent, time-averaged planar shear layer with variable density and energy release is employed using a mixing-length description for the eddy viscosity. Needs for future study are identified.
Excellence in service: an empirical study of the UAE banking sector

Purpose – The purpose of this paper is to examine the critical success factors of total quality management (TQM) implementation in the United Arab Emirates (UAE) banking sector.Design/methodology/approach – Empirical case studies were collected from 250 banks in UAE that have embarked on TQM successfully.Findings – Sixteen factors were found to be critical to TQM implementation success. The factors are top management support, strategy, continuous improvement, benchmarking, customer focus, quality department, quality system, human resource management, recognition and reward, problem analysis, quality service technologies, service design, employees, services capes, service culture and social responsibility.Research limitations/implications – The paper provides a framework for future research to explore organisational excellence in making TQM happen successfully.Originality/value – This research contributes to studies of TQM and service quality in the banking sector context by considering soft issues in its .
Private and polynomial time algorithms for learning Gaussians and beyond

We present a fairly general framework for reducing $(\varepsilon, \delta)$ differentially private (DP) statistical estimation to its non-private counterpart. As the main application of this framework, we give a polynomial time and $(\varepsilon,\delta)$-DP algorithm for learning (unrestricted) Gaussian distributions in $\mathbb{R}^d$. The sample complexity of our approach for learning the Gaussian up to total variation distance $\alpha$ is $\widetilde{O}(d^2/\alpha^2 + d^2\sqrt{\ln(1/\delta)}/\alpha \varepsilon + d\ln(1/\delta) / \alpha \varepsilon)$ matching (up to logarithmic factors) the best known information-theoretic (non-efficient) sample complexity upper bound due to Aden-Ali, Ashtiani, and Kamath (ALT'21). In an independent work, Kamath, Mouzakis, Singhal, Steinke, and Ullman (arXiv:2111.04609) proved a similar result using a different approach and with $O(d^{5/2})$ sample complexity dependence on $d$. As another application of our framework, we provide the first polynomial time $(\varepsilon, \delta)$-DP algorithm for robust learning of (unrestricted) Gaussians with sample complexity $\widetilde{O}(d^{3.5})$. In another independent work, Kothari, Manurangsi, and Velingker (arXiv:2112.03548) also provided a polynomial time $(\varepsilon, \delta)$-DP algorithm for robust learning of Gaussians with sample complexity $\widetilde{O}(d^8)$.
Streaming Instabilities in Accreting and Magnetized Laminar Protoplanetary Disks

The streaming instability (SI) is one of the most promising pathways to the formation of planetesimals from pebbles. Understanding how this instability operates under realistic conditions expected in protoplanetary disks (PPDs) is therefore crucial to assess the efficiency of planet formation. Contemporary models of PPDs show that magnetic fields are key to driving gas accretion through large-scale, laminar magnetic stresses. However, the effect of such magnetic fields on the SI has not been examined in detail. To this end, we study the stability of dusty, magneftized gas in a protoplanetary disk. We find the SI can be enhanced by passive magnetic torques and even persist in the absence of a global radial pressure gradient. In this case, instability is attributed to the azimuthal drift between dust and gas, unlike the classical SI, which is driven by radial drift. This suggests that the SI can remain effective inside dust-trapping pressure bumps in accreting disks. When a live vertical field is considered, we find the magneto-rotational instability can be damped by dust feedback, while the classic SI can be stabilized by magnetic perturbations. We also find that Alfvén waves can be destabilized by dust–gas drift, but this instability requires nearly ideal conditions. We discuss the possible implications of these results for dust dynamics and planetesimal formation in PPDs.
Deformation Robust Roto-Scale-Translation Equivariant CNNs

Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design. By producing features that are guaranteed to transform covariantly to the group actions on the inputs, group-equivariant convolutional neural networks (G-CNNs) achieve significantly improved generalization performance in learning tasks with intrinsic symmetry. General theory and practical implementation of G-CNNs have been studied for planar images under either rotation or scaling transformation, but only individually. We present, in this paper, a roto-scale-translation equivariant CNN (RST-CNN), that is guaranteed to achieve equivariance jointly over these three groups via coupled group convolutions. Moreover, as symmetry transformations in reality are rarely perfect and typically subject to input deformation, we provide a stability analysis of the equivariance of representation to input distortion, which motivates the truncated expansion of the convolutional filters under (pre-fixed) low-frequency spatial modes. The resulting model provably achieves deformation-robust RST equivariance, i.e., the RST symmetry is still"approximately"preserved when the transformation is"contaminated"by a nuisance data deformation, a property that is especially important for out-of-distribution generalization. Numerical experiments on MNIST, Fashion-MNIST, and STL-10 demonstrate that the proposed model yields remarkable gains over prior arts, especially in the small data regime where both rotation and scaling variations are present within the data.
Self-Supervised Class Incremental Learning

Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time, which discards data labels and the model's classifiers. To comprehensively discuss the difference in performance between supervised and self-supervised methods in CIL, we set up three different class incremental schemes: Random Class Scheme, Semantic Class Scheme, and Cluster Scheme, to simulate various class incremental learning scenarios. Besides, we propose Linear Evaluation Protocol (LEP) and Generalization Evaluation Protocol (GEP) to metric the model's representation classification ability and generalization in CIL. Our experiments (on ImageNet-100 and ImageNet) show that SSCIL has better anti-forgetting ability and robustness than supervised strategies in CIL. To understand what alleviates the catastrophic forgetting in SSCIL, we study the major components of SSCIL and conclude that (1) the composition of different data augmentation improves the quality of the model's representation and the \textit{Grayscale} operation reduces the system noise of data augmentation in SSCIL. (2) the projector, like a buffer, reduces unnecessary parameter updates of the model in SSCIL and increases the robustness of the model. Although the performance of SSCIL is significantly higher than supervised methods in CIL, there is still an apparent gap with joint learning. Our exploration gives a baseline of self-supervised class incremental learning on large-scale datasets and contributes some forward strategies for mitigating the catastrophic forgetting in CIL.
Finding the Winning Ticket of BERT for Binary Text Classification via Adaptive Layer Truncation before Fine-tuning

In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to ﬁnd the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classiﬁcation tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before ﬁne-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.
Density Ratio Estimation via Infinitesimal Classification

Density ratio estimation (DRE) is a fundamental machine learning technique for comparing two probability distributions. However, existing methods struggle in high-dimensional settings, as it is difficult to accurately compare probability distributions based on finite samples. In this work we propose DRE-\infty, a divide-and-conquer approach to reduce DRE to a series of easier subproblems. Inspired by Monte Carlo methods, we smoothly interpolate between the two distributions via an infinite continuum of intermediate bridge distributions. We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the"time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective. Crucially, the learned time scores can then be integrated to compute the desired density ratio. In addition, we show that traditional (Stein) scores can be used to obtain integration paths that connect regions of high density in both distributions, improving performance in practice. Empirically, we demonstrate that our approach performs well on downstream tasks such as mutual information estimation and energy-based modeling on complex, high-dimensional datasets.
Freeing Compute Caches from Serialization and Garbage Collection in Managed Big Data Analytics

Managed analytics frameworks (e.g., Spark) cache intermediate results in memory (on-heap) or storage devices (off-heap) to avoid costly recomputations, especially in graph processing. As datasets grow, on-heap caching requires more memory for long-lived objects, resulting in high garbage collection (GC) overhead. On the other hand, off-heap caching moves cached objects on the storage device, reducing GC overhead, but at the cost of serialization and deserialization (S/D). In this work, we propose TeraHeap , a novel approach for providing large analytics caches. TeraHeap uses two heaps within the JVM (1) a garbage-collected heap for ordinary Spark objects and (2) a large heap memory-mapped over fast storage devices for cached objects. TeraHeap eliminates both S/D and GC over cached data without imposing any language restrictions. We implement TeraHeap in Oracle’s Java runtime (OpenJDK-1.8). We use ﬁve popular, memory-intensive graph analytics workloads to understand S/D and GC overheads and evaluate TeraHeap . TeraHeap improves total execution time compared to state-of-the-art Apache Spark conﬁgurations by up to 72
Teaching Sustainability through Adventure

Adventure has been incorporated into sustainability education in a variety of ways, including through outdoor education and, more recently, through technology-enhanced learning. Technology has, for example, expanded opportunities for experiential learning through adventure as well as allowing learners to journey virtually along with explorers and scientists to the far- reaches of the world. This paper offers an overview of how adventure has traditionally been employed in both formal and informal education, discusses the differences between adventure education and adventure learning, shares research conducted on the role of adventure in the GoNorth! Adventure Learning Series, and advances suggestions for how adventure might be employed in sustainability education using distance, online, and mobile learning. The researchers propose the user-driven adventure learning environment (UDALE) as one model that educators and designers can draw from in both formal and informal learning settings as a means to fuse adventure, technology, and sustainability education in a pedagogically meaningful way.
Mesa: A Memory-saving Training Framework for Transformers

There has been an explosion of interest in designing high-performance Transformers. While Transformers have delivered significant performance improvements, training such networks is extremely memory intensive owing to storing all intermediate activations that are needed for gradient computation during backpropagation, especially for long sequences. To this end, we present Mesa, a memory-saving training framework for Transformers. Specifically, Mesa uses exact activations during forward pass while storing a low-precision version of activations to reduce memory consumption during training. The low-precision activations are then dequantized during back-propagation to compute gradients. Besides, to address the heterogeneous activation distributions in the multi-head self-attention layers, we propose a head-wise activation quantization strategy, which quantizes activations based on the statistics of each head to minimize the approximation error. To further boost training efficiency, we learn quantization parameters by running estimates. More importantly, by re-investing the saved memory in employing a larger batch size or scaling up model size, we may further improve the performance under constrained computational resources. Extensive experiments on ImageNet, CIFAR-100 and ADE20K demonstrate that Mesa can achieve flexible memory-savings (up to 50%) during training while achieving comparable or even better performance. Code is available at https://github.com/ziplab/Mesa.
Deep Spoken Keyword Spotting: An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS.
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection

Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student net-work by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong aug-mented input pair without losing label information. Espe-cially when extending SSL to semi-supervised object de-tection (SSOD), many strong augmentation methodologies related to image geometry and interpolation-regularization are hard to utilize since they possibly hurt the location information of the bounding box in the object detection task. To address this, we introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which un-mixes feature tiles for the mixed image tiles for the SSOD framework. Our proposed method makes mixed input image tiles and reconstructs them in the feature space. Thus, MUM can enjoy the interpolation-regularization effect from non-interpolated pseudo-labels and successfully generate a meaningful weak-strong pair. Furthermore, MUM can be easily equipped on top of various SSOD methods. Exten-sive experiments on MS-COCO and PASCAL VOC datasets demonstrate the superiority of MUM by consistently im-proving the mAP performance over the baseline in all the tested SSOD benchmark protocols. The code is released at https. //github.com/JongMokKim/mix-unmix.
On the Existence of Universal Lottery Tickets

The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Recent work has experimentally observed that some of these tickets can be practically reused across a variety of tasks, hinting at some form of universality. We formalize this concept and theoretically prove that not only do such universal tickets exist but they also do not require further training. Our proofs introduce a couple of technical innovations related to pruning for strong lottery tickets, including extensions of subset sum results and a strategy to leverage higher amounts of depth. Our explicit sparse constructions of universal function families might be of independent interest, as they highlight representational benefits induced by univariate convolutional architectures.
Revisiting Born’s Rule through Uhlhorn’s and Gleason’s Theorems

In a previous article we presented an argument to obtain (or rather infer) Born’s rule, based on a simple set of axioms named “Contexts, Systems and Modalities" (CSM). In this approach, there is no “emergence”, but the structure of quantum mechanics can be attributed to an interplay between the quantized number of modalities that is accessible to a quantum system and the continuum of contexts that are required to define these modalities. The strong link of this derivation with Gleason’s theorem was emphasized, with the argument that CSM provides a physical justification for Gleason’s hypotheses. Here, we extend this result by showing that an essential one among these hypotheses—the need of unitary transforms to relate different contexts—can be removed and is better seen as a necessary consequence of Uhlhorn’s theorem.