Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches

In the era of Agile methodologies, organizations are exploring strategies to scale development across teams. Various scaling strategies have emerged, from"SAFe"to"LeSS", with some organizations creating their own methods. Despite numerous studies on organizational challenges with these approaches, none have empirically compared their impact on Agile team effectiveness. This study aims to evaluate the effectiveness of Agile teams using different scaling methods, focusing on factors like responsiveness, stakeholder satisfaction, and management approach. We surveyed 15,078 Agile team members and 1,841 stakeholders, followed by statistical analyses. The results showed minor differences in effectiveness across scaling strategies. In essence, the choice of scaling strategy does not significantly impact team effectiveness, and organizations should select based on their culture and management style.
Breakup of particle-laden droplets in airflow

Abstract The atomisation of a suspension containing liquid and dispersed particles is prevalent in many applications. Previous studies of droplet breakup mainly focused on homogeneous fluids, and the heterogeneous effect of particles on the breakup progress is unclear. In this study, the breakup of particle-laden droplets in airflow is investigated experimentally. Combining synchronised high-speed images from the side view and the 45$^\circ$ view, we compare the morphology of particle-laden droplets with that of homogeneous fluids in different breakup modes. The results show that the higher effective viscosity of particle-laden droplets affects the initial deformation, and the heterogeneous effect of particles appears in the later breakup stage. To evaluate the heterogeneous effect of particles quantitatively, we eliminate the effect of the higher effective viscosity of particle-laden droplets by comparing cases corresponding to the same inviscid Weber number. The quantitative comparison reveals that the heterogeneous effect of particles accelerates the fragmentation of the liquid film and promotes localised rapid piercing. A correlation length that depends on the particle diameter and the volume fraction is proposed to characterise the length scale of the concentration fluctuation under the combined effect of the initial flattening and later stretching during the droplet breakup process. Based on this correlation length, the fragment size distributions are analysed, and the scaling results agree well with the experimental data.
Martin-Löf à la Coq

We present an extensive mechanization of the metatheory of Martin-Löf Type Theory (MLTT) in the Coq proof assistant. Our development builds on pre-existing work in Agda to show not only the decidability of conversion, but also the decidability of type checking, using an approach guided by bidirectional type checking. From our proof of decidability, we obtain a certified and executable type checker for a full-fledged version of MLTT with support for Π, Σ, ℕ, and Id types, and one universe. Our development does not rely on impredicativity, induction-recursion or any axiom beyond MLTT extended with indexed inductive types and a handful of predicative universes, thus narrowing the gap between the object theory and the metatheory to a mere difference in universes. Furthermore, our formalization choices are geared towards a modular development that relies on Coq's features, e.g. universe polymorphism and metaprogramming with tactics.
The carbon cycle and the value of forests as a carbon sink: a tropical case study

The UN Framework Convention on Climate Change recognises that, in the formulation of a global strategy for reducing global emissions of carbon (the main factor in global warming) forests could play an important role. This book highlights that role and demonstrates how the forests of the world may be harvested judiciously and sustainably. The authors argue that the forests are more than just a source of timber and wood; they discuss the role that forests play in reducing global warming, in preventing soil erosion and in helping to minimise the loss of biodiversity. Drawing on the expertise of contributors associated with the analysis of forests, this book is an in depth and fascinating discussion as well as a policy guide for the sustainable management of forests.
Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control

Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential decision making in reinforcement learning, games, and control. Guaranteeing the global optimality of policy gradient methods, however, is highly nontrivial due to nonconcavity of the value functions. In this exposition, we highlight recent progresses in understanding and developing policy gradient methods with global convergence guarantees, putting an emphasis on their finite-time convergence rates with regard to salient problem parameters.
Vojta's conjecture on weighted projective varieties

We state Vojta's conjecture for smooth weighted projective varieties, weighted multiplier ideal sheaves, and weighted log pairs and prove that all three versions of the conjecture are equivalent. Moreover, we introduce generalized weighted general common divisors and express them as heights of weighted projective spaces blown-up at a point, relative to an exceptional divisor. Furthermore, we also prove that assuming Vojta's conjecture for weighted projective varieties one can bound the $\log {\rm h_{wgcd}}$ for any subvariety of codimension $\geq 2$ and a finite set of places $S$. An analogue result is proved for weighted homogeneous polynomials with integer coefficients. As an application of our results we obtain a bound on greatest common divisors, which restricted to projective space is the same as bounds obtained by Corvaja, Zannier, et al.
TiC: Exploring Vision Transformer in Convolution

While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks. For instance, the Segment Anything Model (SAM) based on ViT-Huge requires all input images to be resized to 1024$\times$1024. To overcome this limitation, we propose the Multi-Head Self-Attention Convolution (MSA-Conv) that incorporates Self-Attention within generalized convolutions, including standard, dilated, and depthwise ones. Enabling transformers to handle images of varying sizes without retraining or rescaling, the use of MSA-Conv further reduces computational costs compared to global attention in ViT, which grows costly as image size increases. Later, we present the Vision Transformer in Convolution (TiC) as a proof of concept for image classification with MSA-Conv, where two capacity enhancing strategies, namely Multi-Directional Cyclic Shifted Mechanism and Inter-Pooling Mechanism, have been proposed, through establishing long-distance connections between tokens and enlarging the effective receptive field. Extensive experiments have been carried out to validate the overall effectiveness of TiC. Additionally, ablation studies confirm the performance improvement made by MSA-Conv and the two capacity enhancing strategies separately. Note that our proposal aims at studying an alternative to the global attention used in ViT, while MSA-Conv meets our goal by making TiC comparable to state-of-the-art on ImageNet-1K. Code will be released at https://github.com/zs670980918/MSA-Conv.
The rotating excitons in two-dimensional materials: Valley Zeeman effect and chirality

We propose the rotational dynamics of the intralayer and interlayer excitons with their inherent momenta of inertia in the monolayer and bilayer transition metal dichalcogenides, respectively, where the new chirality of exciton is endowed by the rotational angular momentum, namely, the formations of left- and right-handed excitons at the +K and -K valleys, respectively. We find that angular momenta exchange between excitons and its surrounding phononic bath result in the large fluctuation of the effective g-factor and the asymmetry of valley Zeeman splitting observed in most recently experiments, both of which sensitively depend on the magnetic moments provided by the phononic environment. This rotating exciton model not only proposes a new controllable knob in valleytronics, but opens the door to explore the angular momentum exchange of the chiral quasiparticles with the many-body environment.
Observations of Multiphase, High-velocity, Shocked Gas in the Vela Supernova Remnant

We present an analysis of high-resolution far-UV archival spectra obtained with the Space Telescope Imaging Spectrograph on the Hubble Space Telescope of the star HD 75309, which probes high-velocity shocked gas in the Vela supernova remnant (SNR). We examine high-velocity features from intrinsically strong absorption lines of O i, Si ii, Si ii*, C ii, C ii*, and Si iii. We also detect high-velocity components in the N v doublet and compare these features to observations of high-velocity O vi absorption, available from archival Far Ultraviolet Spectroscopic Explorer data. Kinetic temperatures are derived from the observed fractional abundances of the various ions, while gas densities and thermal pressures are obtained from the relative populations in excited fine-structure levels of C ii and Si ii. Our results indicate that the highly ionized species at high velocity probe gas in a region immediately behind a shock driven into an interstellar cloud, while the lower-ionization species trace material further downstream in the cooling region of the postshock flow. Low-velocity N v and O vi absorption may trace gas in a conductive boundary layer between the unshocked portion of the cloud and the hot X-ray-emitting intercloud medium. Temporal variations in high-velocity Ca ii absorption features observed toward HD 75309 further confirm the highly inhomogeneous nature of the interstellar medium interacting with the Vela SNR.
Generative AI in the Construction Industry: Opportunities & Challenges

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI’s GPT, Google’s PaLM, and Meta’s Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI’s early stage adoption within the construction sector. Given GenAI’s unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors’ opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.
Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters

Vehicle manufacturers are racing to create autonomous navigation and steering control algorithms for their vehicles. These software are made to handle various real-life scenarios such as obstacle avoidance and lane maneuvering. There is some ongoing research to incorporate pothole avoidance into these autonomous systems. However, there is very little research on the effect of hitting a pothole on the autonomous navigation software that uses cameras to make driving decisions. Perturbations in the camera angle when hitting a pothole can cause errors in the predicted steering angle. In this paper, we present a new model to compensate for such angle perturbations and reduce any errors in steering control prediction algorithms. We evaluate our model on perturbations of publicly available datasets and show our model can reduce the errors in the estimated steering angle from perturbed images to 2.3%, making autonomous steering control robust against the dash cam image angle perturbations induced when one wheel of a car goes over a pothole.
Reliable Test-Time Adaptation via Agreement-on-the-Line

Test-time adaptation (TTA) methods aim to improve robustness to distribution shifts by adapting models using unlabeled data from the shifted test distribution. However, there remain unresolved challenges that undermine the reliability of TTA, which include difficulties in evaluating TTA performance, miscalibration after TTA, and unreliable hyperparameter tuning for adaptation. In this work, we make a notable and surprising observation that TTAed models strongly show the agreement-on-the-line phenomenon (Baek et al., 2022) across a wide range of distribution shifts. We find such linear trends occur consistently in a wide range of models adapted with various hyperparameters, and persist in distributions where the phenomenon fails to hold in vanilla models (i.e., before adaptation). We leverage these observations to make TTA methods more reliable in three perspectives: (i) estimating OOD accuracy (without labeled data) to determine when TTA helps and when it hurts, (ii) calibrating TTAed models without label information, and (iii) reliably determining hyperparameters for TTA without any labeled validation data. Through extensive experiments, we demonstrate that various TTA methods can be precisely evaluated, both in terms of their improvements and degradations. Moreover, our proposed methods on unsupervised calibration and hyperparameters tuning for TTA achieve results close to the ones assuming access to ground-truth labels, in terms of both OOD accuracy and calibration error.
TransCC: Transformer Network for Coronary Artery CCTA Segmentation

The accurate segmentation of Coronary Computed Tomography Angiography (CCTA) images holds substantial clinical value for the early detection and treatment of Coronary Heart Disease (CHD). The Transformer, utilizing a self-attention mechanism, has demonstrated commendable performance in the realm of medical image processing. However, challenges persist in coronary segmentation tasks due to (1) the damage to target local structures caused by fixed-size image patch embedding, and (2) the critical role of both global and local features in medical image segmentation tasks.To address these challenges, we propose a deep learning framework, TransCC, that effectively amalgamates the Transformer and convolutional neural networks for CCTA segmentation. Firstly, we introduce a Feature Interaction Extraction (FIE) module designed to capture the characteristics of image patches, thereby circumventing the loss of semantic information inherent in the original method. Secondly, we devise a Multilayer Enhanced Perceptron (MEP) to augment attention to local information within spatial dimensions, serving as a complement to the self-attention mechanism. Experimental results indicate that TransCC outperforms existing methods in segmentation performance, boasting an average Dice coefficient of 0.730 and an average Intersection over Union (IoU) of 0.582. These results underscore the effectiveness of TransCC in CCTA image segmentation.
A decomposition of light's spin angular momentum density

Light carries intrinsic spin angular momentum (SAM) when the electric or magnetic field vector rotates over time. A familiar vector equation calculates the direction of light's SAM density using the right hand rule with reference to the electric and magnetic polarisation ellipses. Using Maxwell's equations, this vector equation can be decomposed into a sum of two distinct terms, akin to the well-known Poynting vector decomposition into orbital and spin currents. We present the first general study of this spin decomposition, showing that the two terms, which we call canonical and Poynting spin, are chiral analogies to the canonical and spin momenta of light in its interaction with matter. Both canonical and Poynting spin incorporate spatial variation of the electric and magnetic fields and are influenced by optical orbital angular momentum (OAM). The decomposition allows us to show that the OAM of a linearly polarised vortex beam can impart a first-order preferential force to chiral matter in the absence of spin.
Filter Pruning For CNN With Enhanced Linear Representation Redundancy

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different feature maps in the same layer, named CCM-loss. This loss term can encourage the neural network to learn stronger linear representation relations between feature maps during the training from the scratch so that more homogenous parts can be removed later in pruning. CCM-loss provides us with another universal transcendental mathematical tool besides L*-norm regularization, which concentrates on generating zeros, to generate more redundancy but for the different genres. Furthermore, we design a matching channel selection strategy based on principal components analysis to exploit the maximum potential ability of CCM-loss. In our new strategy, we mainly focus on the consistency and integrality of the information flow in the network. Instead of empirically hard-code the retain ratio for each layer, our channel selection strategy can dynamically adjust each layer's retain ratio according to the specific circumstance of a per-trained model to push the prune ratio to the limit. Notably, on the Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only 1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet dataset, our approach achieves 42.8% and 47.3% storage and computation reductions, respectively, with an accuracy of 76.23%. Our code is available at https://github.com/Bojue-Wang/CCM-LRR.
Trustworthy Formal Natural Language Specifications

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants to formally verify the correctness of software with respect to a natural language specification. The translation from informal to formal remains a challenging, time-consuming process that is difficult to audit for correctness. This paper shows that it is possible to build support for specifications written in expressive subsets of natural language, within existing proof assistants, consistent with the principles used to establish trust and auditability in proof assistants themselves. We implement a means to provide specifications in a modularly extensible formal subset of English, and have them automatically translated into formal claims, entirely within the Lean proof assistant. Our approach is extensible (placing no permanent restrictions on grammatical structure), modular (allowing information about new words to be distributed alongside libraries), and produces proof certificates explaining how each word was interpreted and how the sentence's structure was used to compute the meaning. We apply our prototype to the translation of various English descriptions of formal specifications from a popular textbook into Lean formalizations; all can be translated correctly with a modest lexicon with only minor modifications related to lexicon size.
Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30M ⊙. On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.
Deep Model Predictive Optimization

A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement learning (MFRL), which is incredibly flexible and general but often results in brittle policies. In contrast, model predictive control (MPC) continually re-plans at each time step to remain robust to perturbations and model inaccuracies. However, despite its real-world successes, MPC often under-performs the optimal strategy. This is due to model quality, myopic behavior from short planning horizons, and approximations due to computational constraints. And even with a perfect model and enough compute, MPC can get stuck in bad local optima, depending heavily on the quality of the optimization algorithm. To this end, we propose Deep Model Predictive Optimization (DMPO), which learns the inner-loop of an MPC optimization algorithm directly via experience, specifically tailored to the needs of the control problem. We evaluate DMPO on a real quadrotor agile trajectory tracking task, on which it improves performance over a baseline MPC algorithm for a given computational budget. It can outperform the best MPC algorithm by up to 27% with fewer samples and an end-to-end policy trained with MFRL by 19%. Moreover, because DMPO requires fewer samples, it can also achieve these benefits with 4.3X less memory. When we subject the quadrotor to turbulent wind fields with an attached drag plate, DMPO can adapt zero-shot while still outperforming all baselines. Additional results can be found at https://tinyurl.com/mr2ywmnw.