Miracle-dz/UniFAR-FTUs huggingface dataset:
Aeolian Vibration of a Conductor with a Stockbridge-type Damper

An iterative finite-difference scheme is derived to predict the vertical, steady-state. rnonofrequent, aeolian vibration of a single conductor van with a Stockbndge-type damper attached. This numerical scheme is based on empirical models developed to represent the vortex-induced lift force from the wind as well as the forces of dissipation associated with the conductor self-darnping and the damper. The scheme has the capability to account for more than one spatial mode of conductor vibration, travelling-wave effects, conductor flexural rigidity , and damper mass. Experimental data on the energy-dissipation characteristics of a sample Stockbridgetype damper are generated by means of the forced response method. Damper nonlinearities and inadvertent phase shifting are identified as important factors in the measurement of the energy dissipated by the damper. Furthemore. it is shown that the magnitude of the fundamental component of the damping force exerted by the Stockbridge-type darnper is a nonlinear function of not only the frequency of vibration, but also the displacement amplitude of the darnper clamp. A two-part numerical anaiysis is perfonned in which the aforementioned finitedifference scheme is applied to simulate the vertical, steady-state, monofrequent, aeolian vibrations of a typical conductor with and without the sample damper. The computed results are employed to investigate (a) the steady-state form ofconductor vibration, (b) the maximum conductor displacement and conductor bending amplitudes near each span end as a function of the vibration frequency and damper location, and (c) the influence of conductor flexural rigidity and damper mass. In addition, results from the finite-difference scheme are compared with solutions from the widely used energy balance method as well as experimenrai data on conductor vibrations, The author is deeply gratehil to Professor I.G. Currie for the privilege and benefit of his continuai support and guidance as well as kindness and generosity, and for his delightful wit. The author is indebted to Dr. D.G. Havard for sharing his expert knowledge and valuable resources, and for his thorough assessments of the thesis research. The author thanks Mr. Ernesto Moralo for his fabrication of the fixtures employed in the experimental study. The author thanks Mr. Miro KalovsS, for his preparation of the electronic instruments utilized in the experimental study. The author thanks Mr. Brian Chapnik for his help with the data acquisition software. The author is appreciative of the financial assistance provided in the form of scholarships from the Natural Sciences and Engineering Research Council of Canada as well as the University of Toronto. Page CHAPTER 1 BACKGROUND
Students’ Attitudes to Universal Design in Architecture Education

It is widely recognised that the built environment can dramatically impact on the participation and engagement of people with disability and diverse needs. It has therefore become necessary for architects and designers to consider these needs when working within their profession. The implementation of universal design teaching into architecture and design curriculum has been recognised as an important step in facilitating and enhancing the uptake of universal design during the design process. Using a quantitative approach, this study aimed to compare, contrast and explore the attitudes of two groups of architecture students to the universal design of built environments. One group had received education relating to diversity and universal design as part of a prior project while the other group had not received this content. Findings from this study demonstrated that while there were no significant differences between the two groups, interesting insights and perspectives emerged regarding student attitudes to universal design and the potential influencers of these attitudes.
Object-oriented Petri nets and π-calculus-based modeling and analysis of reconfigurable manufacturing systems

Reconfigurable manufacturing systems can change the structure of the systems to cope with manufacturing market requirements. Reconfigurability brings about new challenges for reconfigurable manufacturing systems’ development. In order to describe, analyze, and verify the reconfiguration of reconfigurable manufacturing systems, a reconfigurable manufacturing system formal model is proposed from the perspective of multi-agent systems, in which two complementary formalisms, namely, object-oriented Petri nets and π-calculus, are employed as formalisms. The object-oriented Petri nets are utilized to model the initial structure as well as system behaviors of reconfigurable manufacturing systems, while π-calculus is adopted to describe the reconfiguration of reconfigurable manufacturing systems. Some supporting tools of Petri nets and π-calculus can be used to analyze, verify, and validate the reconfigurable manufacturing system formal model. The reconfigurability mechanism and consistency of reconfigurable manufacturing systems can also be analyzed by π-calculus.
SemEval 2022 Task 12: Symlink - Linking Mathematical Symbols to their Descriptions

We describe Symlink, a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents. This is a new task in SemEval 2022, which attracted 180 individual registrations and 59 final submissions from 7 participant teams. We expect the data developed for this task and the findings reported to be valuable for the scientific knowledge extraction and automated knowledge base construction communities. The data used in this task is publicly accessible at https://github.com/nlp-oregon/symlink.
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification

Recent works have empirically shown the effectiveness of data augmentation (DA) in NLP tasks, especially for those suffering from data scarcity. Intuitively, given the size of generated data, their diversity and quality are crucial to the performance of targeted tasks. However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP. In this paper, we present an easy and plug-in data augmentation framework EPiDA to support effective text classification. EPiDA employs two mechanisms: relative entropy maximization (REM) and conditional entropy minimization (CEM) to control data generation, where REM is designed to enhance the diversity of augmented data while CEM is exploited to ensure their semantic consistency. EPiDA can support efficient and continuous data generation for effective classifier training. Extensive experiments show that EPiDA outperforms existing SOTA methods in most cases, though not using any agent networks or pre-trained generation networks, and it works well with various DA algorithms and classification models.
Preharvest Application of Harpin on the Cool Storage Life of Pepper

Effect of preharvest application of Harpin (a plant activator) and modified atmosphere packaging (MAP) on the fruit quality and storage life of pepper (Capsicum annuum L. cv. 'Demre') was investigated. Peppers grown in greenhouse were treated three times with Harpin, the first treatment starting when the plants were 15 days old and subsequent treatments at 14 day-intervals. The fruits were stored in the plastic film materials with various O 2 and CO 2 permeabilities. The fruits were stored in the cold room at 7°C and 90±5% relative humidity. Gas composition (O 2 , CO 2 and C 2 H 4 ) within two film package types (PP and PVC) and quality changes of the peppers were evaluated during the storage period. At the end of the study, Harpin treatment and 35 μm polypropylene (PP) packaging material together gave the best result for a 30 day-storage period.
Spherical Rotation Dimension Reduction with Geometric Loss Functions

Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis. A prime example of such a dataset is a collection of cell cycle measurements, where the inherently cyclical nature of the process can be represented as a circle or sphere. Motivated by the need to analyze these types of datasets, we propose a nonlinear dimension reduction method, Spherical Rotation Component Analysis (SRCA), that incorporates geometric information to better approximate low-dimensional manifolds. SRCA is a versatile method designed to work in both high-dimensional and small sample size settings. By employing spheres or ellipsoids, SRCA provides a low-rank spherical representation of the data with general theoretic guarantees, effectively retaining the geometric structure of the dataset during dimensionality reduction. A comprehensive simulation study, along with a successful application to human cell cycle data, further highlights the advantages of SRCA compared to state-of-the-art alternatives, demonstrating its superior performance in approximating the manifold while preserving inherent geometric structures.
Baxos: Backing off for Robust and Efficient Consensus

Leader-based consensus algorithms are vulnerable to liveness and performance downgrade attacks. We explore the possibility of replacing leader election in Multi-Paxos with random exponential backoff (REB), a simpler approach that requires minimum modifications to the two phase Synod Paxos and achieves better resiliency under attacks. We propose Baxos, a new resilient consensus protocol that leverages a random exponential backoff scheme as a replacement for leader election in consensus algorithms. Our backoff scheme addresses the common challenges of random exponential backoff such as scalability and robustness to changing wide area latency. We extensively evaluate Baxos to illustrate its performance and robustness against two liveness and performance downgrade attacks using an implementation running on Amazon EC2 in a wide area network and a combination of a micro benchmark and YCSB-A workload on Redis. Our results show that Baxos offers more robustness to liveness and performance downgrade attacks than leader-based consensus protocols. Baxos outperforms Multi-Paxos and Raft up to 185% in throughput under liveness and performance downgrade attacks under worst case contention scenarios where each replica proposes requests concurrently while only incurring a 7% reduction on the maximum throughput in the synchronous attack-free scenario.
Computing the Collection of Good Models for Rule Lists

Since the seminal paper by Breiman in 2001, who pointed out a potential harm of prediction multiplicities from the view of explainable AI, global analysis of a collection of all good models, also known as a `Rashomon set,' has been attracted much attention for the last years. Since finding such a set of good models is a hard computational problem, there have been only a few algorithms for the problem so far, most of which are either approximate or incomplete. To overcome this difficulty, we study efficient enumeration of all good models for a subclass of interpretable models, called rule lists. Based on a state-of-the-art optimal rule list learner, CORELS, proposed by Angelino et al. in 2017, we present an efficient enumeration algorithm CorelsEnum for exactly computing a set of all good models using polynomial space in input size, given a dataset and a error tolerance from an optimal model. By experiments with the COMPAS dataset on recidivism prediction, our algorithm CorelsEnum successfully enumerated all of several tens of thousands of good rule lists of length at most $\ell = 3$ in around 1,000 seconds, while a state-of-the-art top-$K$ rule list learner based on Lawler's method combined with CORELS, proposed by Hara and Ishihata in 2018, found only 40 models until the timeout of 6,000 seconds. For global analysis, we conducted experiments for characterizing the Rashomon set, and observed large diversity of models in predictive multiplicity and fairness of models.
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines

Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training --"Which training examples are 'guilty' in making the trained ML model predictions inaccurate or unfair? "Modeling data influence for ML training has attracted intensive interest over the last decade, and one popular framework is to compute the Shapley value of each training example with respect to utilities such as validation accuracy and fairness of the trained ML model. Unfortunately, despite recent intensive interest and research, existing methods only consider a single ML model"in isolation"and do not consider an end-to-end ML pipeline that consists of data transformations, feature extractors, and ML training. We present DataScope (ease.ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training. To this end, we first develop a novel algorithmic framework that computes Shapley value over a specific family of ML pipelines that we call canonical pipelines: a positive relational algebra query followed by a K-nearest-neighbor (KNN) classifier. We show that, for many subfamilies of canonical pipelines, computing Shapley value is in PTIME, contrasting the exponential complexity of computing Shapley value in general. We then put this to practice -- given an sklearn pipeline, we approximate it with a canonical pipeline to use as a proxy. We conduct extensive experiments illustrating different use cases and utilities. Our results show that DataScope is up to four orders of magnitude faster over state-of-the-art Monte Carlo-based methods, while being comparably, and often even more, effective in data debugging.
Learning and Inference in Sparse Coding Models With Langevin Dynamics

Abstract We describe a stochastic, dynamical system capable of inference and learning in a probabilistic latent variable model. The most challenging problem in such models—sampling the posterior distribution over latent variables—is proposed to be solved by harnessing natural sources of stochasticity inherent in electronic and neural systems. We demonstrate this idea for a sparse coding model by deriving a continuous-time equation for inferring its latent variables via Langevin dynamics. The model parameters are learned by simultaneously evolving according to another continuous-time equation, thus bypassing the need for digital accumulators or a global clock. Moreover, we show that Langevin dynamics lead to an efficient procedure for sampling from the posterior distribution in the L0 sparse regime, where latent variables are encouraged to be set to zero as opposed to having a small L1 norm. This allows the model to properly incorporate the notion of sparsity rather than having to resort to a relaxed version of sparsity to make optimization tractable. Simulations of the proposed dynamical system on both synthetic and natural image data sets demonstrate that the model is capable of probabilistically correct inference, enabling learning of the dictionary as well as parameters of the prior.
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning

Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. In contrast, literature on task transferability has established that the choice of intermediate tasks can heavily affect downstream task performance. In this work, we aim to disentangle the effect of scale and relatedness of tasks in multi-task representation learning. We find that, on average, increasing the scale of multi-task learning, in terms of the number of tasks, indeed results in better learned representations than smaller multi-task setups. However, if the target tasks are known ahead of time, then training on a smaller set of related tasks is competitive to the large-scale multi-task training at a reduced computational cost.
Riemannian Hamiltonian methods for min-max optimization on manifolds

In this paper, we study min-max optimization problems on Riemannian manifolds. We introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for solving the original min-max problems. Under the Riemannian Polyak--{\L}ojasiewicz condition on the Hamiltonian function, its minimizer corresponds to the desired min-max saddle point. We also provide cases where this condition is satisfied. For geodesic-bilinear optimization in particular, solving the proxy problem leads to the correct search direction towards global optimality, which becomes challenging with the min-max formulation. To minimize the Hamiltonian function, we propose Riemannian Hamiltonian methods (RHM) and present their convergence analyses. We extend RHM to include consensus regularization and to the stochastic setting. We illustrate the efficacy of the proposed RHM in applications such as subspace robust Wasserstein distance, robust training of neural networks, and generative adversarial networks.
3D intrinsic shapes of quiescent galaxies in observations and simulations

We study the intrinsic 3D shapes of quiescent galaxies over the last half of cosmic history based on their axial ratio distribution. To this end, we construct a sample of unprecedented size, exploiting multi-wavelength $u$-to-$K_s$ photometry from the deep wide area surveys KiDS+VIKING paired with high-quality $i$-band imaging from HSC-SSP. Dependencies of the shapes on mass, redshift, photometric bulge prominence and environment are considered. For comparison, the intrinsic shapes of quenched galaxies in the IllustrisTNG simulations are analyzed and contrasted to their formation history. We find that over the full $010^{11}\ M_{\odot}$, with the effect being most pronounced at lower redshifts. In TNG, the most massive galaxies feature the highest ex-situ stellar mass fractions, pointing to violent relaxation via mergers as the mechanism responsible for their 3D shape transformation. Larger differences between observed and simulated shapes are found at low to intermediate masses. At any mass, the most spheroidal quiescent galaxies in TNG feature the highest bulge mass fractions, and conversely observed quiescent galaxies with the highest bulge-to-total ratios are found to be intrinsically the roundest. Finally, we detect an environmental influence on galaxy shape, at least at the highest masses, such that at fixed mass and redshift quiescent galaxies tend to be rounder in denser environments.
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language models to label internal neurons with open-ended concepts without the need for any labeled data or human examples. We show that CLIP-Dissect provides more accurate descriptions than existing methods for last layer neurons where the ground-truth is available as well as qualitatively good descriptions for hidden layer neurons. In addition, our method is very flexible: it is model agnostic, can easily handle new concepts and can be extended to take advantage of better multimodal models in the future. Finally CLIP-Dissect is computationally efficient and can label all neurons from five layers of ResNet-50 in just 4 minutes, which is more than 10 times faster than existing methods. Our code is available at https://github.com/Trustworthy-ML-Lab/CLIP-dissect. Finally, crowdsourced user study results are available at Appendix B to further support the effectiveness of our method.
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

We explore the potential of CNN-based models for gall-bladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modality for GB diseases due to its low cost and accessibility. However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor. Our exhaustive study of state-of-the-art (SOTA) image classification techniques for the problem reveals that they often fail to learn the salient GB region due to the presence of shadows in the USG images. SOTA object detection techniques also achieve low accuracy because of spurious textures due to noise or adjacent organs. We propose GBCNet to tackle the challenges in our problem. GBCNet first extracts the regions of interest (ROIs) by detecting the GB (and not the cancer), and then uses a new multi-scale, second-order pooling architecture specializing in classifying GBC. To effectively handle spurious textures, we propose a curriculum inspired by human visual acuity, which reduces the texture biases in GBCNet. Experimental results demonstrate that GBC-Net significantly outperforms SOTA CNN models, as well as the expert radiologists. Our technical innovations are generic to other USG image analysis tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in detecting breast cancer from USG images. Project page with source code, trained models, and data is available at https://GBC-iitd.github. io/GBCnet.
Survey on the metric SYZ conjecture and non-archimedean geometry

We survey the metric aspects of the Strominger-Yau-Zaslow conjecture on the existence of special Lagrangian ﬁbrations on Calabi-Yau manifolds near the large complex structure limit. We will discuss the diverse motivations for the conjectural picture, what the best hopes are, and a number of subtleties. The bulk of the survey highlights the role of pluripotential theory, and non-archimedean geometry in particular, with a list of open questions.
Optimal Security Hardening over a Probabilistic Attack Graph: A Case Study of an Industrial Control System using CySecTool

CySecTool is a tool that finds a cost-optimal security controls portfolio in a given budget for a probabilistic attack graph. A portfolio is a set of counter-measures, or controls, against vulnerabilities adopted for a computer system, while an attack graph is a type of a threat scenario model. In an attack graph, nodes are privilege states of the attacker, edges are vulnerabilities escalating privileges, and controls reduce the probabilities of some vulnerabilities being exploited. The tool builds on an optimisation algorithm published by Khouzani et al., enabling a user to quickly create, edit, and incrementally improve models, analyse results for given portfolios and display the best solutions for all possible budgets in the form of a Pareto frontier. A case study was performed utilising a system graph and suspected attack paths prepared by industrial security engineers based on an industrial source with which they work. The goal of the case study is to model a supervisory control and data acquisition (SCADA) industrial system which, due to having a potential to harm people, necessitates strong protection while not allowing to use of typical penetration tools like vulnerability scanners. Results are analysed to show how a cyber-security analyst would use CySecTool to store cyber-security intelligence and draw further conclusions.