Miracle-dz/UniFAR-FTUs huggingface dataset:
National Aerospace Planning Process Enhancements: Analysis and Innovation

Abstract : New advanced decision support technology concepts have been developed to support Air Domain Awareness (ADA) and the National Aerospace Planning Process (NAPP). This report reviews and validates the NAPP requirements based on consultations with 1 Canadian Air Division, assesses relevant existing tools and technologies, tabulates promising research directions, and proposes a set of innovative improvements for implementation in a NAPP Enhancement Prototype NEP. Four ADA innovations, hosted in Google Earth, are proposed. These will enable NEP to better support visualization of sensor coverage, detect coverage gaps, visualize future weather, and analyse dynamic threats to vital points. New visual analytics tools for NEP are proposed that will reveal subtle long-term temporal, geospatial, and behaviouralpatterns for Resource Awareness and Total Air Resource Management (TARM). NEP will include novel tools for air force resource visibility and resource management, including tools for vertical awareness (down to the Wings and squadrons), and horizontal awareness (forward and backward in time). Asset availability awareness is described based on Dashboard and Magnets Grid visualizations. A Hockey Card metaphor encapsulates the key elements of each mission. To rapidly respond to un-forecast events, a resource management app scans existing Air Tasking Orders and proposes viable re-planning solutions based on: rapidity of response, ability to dwell if required, and the availability of an appropriate payload.This is the first of two reports. The second report documents the subsequent design and implementation of the NEP, and its demonstration to the Air Force.
Estratégias de produção e a relação com a prática de inovação tecnológica

A inovacao e considerada de grande importância para o segmento industrial brasileiro, organizacoes estas que estao inseridas em ambientes dinâmicos de competicao. Assim, observa-se uma relacao entre as praticas da inovacao e as estrategias organizacionais. Nesse contexto este estudo tem como objetivo analisar como a inovacao tecnologica, de produto e de processo insere-se nas estrategias de producao de uma industria de producao de aluminio no Rio Grande do Sul. Trata-se de uma pesquisa descritiva, exploratoria, com natureza qualitativa e como metodo utiliza o estudo de caso. A empresa em analise esta presente nos setores de energia, cimento, metais, siderurgia e celulose, porem a unidade selecionada restringe sua producao a aluminio. Os dados foram coletados atraves de entrevistas e observacoes. Os principais resultados demonstram uma maior ocorrencia de inovacao de processo do que produto, onde e priorizado a flexibilidade na planta e os custos. Nao se ve na empresa a inovacao como forma de aumento da competitividade, porem ela ocorre muitas vezes como forma de aumento da lucratividade. Nota-se que algumas caracteristicas relacionadas a tomada de decisao facilitam as praticas de inovacao, por exemplo a integracao vertical, porem as praticas organizacionais nao sao bem desenvolvidas para dar suporte a inovacao.
Transfer Learning for Robotics: Can a Robot Learn from Another Robot's Data?

Transfer Learning for Robotics: Can a Robot Learn from Another Robot’s Data? Kaizad V. Raimalwala Master of Applied Science Graduate Department of Aerospace Science and Engineering University of Toronto 2015 Machine learning methods have been used to improve the performance of control systems with experimental data when accurate system or environment models are unavailable. Transfer Learning (TL) allows for this data to come from another, similar system. A simplified TL scenario is studied to understand how the quality of an alignment-based transfer of data varies with the parameters of two linear, time-invariant (LTI), singleinput, single-output systems that are tasked to follow the same reference signal. A scalar, LTI transformation is used to align the output from a source system to the output from a target system. An upper bound on the 2-norm of the transformation error is derived and minimized with respect to the transformation scalar. This minimized bound is analyzed with respect to the system parameters to show when TL works best. TL is further studied for wheeled robots, with data from simulation and experiment to supplement theoretical analysis.
GRB Afterglow of the Sub-relativistic Materials with Energy Injection

Sub-relativistic materials launched during the merger of binary compact objects and the core collapse of massive stars acquire velocity structures when expanding in a stratified environment. The remnant (either a spinning magnetized neutron star (NS) or a central black hole) from the compact object or core collapse could additionally inject energy into the afterglow via spin-down luminosity or/and by accreting fallback material, producing a refreshed shock, modifying the dynamics, and leading to rich radiation signatures at distinct timescales and energy bands with contrasting intensities. We derive the synchrotron light curves evolving in a stratified environment when a power-law velocity distribution parameterizes the energy of the shock, and the remnant continuously injects energy into the blast wave. As the most relevant case, we describe the latest multiwavelength afterglow observations (≳900 days) of the GW170817/GRB 170817A event via a synchrotron afterglow model with energy injection of a sub-relativistic material. The features of the remnant and the synchrotron emission of the sub-relativistic material are consistent with a spinning magnetized NS and the faster blue kilonova afterglow, respectively. Using the multiband observations of some short bursts with evidence of kilonovae, we provide constraints on the expected afterglow emission.
Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research

We establish a dataset of over 1.6×104 experimental images of Bose–Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet—an implementation of a physics-informed ML data analysis framework—consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.
Multiplexing and its upper bound in biological sensory receptors

Biological sensory receptors provide perfect examples of microscopic scale information transduc-tion in the presence of non-negligible thermal ﬂuctuations. For example, studies of ligand-receptor reveal that accurate concentration sensing is achieved by integrating out noise in the sensor’s stochastic trajectories. However, we argue that the stochastic trajectory is not always an adversary – it could allow a single sensor to perform multiplexing (or muxing) by simultaneously transducing multiple environmental variables (e.g., concentration, temperature, and ﬂow speed) to the downstream sensory network. This work develops a general theory of stochastic sensory muxing and a theoretical upper bound of muxing. The theory is demonstrated and veriﬁed by an exactly solvable Markov dynamics model, where an arbitrary sensor can achieve the upper bound of muxing without optimizing parameters. The theory is further demonstrated by a realistic Langevin dynamics simulation of a ligand receptor within a bath of ligands. Simulations verify that even a binary state ligand receptor with short-term memory can simultaneously sense two out of three independent environmental variables – ligand concentration, temperature, and media’s ﬂow speed. Both models demonstrate that the upper bound for muxing is tight. This theory provides insights on designing novel microscopic sensors that are capable of muxing in realistic and complex environments.
Primrose: Selecting Container Data Types by their Properties

Context: Container data types are ubiquitous in computer programming, enabling developers to efficiently store and process collections of data with an easy-to-use programming interface. Many programming languages offer a variety of container implementations in their standard libraries based on data structures offering different capabilities and performance characteristics. Inquiry: Choosing the *best* container for an application is not always straightforward, as performance characteristics can change drastically in different scenarios, and as real-world performance is not always correlated to theoretical complexity. Approach: We present Primrose, a language-agnostic tool for selecting the best performing valid container implementation from a set of container data types that satisfy *properties* given by application developers. Primrose automatically selects the set of valid container implementations for which the *library specifications*, written by the developers of container libraries, satisfies the specified properties. Finally, Primrose ranks the valid library implementations based on their runtime performance. Knowledge: With Primrose, application developers can specify the expected behaviour of a container as a type refinement with *semantic properties*, e.g., if the container should only contain unique values (such as a `set`) or should satisfy the LIFO property of a `stack`. Semantic properties nicely complement *syntactic properties* (i.e., traits, interfaces, or type classes), together allowing developers to specify a container's programming interface *and* behaviour without committing to a concrete implementation. Grounding: We present our prototype implementation of Primrose that preprocesses annotated Rust code, selects valid container implementations and ranks them on their performance. The design of Primrose is, however, language-agnostic, and is easy to integrate into other programming languages that support container data types and traits, interfaces, or type classes. Our implementation encodes properties and library specifications into verification conditions in Rosette, an interface for SMT solvers, which determines the set of valid container implementations. We evaluate Primrose by specifying several container implementations, and measuring the time taken to select valid implementations for various combinations of properties with the solver. We automatically validate that container implementations conform to their library specifications via property-based testing. Importance: This work provides a novel approach to bring abstract modelling and specification of container types directly into the programmer's workflow. Instead of selecting concrete container implementations, application programmers can now work on the level of specification, merely stating the behaviours they require from their container types, and the best implementation can be selected automatically.
Spin-spin coupling-based quantum and classical phase transitions in two-impurity spin-boson models

The class of two-interacting-impurity spin-boson models with vanishing transverse ﬁelds on the spin-pair is studied. The model can be exactly mapped into two independent standard single-impurity spin-boson models where the role of the tunnelling parameter is played by the spin-spin coupling. The dynamics of the magnetization is analysed for diﬀerent levels of (an)isotropy. Further, the existence of a decoherence-free subspace as well as of both classical and quantum (ﬁrst-order and Kosterlitz-Thouless type) phase transitions, in the Omhic regime, is brought to light.
On $P$-algebras and their duals

The notion of P -algebra due to Margolis, building on work of Moore & Peterson, was motivated by the case of the Steenrod algebra at a prime and its modules. We develop aspects of this theory further, focusing especially on coherent modules and ﬁnite dimensional modules. We also discuss the dual Hopf algebra of P -algebra and its comodules. One of our aims is provide a collection of techniques for calculating cohomology groups over P -algebras and their duals, in particular giving vanishing results. Much of our work is implicit in that of Margolis and others but we are unaware of systematic discussions in the literature. We give some examples illustrating topological applications which follow easily from our results.
Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework

Continual learning requires the model to maintain the learned knowledge while learning from a non-i.i. d data stream continually. Due to the single-pass training setting, online continual learning is very challenging, but it is closer to the real-world scenarios where quick adaptation to new data is appealing. In this paper, we focus on online class-incremental learning setting in which new classes emerge over time. Almost all existing methods are replay-based with a softmax classifier. However, the inherent logits bias problem in the softmax classifier is a main cause of catastrophic forgetting while existing solutions are not applicable for online settings. To bypass this problem, we abandon the softmax classifier and propose a novel generative framework based on the feature space. In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way. In order to improve the ability to learn new data, we further propose a hybrid of generative and discriminative loss to train the model. Extensive experiments on several benchmarks, including newly introduced task-free datasets, show that our method beats a series of state-of-the-art replay-based methods with discriminative classifiers, and reduces catastrophic forgetting consistently with a remarkable margin.
SNaC: Coherence Error Detection for Narrative Summarization

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation frameworks. A long summary that appropriately covers the facets of that text must also present a coherent narrative, but current automatic and human evaluation methods fail to identify gaps in coherence. In this work, we introduce SNaC, a narrative coherence evaluation framework for fine-grained annotations of long summaries. We develop a taxonomy of coherence errors in generated narrative summaries and collect span-level annotations for 6.6k sentences across 150 book and movie summaries. Our work provides the first characterization of coherence errors generated by state-of-the-art summarization models and a protocol for eliciting coherence judgments from crowdworkers. Furthermore, we show that the collected annotations allow us to benchmark past work in coherence modeling and train a strong classifier for automatically localizing coherence errors in generated summaries. Finally, our SNaC framework can support future work in long document summarization and coherence evaluation, including improved summarization modeling and post-hoc summary correction.
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems. Unlike existing studies focusing on the improvement of single-task approaches, BEVerse features in producing spatio-temporal Birds-Eye-View (BEV) representations from multi-camera videos and jointly reasoning about multiple tasks for vision-centric autonomous driving. Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images. After the ego-motion alignment, the spatio-temporal encoder is utilized for further feature extraction in BEV. Finally, multiple task decoders are attached for joint reasoning and prediction. Within the decoders, we propose the grid sampler to generate BEV features with different ranges and granularities for different tasks. Also, we design the method of iterative flow for memory-efficient future prediction. We show that the temporal information improves 3D object detection and semantic map construction, while the multi-task learning can implicitly benefit motion prediction. With extensive experiments on the nuScenes dataset, we show that the multi-task BEVerse outperforms existing single-task methods on 3D object detection, semantic map construction, and motion prediction. Compared with the sequential paradigm, BEVerse also favors in significantly improved efficiency. The code and trained models will be released at https://github.com/zhangyp15/BEVerse.
Bias Analysis of Spatial Coherence-Based RTF Vector Estimation for Acoustic Sensor Networks in a Diffuse Sound Field

In many multi-microphone algorithms, an estimate of the relative transfer functions (RTFs) of the desired speaker is required. Recently, a computationally efficient RTF vector estimation method was proposed for acoustic sensor networks, assuming that the spatial coherence (SC) of the noise component between a local microphone array and multiple external microphones is low. Aiming at optimizing the output signal-to-noise ratio (SNR), this method linearly combines multiple RTF vector estimates, where the complex-valued weights are computed using a generalized eigenvalue decomposition (GEVD). In this paper, we perform a theoretical bias analysis for the SC-based RTF vector estimation method with multiple external microphones. Assuming a certain model for the noise field, we derive an analytical expression for the weights, showing that the optimal model-based weights are real-valued and only depend on the input SNR in the external microphones. Simulations with real-world recordings show a good accordance of the GEVD-based and the model-based weights. Nevertheless, the results also indicate that in practice, estimation errors occur which the model-based weights cannot account for.
Debiasing Neural Retrieval via In-batch Balancing Regularization

People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provides a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven’t been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this.In this work, we propose the {textbf{I}n-{textbf{B}atch {textbf{B}alancing {textbf{R}egularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable {textbf{normed Pairwise Ranking Fairness} (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark {cite{rekabsaz2020neural} show that our {ibbr{} method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.
Simple Regularisation for Uncertainty-Aware Knowledge Distillation

Considering uncertainty estimation of modern neural networks (NNs) is one of the most important steps towards deploying machine learning systems to meaningful real-world applications such as in medicine, finance or autonomous systems. At the moment, ensembles of different NNs constitute the state-of-the-art in both accuracy and uncertainty estimation in different tasks. However, ensembles of NNs are unpractical under real-world constraints, since their computation and memory consumption scale linearly with the size of the ensemble, which increase their latency and deployment cost. In this work, we examine a simple regularisation approach for distribution-free knowledge distillation of ensemble of machine learning models into a single NN. The aim of the regularisation is to preserve the diversity, accuracy and uncertainty estimation characteristics of the original ensemble without any intricacies, such as fine-tuning. We demonstrate the generality of the approach on combinations of toy data, SVHN/CIFAR-10, simple to complex NN architectures and different tasks.
Random matrix theory of polarized light scattering in disordered media

In this work we present a method for generating random matrices describing electromagnetic scattering from disordered media containing dielectric particles with pre- scribed single particle scattering characteristics. Resulting scattering matrices automatically satisfy the physical constraints of unitarity, reciprocity and time reversal, whilst also incorporating the polarization properties of electromagnetic waves and scattering anisotropy. Our technique therefore enables statistical study of a variety of polarization phenomena, including depolarization rates and polarization-dependent scattering by chiral particles. In this vein, we perform numerical simulations for media containing isotropic and chiral spherical particles of diﬀerent sizes for thicknesses ranging from the single to multiple scattering regime and discuss our results, drawing comparisons to established theory.
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention

Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However, most existing work focuses on detecting static FBNs from entire fMRI signals, such as correlation-based functional connectivity. Sliding-window is a widely used strategy to capture the dynamics of FBNs, but it is still limited in representing intrinsic functional interactive dynamics at each time step. And the number of FBNs usually need to be set manually. More over, due to the complexity of dynamic interactions in brain, traditional linear and shallow models are insufficient in identifying complex and spatially overlapped FBNs across each time step. In this paper, we propose a novel Spatial and Channel-wise Attention Autoencoder (SCAAE) for discovering FBNs dynamically. The core idea of SCAAE is to apply attention mechanism to FBNs construction. Specifically, we designed two attention modules: 1) spatial-wise attention (SA) module to discover FBNs in the spatial domain and 2) a channel-wise attention (CA) module to weigh the channels for selecting the FBNs automatically. We evaluated our approach on ADHD200 dataset and our results indicate that the proposed SCAAE method can effectively recover the dynamic changes of the FBNs at each fMRI time step, without using sliding windows. More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.