Miracle-dz/UniFAR-FTUs huggingface dataset:
MORAVA K-THEORY AND FILTRATIONS BY POWERS

We prove the convergence of the Adams spectral sequence based on Morava K-theory and relate it to the filtration by powers of the maximal ideal in the Lubin–Tate ring through a Miller square. We use the filtration by powers to construct a spectral sequence relating the homology of the K-local sphere to derived functors of completion and express the latter as cohomology of the Morava stabiliser group. As an application, we compute the zeroth limit at all primes and heights.
ConTesse: Accurate Occluding Contours for Smooth Surfaces

This paper proposes a method for computing the visible occluding contours of smooth surfaces. First, new necessary and suﬃcient conditions are introduced for when a sampled occluding contour is valid, that is, when it may be assigned consistent visibility. Previous methods do not guarantee these conditions, which helps explain why smooth contour visibility has been such a challenging problem in the past. The paper then proposes an algorithm that, given a smooth subdivision surface, ﬁnds sampled contours satisfying these conditions, and then generates a new triangle mesh matching the given occluding contours. The contours of the output triangle mesh may then be rendered with standard non-photorealistic rendering algorithms, using the mesh for visibility computation
A Portable and Passive Gravity Compensation Arm Support for Drone Teleoperation

Carine Rognon,1 Loic Grossen,1 Stefano Mintchev,2 Jenifer Miehlbradt,3 Silvestro Micera,3 and Dario Floreano1, ∗ Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Environmental Robotics Laboratory, Dep. of Environmental Systems Science, ETHZ, Zurich, Switzerland & Swiss Federal Institute for Forest Snow, and Landscape Research (WSL), Birmensdorf, Switzerland Translational Neural Engineering Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Characterizing the initial conditions of heavy-ion collisions at the LHC with mean transverse momentum and anisotropic flow correlations

The study of the initial conditions of relativistic heavy-ion collisions and the subsequent development of hot and dense nuclear matter at the LHC is fundamental for the understanding of the strong nuclear force. The traditional approach of comparing observables with hydrodynamical models based on different initial conditions typically fails to isolate the effects of the initial conditions due to the sensitivity of the observables to the collective behaviour of the expanding system. Correlations of the mean transverse momentum [p T] and the anisotropic flow Fourier coefficients v n have been shown to serve as a unique probe of the initial conditions of the heavy-ion collisions with little to no bias from final state effects. In this talk, correlations between [p T] and v 2 or v 3 are presented as a function of centrality in Pb−Pb and Xe−Xe collisions at sNN=5.02 TeV and sNN=5.44 TeV, respectively, measured with ALICE. Additionally, measurements of the higher-order correlation between [p T], v 2, and v 3 is measured for the first time. All of these measurements are compared with hydrodynamical models using IP-Glasma or TRENTo initial conditions. The former is based on Color Glass Condensate effective theory with gluon saturation and the latter is a parameterized model with nucleons as the relevant degrees of freedom. The data is best described by models using IP-Glasma initial conditions, whereas the TRENTo based models fail to describe the data regardless of the parametrization.
Implicit SVD for Graph Representation Learning

Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e.g., for calculating gradients via backprop over many data epochs. Meanwhile, Singular Value Decomposition (SVD) can find closed-form solutions to convex problems, using merely a handful of epochs. In this paper, we make GRL more computationally tractable for those with modest hardware. We design a framework that computes SVD of \textit{implicitly} defined matrices, and apply this framework to several GRL tasks. For each task, we derive linear approximation of a SOTA model, where we design (expensive-to-store) matrix $\mathbf{M}$ and train the model, in closed-form, via SVD of $\mathbf{M}$, without calculating entries of $\mathbf{M}$. By converging to a unique point in one step, and without calculating gradients, our models show competitive empirical test performance over various graphs such as article citation and biological interaction networks. More importantly, SVD can initialize a deeper model, that is architected to be non-linear almost everywhere, though behaves linearly when its parameters reside on a hyperplane, onto which SVD initializes. The deeper model can then be fine-tuned within only a few epochs. Overall, our procedure trains hundreds of times faster than state-of-the-art methods, while competing on empirical test performance. We open-source our implementation at: https://github.com/samihaija/isvd
Rise and fall of Yu-Shiba-Rusinov bound states in charge-conserving 
s
-wave one-dimensional superconductors

We re-examine the problem of a magnetic impurity coupled to a superconductor focusing on the role of quantum ﬂuctuations. We study in detail, a system that consists of a one-dimensional charge conserving spin-singlet superconductor coupled to a boundary magnetic impurity. Our main ﬁnding is that quantum ﬂuctuations lead to the destruction of Yu-Shiba-Rusinov (YSR) intra-gap bound-states in all but a narrow region of the phase diagram. We carry out our analysis in three stages, increasing the role of the quantum ﬂuctuations at each stage. First we consider the limit of a classical impurity and study the bulk semiclassically, ﬁnding YSR states throughout the phase diagram, a situation similar to conventional BCS superconductors. In the second stage, we reintroduce quantum ﬂuctuations in the bulk and ﬁnd that the YSR state is suppressed over half of the phase diagram, existing only around the quantum critical point separating the unscreened and the partially screened phases. In the ﬁnal stage we solve exactly the full interacting model with arbitrary coupling constants using Bethe Ansatz. We ﬁnd that including both the quantum ﬂuctuating bulk and quantum impurity destabilizes the YSR state over most of the phase diagram allowing it to exist only in a small region, the YSR regime, between a Kondo-screened and an unscreened regime. Within the YSR regime a ﬁrst order phase transition occurs between a spin singlet and doublet ground state. We also ﬁnd that for large enough impurity spin exchange interaction a renormalized Kondo-screened regime is established. In this regime, not found for BCS superconductors, there is no YSR state and a renormalized Kondo temperature scale is generated.
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions

Mimicking and learning the long-term memory of efficient markets is a fundamental problem in the interaction between machine learning and financial economics to sequential data. Despite the prominence of this issue, current treatments either remain largely limited to heuristic techniques or rely significantly on periodogram or Gaussianty assumptions. In this paper, we present the ApeRIodic SEmi-parametric (ARISE) process for investigating efficient markets. The ARISE process is formulated as an infinite-sum function of some known processes and employs the aperiodic spectrum estimation to determine the key hyper-parameters, thus possessing the power and potential of modeling the price data with long-term memory, non-stationarity, and aperiodic spectrum. We further theoretically show that the ARISE process has the mean-square convergence, consistency, and asymptotic normality without periodogram and Gaussianity assumptions. In practice, we apply the ARISE process to identify the efficiency of real-world markets. Besides, we also provide two alternative ARISE applications: studying the long-term memorability of various machine-learning models and developing a latent state-space model for inference and forecasting of time series. The numerical experiments confirm the superiority of our proposed approaches.
Traffic4cast - Large-scale Traffic Prediction using 3DResNet and Sparse-UNet

The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide high-resolution traffic states given the static and dynamic traffic information obtained previously. The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-scale cities using historical data points. The model is supposed to be generic, in a way that it can be applied to new cities. By considering spatiotemporal feature learning and modeling efficiency, we explore 3DResNet and Sparse-UNet approaches for the tasks in this competition. The 3DResNet based models use 3D convolution to learn the spatiotemporal features and apply sequential convolutional layers to enhance the temporal relationship of the outputs. The Sparse-UNet model uses sparse convolutions as the backbone for spatiotemporal feature learning. Since the latter algorithm mainly focuses on non-zero data points of the inputs, it dramatically reduces the computation time, while maintaining a competitive accuracy. Our results show that both of the proposed models achieve much better performance than the baseline algorithms. The codes and pretrained models are available at https://github.com/resuly/Traffic4Cast-2021.
Generalized spin-boson models with non-normalizable form factors

Generalized spin-boson (GSB) models describe the interaction between a quantum mechanical system and a structured boson environment, mediated by a family of coupling functions known as form factors. We propose an extension of the class of GSB models, which can accommodate non-normalizable form factors provided that they satisfy a weaker growth constraint, thus accounting for a rigorous description of a wider range of physical scenarios; we also show that such “singular” GSB models can be rigorously approximated by GSB models with normalizable form factors. Furthermore, we discuss in greater detail the structure of the spin-boson model with a rotating wave approximation: for this model, the result is improved via a nonperturbative approach that enables us to further extend the class of admissible form factors as well as to compute its resolvent and characterize its self-adjointness domain.
Evaluation of the economics of conversion to compressed natural gas for a municipal bus fleet

Domestic natural gas production has increased markedly in the United States, and now compressed natural gas (CNG) has the potential to become a cleaner and less expensive energy source than diesel fuel for use in the public transportation sector, especially for city bus fleets. This paper provides an economic analysis of possible CNG conversion for Lafayette, IN CityBus Corporation. It uses benefit–cost analysis to compare the total cost of three potential options for bus replacement: standard diesel, hybrid diesel‐electric, and CNG. A spreadsheet model was used to estimate the total cost of these three fleet options over a 15‐year project horizon. Results suggest that the CNG option has the lowest net present value (NPV) cost, and that cost savings would be larger if the corporation could obtain a grant for the CNG fueling station or if the project life span could expand to 20 years. From the environmental perspective, the CNG option would reduce greenhouse gas and particulate emissions particularly in comparison with the diesel option. Monte Carlo simulation was used to examine the inherent riskiness of the three fleet options. CNG is always lower cost than hybrid diesel‐electric. Depending on assumptions regarding the underlying price distributions, the CNG option has a 51–100% chance of being lower cost than the standard diesel option.
A mechanorheological memory suspension

Smart materials that can be manipulated by external stimuli [1] have wide applications, from automotive shock absorbers [2] to microfluidics [3]. Electrorheological [3] and magnetorheological [4] fluids are prototypical examples. Applying the relevant field solidifies the fluid; removing the field causes the material to re-fluidise. These fluids are monostable and lack memory; the solid state persists only when a field is applied. We report a new bistable smart material, a mechanorheological (MecR) fluid whose rheology is tuneable by mechanical stimuli. While most complex fluids are MecR in the sense that their rheology is rate-dependent, e.g. suspensions may shear thicken [5, 6], dry grains fluidise when vibrated [7], these fluids lack memory. Surprisingly, adding non-Brownian particles to a colloidal gel forms a bistable MecR suspension, which is mechanically switchable between solid and liquid states and ‘remembers’ [8] the state into which it has been switched upon removal of the stimuli. Binary suspensions of repulsive granular (= non-Brownian) and attractive colloidal (= Brownian) particles are common in industry, from fast-moving consumer products in which colloidal gels [9] are used to prevent sedimentation of larger grains, to battery cathodes formed by depositing thin films of catholyte slurries consisting of grains of lithium-ion conductor (≈ 10 μm) dispersed in a conductive carbon-black gel [10]. In many applications it is crucial to control both the suspension rheology and microstructure, e.g. in battery cathodes the slurry rheology is key to coating efficiency while its microstructure controls battery performance [11, 12]. Granular suspensions and colloidal gels have received considerable attention individually [6, 13, 14, 15]. However, mixtures of the two have not been studied with any degree of thoroughness. While the implicit assumption seems to be that such a mixture would simply behave as ‘the sum of the parts’, we show that, in fact, a binary suspension of grains and attractive colloids show surprising novelty.
Socially-aware evaluation framework for transportation

ABSTRACT Technological advancements are rapidly changing traffic management in cities. Navigation applications, in particular, have impacted cities in many ways by rerouting traffic. As different routing strategies distribute traffic differently, understanding these disparities across multiple city-relevant dimensions is extremely important for decision-makers. We develop a multi-themed framework called Socially- Aware Evaluation Framework for Transportation (SAEF), which assists in understanding how traffic routing and the resultant dynamics affect cities. The framework is presented for four Bay Area cities, for which we compare three routing strategies - user equilibrium travel time, system optimal travel time, and system optimal fuel. The results demonstrate that many neighborhood impacts, such as traffic load on residential streets and around minority schools, degraded with the system-optimal travel time and fuel routing in comparison to the user-equilibrium travel time routing. The findings also show that all routing strategies subject the city's disadvantaged neighborhoods to disproportionate traffic exposure. Our intent with this work is to provide an evaluation framework that enables reflection on the consequences of traffic routing and management strategies, allowing city planners to recognize the trade-offs and potential unintended consequences.
Reliability Function of Quantum Information Decoupling

Quantum information decoupling is a fundamental quantum information processing task, which also serves as a crucial tool in a diversity of topics in quantum physics. In this paper, we characterize the reliability function of catalytic quantum information decoupling, that is, the best exponential rate under which perfect decoupling is asymptotically approached. We have obtained the exact formula when the decoupling cost is below a critical value. In the situation of high cost, we provide upper and lower bounds. This result is then applied to quantum state merging, exploiting its inherent connection to decoupling. In addition, as technical tools, we derive the exact exponents for the smoothing of the conditional min-entropy and max-information, and we prove a novel bound for the convex-split lemma. Our results are given in terms of the sandwiched R\'enyi divergence, providing it with a new type of operational meaning in characterizing how fast the performance of quantum information tasks approaches the perfect.
Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training

With deep learning models rapidly growing in size, systems-level solutions for large-model training are required. We present Amazon SageMaker model parallelism, a software library that integrates with PyTorch, and enables easy training of large models using model parallelism and other memory-saving features. In contrast to existing solutions, the implementation of the SageMaker library is much more generic and flexible, in that it can automatically partition and run pipeline parallelism over arbitrary model architectures with minimal code change, and also offers a general and extensible framework for tensor parallelism, which supports a wider range of use cases, and is modular enough to be easily applied to new training scripts. The library also preserves the native PyTorch user experience to a much larger degree, supporting module re-use and dynamic graphs, while giving the user full control over the details of the training step. We evaluate performance over GPT-3, RoBERTa, BERT, and neural collaborative filtering, and demonstrate competitive performance over existing solutions.
Indian Licence Plate Dataset in the wild

Indian Licence Plate Detection is a problem that has not been explored much at an open-source level.There are proprietary solutions available for it, but there is no big open-source dataset that can be used to perform experiments and test different approaches.Most of the large datasets available are for countries like China, Brazil, but the model trained on these datasets does not perform well on Indian plates because the font styles and plate designs used vary significantly from country to country.This paper introduces an Indian license plate dataset with 16192 images and 21683 plate plates annotated with 4 points for each plate and each character in the corresponding plate.We present a benchmark model that uses semantic segmentation to solve number plate detection. We propose a two-stage approach in which the first stage is for localizing the plate, and the second stage is to read the text in cropped plate image.We tested benchmark object detection and semantic segmentation model, for the second stage, we used lprnet based OCR.
Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention

Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky approach, where pretrained neural networks are used as a feature extractors and then extracted features are being fused. However, this approach does not consider attributes of multimodal data and feature extractors cannot be fine-tuned for specific task which can be disadvantageous for overall model accuracy. To this end, our impact is twofold: (i) we train model end-to-end, which allows early layers of neural network to be adapted with taking into account later, fusion layers, of two modalities; (ii) all layers of our model was fine-tuned for downstream task of emotion recognition, so there were no need to train neural networks from scratch. Our model achieves best validation accuracy of 60.37% which is approximately 8.5% higher, than VGAF dataset baseline and is competitive with existing works, audio and video modalities.
Improving Large-scale Language Models and Resources for Filipino

In this paper, we improve on existing language resources for the low-resource Filipino language in two ways. First, we outline the construction of the TLUnified dataset, a large-scale pretraining corpus that serves as an improvement over smaller existing pretraining datasets for the language in terms of scale and topic variety. Second, we pretrain new Transformer language models following the RoBERTa pretraining technique to supplant existing models trained with small corpora. Our new RoBERTa models show significant improvements over existing Filipino models in three benchmark datasets with an average gain of 4.47% test accuracy across three classification tasks with varying difficulty.
Towards Theoretical Understanding of Flexible Transmitter Networks via Approximation and Local Minima

Flexible Transmitter Network (FTNet) is a recently proposed bio-plausible neural network and has achieved competitive performance with the state-of-the-art models when handling temporal-spatial data. However, there remains an open problem about the theoretical understanding of FTNet. This work investigates the theoretical properties of one-hidden-layer FTNet from the perspectives of approximation and local minima. Under mild assumptions, we show that: i) FT-Net is a universal approximator; ii) the approximation complexity of FTNet can be exponentially smaller than those of real-valued neural networks with feedforward/recurrent architectures and is of the same order in the worst case; iii) any local minimum of FTNet is the global minimum, which suggests that it is possible for local search algorithms to converge to the global minimum. Our theoretical results indicate that FTNet can eﬃciently express target functions and has no concern about local minima, which complements the theoretical blank of FTNet and exhibits the possibility for ameliorating the FTNet.
Applicability of Multilayer Diffusion Network Inference to Social Media Data

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, attitude, or language. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns, but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we first introduce an effective implementation of the inference method that can achieve higher accuracy than existing implementations in comparable runtime. Then, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the method, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our findings include that the inference accuracy varies extremely with network density, and that the method fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the method before running the inference on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation supports further testing under personalized settings.
Fundamental groups of orbit configuration spaces

In this note we deduce that the fundamental group of the orbit configuration space of an effective and properly discontinuous action of a discrete group, on an aspherical 2-manifold with isolated fixed points is torsion free. Furthermore, if the 2-manifold has at least one puncture then it is polyfree, and hence has an iterated semi-direct product of free groups structure. The first result supports the Asphericity conjecture of [9], and the second one gives a general version of [[13], Theorem 6.3].