Miracle-dz/UniFAR-FTUs huggingface dataset:
Reachability of Dimension-Bounded Linear Systems

In this paper, the reachability of dimension-bounded linear systems is investigated. Since state dimensions of dimension-bounded linear systems vary with time, the expression of state dimension at each time is provided. A method for judging the reachability of a given vector space $ \mathcal{V}_{r} $ is proposed. In addition, this paper proves that the $ t $-step reachable subset is a linear space, and gives a computing method. The $ t $-step reachability of a given state is verified via a rank condition. Furthermore, annihilator polynomials are discussed and employed to illustrate the relationship between the invariant space and the reachable subset after the invariant time point $ t^{\ast} $. The inclusion relation between reachable subsets at times $ t^{\ast}+i $ and $ t^{\ast}+j $ is shown via an example.
Arbitrary order of convergence for Riesz fractional derivative via central difference method

We propose a novel method to compute a finite difference stencil for Riesz derivative for artibitrary speed of convergence. This method is based on applying a pre-filter to the Gr\"unwald-Letnikov type central difference stencil. The filter is obtained by solving for the inverse of a symmetric Vandemonde matrix and exploiting the relationship between the Taylor's series coefficients and fast Fourier transform. The filter costs O\left(N^{2}\right) operations to evaluate for O\left(h^{N}\right) of convergence, where h is the sampling distance. The higher convergence speed should more than offset the overhead with the requirement of the number of nodal points for a desired error tolerance significantly reduced. The benefit of progressive generation of the stencil coefficients for adaptive grid size for dynamic problems with the Gr\"unwald-Letnikov type difference scheme is also kept because of the application of filtering. The higher convergence rate is verified through numerical experiments.
Boundary-aware Graph Reasoning for Semantic Segmentation

In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation. Rather than directly construct the graph based on the backbone features, our BGR module explores a reasonable way to combine segmentation erroneous regions with the graph construction scenario. Motivated by the fact that most hard-to-segment pixels broadly distribute on boundary regions, our BGR module uses the boundary score map as prior knowledge to intensify the graph node connections and thereby guide the graph reasoning focus on boundary regions. In addition, we employ an efficient graph convolution implementation to reduce the computational cost, which benefits the integration of our BGR module into current segmentation backbones. Extensive experiments on three challenging segmentation benchmarks demonstrate the effectiveness of our proposed BGR module for semantic segmentation.
Unknown Object Segmentation through Domain Adaptation

The ability to segment unknown objects in cluttered scenes has a profound impact on robot grasping. The rise of deep learning has greatly transformed the pipeline of robotic grasping from model-based approach to data-driven stream, which generally requires a large scale of grasping data either collected in simulation or from real-world examples. In this paper, we proposed a sim-to-real framework to transfer the object segmentation model learned in simulation to the real-world. First, data samples are collected in simulation, including RGB, 6D pose, and point cloud. Second, we also present a GAN-based unknown object segmentation method through domain adaptation, which consists of an image translation module and an image segmentation module. The image translation module is used to shorten the reality gap and the segmentation module is responsible for the segmentation mask generation. We used the above method to perform segmentation experiments on unknown objects in a bin-picking scenario. Finally, the experimental result shows that the segmentation model learned in simulation can be used for real-world data segmentation.
Decomposing the Dynamics of the Lorenz 1963 model using Unstable Periodic Orbits : Averages , Transitions , and Quasi-Periodic Sets

Unstable periodic orbits (UPOs) are a valuable tool for studying chaotic dynamical systems. They allow one to extract information from a system and to distill its dynamical structure. We consider here the Lorenz 1963 model with the classic parameters’ value and decompose its dynamics in terms of UPOs. We investigate how a chaotic orbit can be approximated in terms of UPOs. At each instant, we rank the UPOs according to their proximity to the position of the orbit in the phase space. We study this process from two different perspectives. First, we find that, somewhat unexpectedly, longer period UPOs overwhelmingly provide the best local approximation to the trajectory, even if our UPO-detecting algorithm severely undersamples them. Second, we construct a finite-state Markov chain by studying the scattering of the forward trajectory between the neighbourhood of the various UPOs. Each UPO and its neighbourhood are taken as a possible state of the system. We then study the transitions between the different states. Through the analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a novel interpretation of the mixing processes occurring in the system by taking advantage of the concept of quasi-invariant sets.
Neutrino backgrounds in future liquid noble element dark matter direct detection experiments

Experiments that use liquid noble gasses as target materials, such as argon and xenon, play a significant role in direct detection searches for WIMP(-like) dark matter. As these experiments grow in size, they will soon encounter a new background to their dark matter discovery potential from neutrino scattering off nuclei and electrons in their targets. Therefore, a better understanding of this new source of background is crucial for future large-scale experiments such as ARGO and DARWIN. In this work, we study the impact of atmospheric neutrino flux uncertainties, electron recoil rejection efficiency, recoil energy sensitivity, and other related factors on the dark matter discovery reach. We also show that a significant improvement in sensitivity can potentially be obtained, at large exposures, by combining data from independent argon and xenon experiments.
Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences

With active research in audio compression techniques yielding substantial breakthroughs, spectral reconstruction of low-quality audio waves remains a less indulged topic. In this paper, we propose a novel approach for reconstructing higher frequencies from considerably longer sequences of low-quality MP3 audio waves. Our technique involves inpainting audio spectrograms with residually stacked autoencoder blocks by manipulating individual amplitude and phase values in relation to perceptual differences. Our architecture presents several bottlenecks while preserving the spectral structure of the audio wave via skip-connections. We also compare several task metrics and demonstrate our visual guide to loss selection. Moreover, we show how to leverage differential quantization techniques to reduce the initial model size by more than half while simultaneously reducing inference time, which is crucial in real-world applications.
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, however, typically come with critical safety constraints that must not be violated. In order to ensure safety, we enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. In particular, we extend the idea of linearizing the single-step transition dynamics, as was done for single-agent systems in Safe DDPG (Dalal et al., 2018), to multi-agent settings. We additionally propose to circumvent infeasibility problems in the action correction step using soft constraints (Kerrigan&Maciejowski, 2000). Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction of the soft constraints under mild assumptions. We empirically find that the soft formulation achieves a dramatic decrease in constraint violations, making safety available even during the learning procedure.
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

Semantic segmentation aims to predict pixel-level labels. It has become a popular task in various computer vision applications. While fully supervised segmentation methods have achieved high accuracy on large-scale vision datasets, they are unable to generalize on a new test environment or a new domain well. In this work, we first introduce a new Unaligned Domain Score to measure the efficiency of a learned model on a new target domain in unsupervised manner. Then, we present the new Bijective Maximum Likelihood1 (BiMaL) loss that is a generalized form of the Adversarial Entropy Minimization without any assumption about pixel independence. We have evaluated the proposed BiMaL on two domains. The proposed BiMaL approach consistently outperforms the SOTA methods on empirical experiments on "SYNTHIA to Cityscapes", "GTA5 to Cityscapes", and "SYNTHIA to Vistas".
Error Performance of Uplink SIMO-NOMA with Joint Maximum-Likelihood and Adaptive M-PSK

This paper studies the performance of the uplink Non-Orthogonal Multiple Access (NOMA) with Joint Maximum-Likelihood Detector (JML). We present a generalized upper bound of bit-error rate (BER) expression of adaptive M-ary phase-shift keying (M-PSK) over Rayleigh fading channels. Our studies are enriched through the use of a single transmitting antenna by each user and multiple receiving antennas at the base station (single-input-multiple-output (SIMO)). The derivation of the upper bound expression is obtained considering an arbitrary constellation size by performing Maximum Ratio Combining (MRC) of diversity paths. The extensive computer simulations validate the analysis and it is revealed that the JML outperforms the existing detecting algorithms significantly and achieves the full diversity order for each user.
Deformations of circle-valued functions on $2$-torus

In this paper we give an algebraic description of fundamental groups of orbits of circle-valued smooth functions from some subspace of the space of smooth functions with isolated singularities on 2 -torus T 2 with respect to the action of the group of diﬀeomorphisms of T 2 .
M6-T: Exploring Sparse Expert Models and Beyond Anonymous ACL submission

Sparse expert models can achieve promising 001 results with outrageous large amount of param-002 eters but constant computation cost, and thus 003 it has become a trend in model scaling. Still, 004 it is a mystery how Mixture-of-Experts (MoE) 005 layers leveraging the parameters with sparse 006 activation bring quality gains. In this work, we 007 investigate several key factors in sparse expert 008 models. We find that load imbalance may not 009 be a significant problem affecting model qual-010 ity, and auxiliary balancing loss can be removed 011 without significant performance degrade. We 012 further discover that larger number of sparsely 013 activated experts k may not necessarily bene-014 fit the performance on the time basis, and we 015 observe diminishing marginal utility that the 016 performance gap gradually narrows with the 017 increase in k We take a step forward to pro-018 pose a simple method called expert prototyping 019 that splits experts into different prototypes and 020 applies top-k routing for each prototype in par-021 allel. Our experiments demonstrate that the pro-022 totyping strategy improves the model quality, in 023 comparison with further increasing to a larger k 024 with comparable computation cost to prototyp-025 ing. Furthermore, we conduct an exploration on 026 training extremely large-scale models, and we 027 figure out that the strategy shows greater effec-028 tiveness in training larger models. Notably, we 029 push the model scale to over 1 trillion parame-030 ters on solely 480 NVIDIA V100-32GB GPUs. 031 The proposed giant model M6-T with expert 032 prototyping achieves substantial speedup in 033 convergence over the same-size baseline. 034
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold (such as images, text, and speech) deep networks (Networks), specifically convolutional deep networks (ConvNets), tend to outperform Forests. We conjecture that at least part of the reason for this is that the input to Networks is not simply the feature magnitudes, but also their indices. In contrast, naive Forest implementations fail to explicitly consider feature indices. A recently proposed Forest approach demonstrates that Forests, for each node, implicitly sample a random matrix from some specific distribution. These Forests, like some classes of Networks, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach and show that one can choose distributions in a manifold-aware fashion to incorporate feature locality. We demonstrate the empirical performance on data whose features live on three different manifolds: a torus, images, and time-series. Moreover, we demonstrate its strength in multivariate simulated settings and also show superiority in predicting surgical outcome in epilepsy patients and predicting movement direction from raw stereotactic EEG data from non-motor brain regions. In all simulations and real data, Manifold Oblique Random Forest (MORF) algorithm outperforms approaches that ignore feature space structure and challenges the performance of ConvNets. Moreover, MORF runs fast and maintains interpretability and theoretical justification.
Charge collection measurements of irradiated pad diodes using an edge-on electron beam

The collected charge of three pad diodes has been measured along the diode width using a 4.2 GeV electron beam at the DESY II beam test facility. The electron beam enters parallel to the readout electrode and perpendicular to the edge of the diode. The position of the electron beam was reconstructed by three planes of an EUDET-type telescope. An in-situ procedure was developed to align the diode surface parallel to the electron beam. The result of these measurements was the charge profile as a function of depth for each diode. For a non-irradiated diode, the charge profile is uniform as a function of the beam position for bias voltages above the full-depletion, as expected. For irradiated diodes, the charge profile is non-uniform and changes as a function of bias voltage.
Reduced-Order Multiscale Modeling of Plastic Deformations in 3D Cast Metallic Alloys with Spatially Varying Microstructures

Cast aluminum alloys are increasingly utilized as light-weight materials in automobile industry due to their superior capability in withstanding high mechanical loads. A major challenge impeding the large-scale use of these alloys in high-performance applications is the presence of manufacturing-induced, spatially varying porosity defects. To understand the impacts of these defects on the macro-mechanical properties of cast alloys, multiscale simulations are often required. In this paper, we introduce a computationally efficient reduced-order multiscale framework to simulate the behavior of metallic components containing process-induced porosity under irreversible nonlinear deformations. The proposed model starts from a data compression scheme which significantly reduces the number of unknown variables by agglomerating close-by finite element nodes into a limited number of clusters. It proceeds to project solution variables into a lower dimensional space via a novel reduced-order method where the material’s elasto-plastic behaviors are approximated. The model then combines with a porosity-oriented microstructure characterization and reconstruction algorithm to mimic the material’s local heterogeneity with reconstructed pores of various morphologies and spatial distributions. The performance and versatility of the proposed approach are demonstrated by several numerical experiments.
OHPL: One-shot Hand-eye Policy Learner

The control of a robot for manipulation tasks generally relies on object detection and pose estimation. An attractive alternative is to learn control policies directly from raw input data. However, this approach is time-consuming and expensive since learning the policy requires many trials with robot actions in the physical environment. To reduce the training cost, the policy can be learned in simulation with a large set of synthetic images. The limit of this approach is the domain gap between the simulation and the robot workspace. In this paper, we propose to learn a policy for robot reaching movements from a single image captured directly in the robot workspace from a camera placed on the end-effector (a hand-eye camera). The idea behind the proposed policy learner is that view changes seen from the hand-eye camera produced by actions in the robot workspace are analogous to locating a region-of-interest in a single image by performing sequential object localisation. This similar view change enables training of object reaching policies using reinforcement-learning-based sequential object localisation. To facilitate the adaptation of the policy to view changes in the robot workspace, we further present a dynamic filter that learns to bias an input state to remove irrelevant information for an action decision. The proposed policy learner can be used as a powerful representation for robotic tasks, and we validate it on static and moving object reaching tasks.
Numerical Modeling and Physical Interplay of Stochastic Turbulent Acceleration for Nonthermal Emission Processes

Particle acceleration is a ubiquitous phenomenon in astrophysical and space plasma. Diffusive shock acceleration (DSA) and stochastic turbulent acceleration (STA) are known to be the possible mechanisms for producing very highly energetic particles, particularly in weakly magnetized regions. An interplay of different acceleration processes along with various radiation losses is typically observed in astrophysical sources. While DSA is a systematic acceleration process that energizes particles in the vicinity of shocks, STA is a random energizing process, where the interaction between cosmic ray particles and electromagnetic fluctuations results in particle acceleration. This process is usually interpreted as a biased random walk in energy space, modeled through a Fokker–Planck equation. In the present work, we describe a novel Eulerian algorithm, adopted to incorporate turbulent acceleration in the presence of DSA and radiative processes like synchrotron and inverse Compton emission. The developed framework extends the hybrid Eulerian−Lagrangian module in a full-fledged relativistic Magneto-hydrodynamic (RMHD) code PLUTO. From our validation tests and case studies, we showcase the competing and complementary nature of both acceleration processes. Axisymmetric simulations of an RMHD jet with this extended hybrid framework clearly demonstrate that emission due to shocks is localized, while that due to turbulent acceleration originates in the backflow and is more diffuse, particularly in the high-energy X-ray band.
Language Model Evaluation in Open-ended Text Generation

Although current state-of-the-art language models have achieved impressive results in numerous natural language processing tasks, still they could not solve the problem of producing repetitive, dull and sometimes inconsistent text in open-ended text generation. Studies often attribute this problem to the maximum likelihood training objective, and propose alternative approaches by using stochastic decoding methods or altering the training objective. However, there is still a lack of consistent evaluation metrics to directly compare the efficacy of these solutions. In this work, we study different evaluation metrics that have been proposed to evaluate quality, diversity and consistency of machine-generated text. From there, we propose an practical pipeline to evaluate language models in open-ended generation task, and research on how to improve the model’s performance in all dimensions by leveraging different auxiliary training objectives.
Decidability of positive characteristic tame Hahn fields in $\mathcal{L}_t$

We show that any positive characteristic tame Hahn field $\mathbb{F}((t^\Gamma))$ containing $t$ is decidable in $\mathcal{L}_t$, the language of valued fields with a constant symbol for $t$, if $\mathbb{F}$ and $\Gamma$ are decidable. In particular, we obtain decidability of $\mathbb{F}_p((t^{1/p^{\infty}}))$ and $\mathbb{F}_p((t^{\mathbb{Q}}))$ in $\mathcal{L}_t$. This uses a new AKE-principle for equal characteristic tame fields in $\mathcal{L}_t$, building on work by Kuhlmann, together with Kedlaya's work on finite automata and algebraic extensions of function fields. In the process, we obtain an AKE-principle for tame fields in mixed characteristic.
A distillation based approach for the diagnosis of diseases

Presently, Covid-19 is a serious threat to the world at large. Efforts are being made to reduce disease screening times and in the development of a vaccine to resist this disease, even as thousands succumb to it everyday. We propose a novel method of automated screening of diseases like Covid-19 and pneumonia from Chest X-Ray images with the help of Computer Vision. Unlike computer vision classification algorithms which come with heavy computational costs, we propose a knowledge distillation based approach which allows us to bring down the model depth, while preserving the accuracy. We make use of an augmentation of the standard distillation module with an auxiliary intermediate assistant network that aids in the continuity of the flow of information. Following this approach, we are able to build an extremely light student network, consisting of just 3 convolutional blocks without any compromise on accuracy. We thus propose a method of classification of diseases which can not only lead to faster screening, but can also operate seamlessly on low-end devices.