Miracle-dz/UniFAR-FTUs huggingface dataset:
Model specification, observational equivalence and performance of unit root tests

In this paper we highlight the necessity of new criteria for evaluation of performance of unit root tests. We suggest focusing directly on the reasons that create ambiguity in unit root test’s results. Two reasons for unsatisfactory properties of unit root tests can be found in the literature (i) the model misspecification and (ii) observational equivalence. Regarding first reason, there is immense literature on several components of model specification covering specification techniques, consequence of misspecification and robust methods. However complete model specification involves multiple decisions and most of studies on performance of unit root tests do not address issue of multiple specification decisions simultaneously. The Monte Carlo studies are conditional on some of implicit specification and for Monte Carlo; these specifications are by construction valid. But for real data, the implicit decisions are often not true and specification decisions need to be endogenized. A closer match with real case is possible if multiple specification decisions are endogenized, thus providing more reliable measure of performance of unit root tests. Second problem in differentiating trend and difference stationary process is the observational equivalence between two processes. We suggest exploring data generating processes with different long run dynamics and small sample equivalence so that a researcher should have an idea about other plausible models for a data set for which he has estimated some model.
A Framework for Collaborative Multi-Robot Mapping using Spectral Graph Wavelets

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift's origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90\% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization depends upon the slowest client. In this work, we propose SWIFT: a novel wait-free decentralized FL algorithm that allows clients to conduct training at their own speed. Theoretically, we prove that SWIFT matches the gold-standard iteration convergence rate $\mathcal{O}(1/\sqrt{T})$ of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations $T$). Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms. Although SWIFT achieves the same iteration convergence rate with respect to $T$ as other state-of-the-art (SOTA) parallel stochastic algorithms, it converges faster with respect to run-time due to its wait-free structure. Our experimental results demonstrate that SWIFT's run-time is reduced due to a large reduction in communication time per epoch, which falls by an order of magnitude compared to synchronous counterparts. Furthermore, SWIFT produces loss levels for image classification, over IID and non-IID data settings, upwards of 50% faster than existing SOTA algorithms.
Jet tagging algorithm of graph network with Haar pooling message passing

Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extracts the features of graph, but embeds additional information obtained by clustering of k-means of different particle features. We construct Haarpooling from five different features: absolute energy $\log E$, transverse momentum $\log p_T$, relative coordinates $(\Delta\eta,\Delta\phi)$, the mixed ones $(\log E, \log p_T)$ and $(\log E, \log p_T, \Delta\eta,\Delta\phi)$. The results show that an appropriate selection of information for HaarPooling enhances the accuracy of quark-gluon tagging, as adding extra information of $\log P_T$ to the HMPNet outperforms all the others, whereas adding relative coordinates information $(\Delta\eta,\Delta\phi)$ is not very effective. This implies that by adding effective particle features from HaarPooling can achieve much better results than solely pure message passing neutral network (MPNN) can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally we compare the HMPNet study, ordering by $p_T$, with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.
Grating Lobe Reduction in Plane-Wave Imaging With Angular Compounding Using Subtraction of Coherent Signals

Plane-wave imaging (PWI) with angular compounding has gained in popularity over recent years, because it provides high frame rates and good image properties. However, most linear arrays used in clinical practice have a pitch that is equal to than the wavelength of ultrasound. Hence, the presence of grating lobes is a concern for PWI using multiple transmit angles. The presence of grating lobes produces clutter in images and reduces the ability to observe tissue contrast. Techniques to reduce or eliminate the presence of grating lobes for PWI using multiple angles will result in improved image quality. Null subtraction imaging (NSI) is a nonlinear beamforming technique that has been explored for improving the lateral resolution of ultrasonic imaging. However, the apodization scheme used in NSI also eliminates or greatly reduces the presence of grating lobes. Imaging tasks using NSI were evaluated in simulations and physical experiments involving tissue-mimicking phantoms and rat tumors in vivo. Images created with NSI were compared with images created using traditional delay and sum (DAS) with Hann apodization and images created using a generalized coherence factor (GCF). NSI was observed to greatly reduce the presence of grating lobes in ultrasonic images, compared to DAS with Hann and GCF, while maintaining spatial resolution and contrast in the images. Therefore, NSI can provide a novel means of creating images using PWI with multiple steering angles on clinically available linear arrays while reducing the adverse effects associated with grating lobes.
Streaming Parrotron for on-device speech-to-speech conversion

We present a fully on-device streaming Speech2Speech conversion model that normalizes a given input speech directly to synthesized output speech. Deploying such a model on mobile devices pose significant challenges in terms of memory footprint and computation requirements. We present a streaming-based approach to produce an acceptable delay, with minimal loss in speech conversion quality, when compared to a reference state of the art non-streaming approach. Our method consists of first streaming the encoder in real time while the speaker is speaking. Then, as soon as the speaker stops speaking, we run the spectrogram decoder in streaming mode along the side of a streaming vocoder to generate output speech. To achieve an acceptable delay-quality trade-off, we propose a novel hybrid approach for look-ahead in the encoder which combines a look-ahead feature stacker with a look-ahead self-attention. We show that our streaming approach is almost 2x faster than real time on the Pixel4 CPU.
Topics in broadband gravitational-wave astronomy

The direct detection of gravitational waves promises to open a new observational window onto the universe, and a number of large scale efforts are underway worldwide to make such a detection a reality. In this work, we attack some of the current problems in gravitational-wave detection over a wide range of frequencies. In the first part of this work, low frequency gravitational-wave detection is considered using pulsar timing arrays (PTAs). PTAs are a promising tool for probing the universe through gravitational radiation. Supermassive black hole binaries (SMBHBs), cosmic strings, relic gravitational waves from inflation, and first order phase transitions in the early universe are expected to contribute to a stochastic background of gravitational waves in the PTA frequency band of 1 nHz-100 nHz. The detection of low-frequency stochastic backgrounds of gravitational waves in the PTA band is considered in the context of constructing an optimal cross-correlation statistic in the time domain. Also presented are some useful applications of this statistic, and discussion on its limitations in actual gravitational-wave searches. Also considered are methods by which gravitational waves in the PTA frequency band can serve as a mechanism for testing general relativity (GR). In addition to providing a new paradigm for exploring the universe, the direct detection of gravitational waves will allow general relativity to be tested against other metric theories of gravity in the regime of strong gravitational fields. This work involves the analysis of the overlap reduction function (ORF), a geometrical factor that appears in the expected cross correlation of signals, for general metric theories of gravity. The ORF characterizes the loss of sensitivity due to detectors not being co-located or coaligned, and it is an important element in defining the optimal cross-correlation statistic. It is shown that PTA detection sensitivity increases for non-transverse gravitational waves. Additionally, the ORFs for a subset of the NANOGrav PTA are described, and are used to show that sensitivity to vector and longitudinal modes can increase dramatically for pulsar pairs with small angular separations. Implications of these results are discussed. In the second part of this work, the detection of gravitational-wave bursts in the 10 Hz-1000 Hz frequency band is considered using ground-based laser interferometers. An excess power method for conducting unmodeled searches for gravitational-wave bursts is discussed, and its implementation into a search pipeline is described in detail. The performance of this pipeline is probed using software injections. Also discussed are potential applications of the ExcessPower pipeline to detector characterization efforts, which aim to improve interferometric searches by characterizing and mitigating non-Gaussian noise transients in the detectors.
Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies

The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources. We tackle the task of segmenting running (spoken) narratives, which poses hitherto unaddressed challenges. As a test case, we address Holocaust survivor testimonies, given in English. Other than the importance of studying these testimonies for Holocaust research, we argue that they provide an interesting test case for topical segmentation, due to their unstructured surface level, relative abundance (tens of thousands of such testimonies were collected), and the relatively confined domain that they cover. We hypothesize that boundary points between segments correspond to low mutual information between the sentences proceeding and following the boundary. Based on this hypothesis, we explore a range of algorithmic approaches to the task, building on previous work on segmentation that uses generative Bayesian modeling and state-of-the-art neural machinery. Compared to manually annotated references, we find that the developed approaches show considerable improvements over previous work.
A comparative study on deep‐learning methods for dense image matching of multi‐angle and multi‐date remote sensing stereo‐images

Deep‐learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo‐images lacking a systematic evaluation on how robust DL methods are on satellite stereo‐images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi‐date multi‐site satellite stereopairs with varying geometric configurations, against the traditional well‐practiced Census‐semi‐global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning‐based cost metric through convolutional neural networks (MC‐CNN) followed by SGM, and three end‐to‐end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning‐based cost metric and Census‐SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census‐SGM, while learning‐based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground‐view).
Discrete orthogonality of the polynomial sequences in the $q$-Askey scheme

We obtain weight functions associated with q -linear and q -quadratic lattices that yield discrete orthogonality with respect to a quasi-deﬁnite moment functional for the Askey-Wilson polynomials and all the polynomial sequences in the q-Askey scheme, with the exception of the continuous q -Hermite polynomials.
Dual Mechanism Priming Effects in Hindi Word Order

Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model, and we capture more abstract syntactic priming with an adaptive neural language model. We permute the preverbal constituents of corpus sentences and then use a logistic regression model to predict which sentences actually occurred in the corpus against artificially generated meaning-equivalent variants. Our results indicate that lexical priming and lexically-independent syntactic priming affect complementary sets of verb classes. By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks

Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.
X‐ray properties of galaxy clusters and groups from a cosmological hydrodynamical simulation

We present results on the X-ray properties of clusters and groups of galaxies, extracted from a large cosmological hydrodynamical simulation. We used the TREE+SPH code GADGET to simulate a concordance A cold dark matter cosmological model within a box of 192 h -1 Mpc on a side, 480 3 dark matter particles and as many gas particles. The simulation includes radiative cooling assuming zero metallicity, star formation and supernova feedback. The very high dynamic range of the simulation allows us to cover a fairly large interval of cluster temperatures. We compute X-ray observables of the intracluster medium (ICM) for simulated groups and clusters and analyse their statistical properties. The simulated mass-temperature relation is consistent with observations once we mimic the procedure for mass estimates applied to real clusters. Also, with the adopted choices of Ω m = 0.3 and σ 8 = 0.8 for matter density and power spectrum normalization, respectively, the resulting X-ray temperature functton agrees with the most recent observational determinations. The luminosity-temperature relation also agrees with observations for clusters with T ≥ 2 keV. At the scale of groups, T ≥ 1 keV, we find no change of slope in this relation. The entropy in central cluster regions is higher than predicted by gravitational heating alone, the excess being almost the same for clusters and groups. We also find that the simulated clusters appear to have suffered some overcooling. We find f * ≃ 0.2 for the fraction of baryons in stars within clusters, thus approximately twice as large as the value observed. Interestingly, temperature profiles of simulated clusters are found to increase steadily toward cluster centres. They decrease in the outer regions, much like observational data do at r ≥ 0.2r vir , while not showing an isothermal regime followed by a smooth temperature decline in the innermost regions. Our results thus demonstrate the need for yet more efficient sources of energy feedback and/or the need to consider additional physical process which may be able to further suppress the gas density at the scale of poor clusters and groups, and, at the same time, to regulate the cooling of the ICM in central regions.
MAROAM: Map-based Radar SLAM through Two-step Feature Selection

In this letter, we propose MAROAM, a millimeter wave radar-based SLAM framework, which employs a two-step feature selection process to build the global consistent map. Specifically, we first extract feature points from raw data based on their local geometric properties to filter out those points that violate the principle of millimeter-wave radar imaging. Then, we further employ another round of probabilistic feature selection by examining how often and how recent the feature point has been detected in the proceeding frames. With such a two-step feature selection, we establish a global consistent map for accurate and robust pose estimation as well as other downstream tasks. At last, we perform loop closure and graph optimization in the back-end, further reducing the accumulated drift error. We evaluate the performance of MAROAM on the three datasets: the Oxford Radar RobotCar Dataset, the MulRan Dataset and the Boreas Dataset. We consider a variety of experimental settings with different scenery, weather, and road conditions. The experimental results show that the accuracy of MAROAM is 7.95%, 37.0% and 8.9% higher than the currently best-performing algorithms on these three datasets, respectively. The ablation results also show that our map-based odometry performs 28.6% better than the commonly used scan-to-frames method. Finally, as devoted contributors to the open-source community, we will open source the algorithm after the paper is accepted.
Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation

Differentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate through forward iteration layers to compute gradients, which couples forward computation and backpropagation, and needs to balance forward planner performance and computational cost of the backward pass. To alleviate this issue, we propose to differentiate through the Bellman fixed-point equation to decouple forward and backward passes for Value Iteration Network and its variants, which enables constant backward cost (in planning horizon) and flexible forward budget and helps scale up to large tasks. We study the convergence stability, scalability, and efficiency of the proposed implicit version of VIN and its variants and demonstrate their superiorities on a range of planning tasks: 2D navigation, visual navigation, and 2-DOF manipulation in configuration space and workspace.
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation

Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping policies for arbitrary objects has proven challenging due to the difficulty of generating high quality training data. In this work, we propose a learning system (ISAGrasp) for leveraging a small number of human demonstrations to bootstrap the generation of a much larger dataset containing successful grasps on a variety of novel objects. Our key insight is to use a correspondence-aware implicit generative model to deform object meshes and demonstrated human grasps in order to generate a diverse dataset of novel objects and successful grasps for supervised learning, while maintaining semantic realism. We use this dataset to train a robust grasping policy in simulation which can be deployed in the real world. We demonstrate grasping performance with a four-fingered Allegro hand in both simulation and the real world, and show this method can handle entirely new semantic classes and achieve a 79% success rate on grasping unseen objects in the real world.