Miracle-dz/UniFAR-FTUs huggingface dataset:
Internal-current Lorentz-force Heating of Astrophysical Objects

Two forms of ohmic heating of astrophysical secondaries have received particular attention: unipolar-generator heating with currents running between the primary and secondary, and magnetic induction heating due to the primary’s time-varying field. Neither appears to cause significant dissipation in the contemporary solar system. But these discussions have overlooked heating derived from the spatial variation of the primary’s field across the interior of the secondary. This leads to Lorentz-force-driven currents around paths entirely internal to the secondary, with resulting ohmic heating. We examine three ways to drive such currents, by the cross product of (1) the secondary’s azimuthal orbital velocity with the nonaxially symmetric field of the primary, (2) the radial velocity (due to nonzero eccentricity) of the secondary with the primary’s field, or (3) the out-of-plane velocity (due to nonzero inclination) with the primary’s field. The first of these operates even for a spin-locked secondary whose orbit has zero eccentricity, in strong contrast to tidal dissipation. We show that Jupiter’s moon Io today could dissipate about 600 GW (more than likely current radiogenic heating) in the outer 100 m of its metallic core by this mechanism. Had Io ever been at 3 Jovian radii instead of its current 5.9, it could have been dissipating 15,000 GW. Ohmic dissipation provides a mechanism that could operate in any solar system to drive inward migration of secondaries that then necessarily comes to a halt upon reaching a sufficiently close distance to the primary.
Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle Adjustment

—In this paper, an essential problem of robust visual odometry (VO) is approached by incorporating geometry-based methods into deep-learning architecture in a self-supervised manner. Generally, pure geometry-based algorithms are not as robust as deep learning in feature-point extraction and matching, but perform well in ego-motion estimation because of their well-established geometric theory. In this work, a novel optical ﬂow network (PANet) built on a position-aware mechanism is proposed ﬁrst. Then, a novel system that jointly estimates depth, optical ﬂow, and ego-motion without a typical network to learning ego-motion is proposed. The key component of the proposed system is an improved bundle adjustment module containing multiple sampling, initialization of ego-motion, dynamic damping factor adjustment, and Jacobi matrix weighting. In addition, a novel relative photometric loss function is advanced to improve the depth estimation accuracy. The experiments show that the proposed system not only outperforms other state-of-the-art methods in terms of depth, ﬂow, and VO estimation among self-supervised learning-based methods on KITTI dataset, but also signiﬁcantly improves robustness compared with geometry-based, learning-based and hybrid VO systems. Further experiments show that our model achieves outstanding generalization ability and performance in challenging indoor (TMU-RGBD) and outdoor (KAIST) scenes.
Monocular Road Planar Parallax Estimation

Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sensors such as LiDAR or directly predicting the depth of points via deep learning. However, the former is expensive, and the latter lacks the use of geometry information for the scene. In this paper, instead of following existing methodologies, we propose Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences based on planar parallax, which takes full advantage of the omnipresent road plane geometry in driving scenes. RPANet takes a pair of images aligned by the homography of the road plane as input and outputs a $\gamma $ map (the ratio of height to depth) for 3D reconstruction. The $\gamma $ map has the potential to construct a two-dimensional transformation between two consecutive frames. It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames. Furthermore, we introduce a novel cross-attention module to make the network better perceive the displacements caused by planar parallax. To verify the effectiveness of our method, we sample data from the Waymo Open Dataset and construct annotations related to planar parallax. Comprehensive experiments are conducted on the sampled dataset to demonstrate the 3D reconstruction accuracy of our approach in challenging scenarios.
Bridging the gap between learning and heuristic based pushing policies

— Non-prehensile pushing actions have the potential to singulate a target object from its surrounding clutter in order to facilitate the robotic grasping of the target. To address this problem we utilize a heuristic rule that moves the target object towards the workspace’s empty space and demonstrate that this simple heuristic rule achieves singulation. Furthermore, we incorporate this heuristic rule to the reward in order to train more efﬁciently reinforcement learning (RL) agents for singulation. Simulation experiments demonstrate that this insight increases performance. Finally, our results show that the RL-based policy implicitly learns something similar to one of the used heuristics in terms of decision making.
Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection

In many speech-enabled human-machine interactions, user speech can overlap with the device playback audio. In these instances, the performance of tasks such as keyword-spotting (KWS) and device-directed speech detection (DDD) can degrade significantly. To address this problem, we propose an implicit acoustic echo cancellation (iAEC) framework where a neural network is trained to exploit the additional information from a reference microphone channel to learn to ignore the interfering signal and improve detection performance. We study this framework for the tasks of KWS and DDD on, respectively, an augmented version of Google Speech Commands v2 and a real-world Alexa device dataset. Notably, we show a 56% reduction in false-reject rate for the DDD task during device playback conditions. We also show comparable or superior performance over a strong end-to-end neural echo cancellation baseline for the KWS task with two order of magnitude less computational requirements.
Knot topology of exceptional point and non-Hermitian no-go theorem

Exceptional points (EPs) are peculiar band singularities and play a vital role in a rich array of unusual optical phenomena and non-Hermitian band theory. In this paper, we provide a topological classiﬁcation of isolated EPs based on homotopy theory. In particular, the classiﬁcation indicates that an n -th order EP in two dimensions is fully characterized by the braid group B n , with its eigenenergies tied up into a geometric knot along a closed path enclosing the EP. The quantized discriminant invariant of the EP is the writhe of the knot. The knot crossing number gives the number of bulk Fermi arcs emanating from each EP. Furthermore, we put forward a non-Hermitian no-go theorem, which governs the possible conﬁgurations of EPs and their splitting rules on a two-dimensional lattice and goes beyond the previous fermion doubling theorem. We present a simple algorithm generating the non-Hermitian Hamiltonian with a prescribed knot. Our framework constitutes a systematic topological classiﬁcation of the EPs and paves the way towards exploring the intriguing phenomena related to the enigmatic non-Hermitian band degeneracy.
Poisson Noise Channel with Dark Current: Numerical Computation of the Optimal Input Distribution

This paper considers a discrete time-Poisson noise channel which is used to model pulse-amplitude modulated optical communication with a direct-detection receiver. The goal of this paper is to obtain insights into the capacity and the structure of the capacity-achieving distribution for the channel under the amplitude constraint A and in the presence of dark current λ. Using recent theoretical progress on the structure of the capacity-achieving distribution, this paper develops a numerical algorithm, based on the gradient ascent and Blahut-Arimoto algorithms, for computing the capacity and the capacity-achieving distribution. The algorithm is used to perform extensive numerical simulations for various regimes of A and λ.
Control Analysis of Packet Transmission Algorithms: Study on Fairness and Stability

This document is a study of fairness, feedback and stability notions of different packet transmission algorithms. We start the discussion with defining two scalable control algorithms namely primal and dual algorithm. We discuss the dual algorithm model and then understand the fair dual algorithm. Further, we discuss different notions of fairness under fair dual algorithm those correspond to TCP and RCP congestion control protocols. Feedback parameters are analyzed in each of these fairness algorithms and thus their stability is studied.
Solving Infinite Games in the Baire Space

Infinite games (in the form of Gale-Stewart games) are studied where a play is a sequence of natural numbers chosen by two players in alternation, the winning condition being a subset of the Baire space ωω. We consider such games defined by a natural kind of parity automata over the alphabet ℕ, called ℕ-MSO-automata, where transitions are specified by monadic second-order formulas over the successor structure of the natural numbers. We show that the classical Büchi-Landweber Theorem (for finite-state games in the Cantor space 2ω) holds again for the present games: A game defined by a deterministic parity ℕ-MSO-automaton is determined, the winner can be computed, and an ℕ-MSO-transducer realizing a winning strategy for the winner can be constructed.
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views. Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but the computation cost is too expensive. Then deep-learning-based methods have gained prevalence due to the excellent performances and lower computation. However, these methods ignore the mismatch between the CNN's \textbf{local} feature extraction capability and the sinogram's \textbf{global} characteristics. To overcome the problem, we propose \textbf{Du}al-\textbf{Do}main \textbf{Trans}former (\textbf{DuDoTrans}) to simultaneously restore informative sinograms via the long-range dependency modeling capability of Transformer and reconstruct CT image with both the enhanced and raw sinograms. With such a novel design, reconstruction performance on the NIH-AAPM dataset and COVID-19 dataset experimentally confirms the effectiveness and generalizability of DuDoTrans with fewer involved parameters. Extensive experiments also demonstrate its robustness with different noise-level scenarios for sparse-view CT reconstruction. The code and models are publicly available at https://github.com/DuDoTrans/CODE
A Gaussian Process-Based Ground Segmentation for Sloped Terrains

A Gaussian Process $\left( {\mathcal{G}\mathcal{P}} \right)$ based ground segmentation method is proposed in this paper which is fully developed in a probabilistic framework. The proposed method tends to obtain a continuous realistic model of the ground. The LiDAR three-dimensional point cloud data is used as the sole source of the input data. The physical realities of the data are taken into account to properly classify sloped ground as well as the flat ones. Furthermore, unlike conventional ground segmentation methods, no height or distance constraints or limitations are required for the data for the lack of access to the physical behavior of the ground. Furthermore, a density-like parameter is defined to handle ground-like obstacle points in the ground candidate set. The non-stationary covariance kernel function is used for the Gaussian Process, by which Bayesian inference is applied using the maximum A Posteriori criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results show the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process-based ground segmentation methods.
Health Monitoring of Industrial machines using Scene-Aware Threshold Selection

This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained using log-melspectrogram representations of the sound signal. In classification, our hypothesis is that the reconstruction error computed for an abnormal machine is larger than that of the a normal machine, since only normal machine sounds are being used to train the autoencoder. A threshold is chosen to discriminate between normal and abnormal machines. However, the threshold changes as surrounding conditions vary. To select an appropriate threshold irrespective of the surrounding, we propose a scene classification framework, which can classify the underlying surrounding. Hence, the threshold can be selected adaptively irrespective of the surrounding. The experiment evaluation is performed on MIMII dataset for industrial machines namely fan, pump, valve and slide rail. Our experiment analysis shows that utilizing adaptive threshold, the performance improves significantly as that obtained using the fixed threshold computed for a given surrounding only.
Is Speech Emotion Recognition Language-Independent? Analysis of English and Bangla Languages using Language-Independent Vocal Features

—A language agnostic approach to recognizing emotions from speech remains an incomplete and challenging task. In this paper, we used Bangla and English languages to assess whether distinguishing emotions from speech is independent of language. The following emotions were categorized for this study: happiness, anger, neutral, sadness, disgust, and fear. We employed three Emotional Speech Sets, of which the ﬁrst two were developed by native Bengali speakers in Bangla and English languages separately. The third was the Toronto Emotional Speech Set (TESS), which was developed by native English speakers from Canada. We carefully selected language-independent prosodic features, adopted a Support Vector Machine (SVM) model, and conducted three experiments to carry out our proposition. In the ﬁrst experiment, we measured the performance of the three speech sets individually. This was followed by the second experiment, where we recorded the classiﬁcation rate by combining the speech sets. Finally, in the third experiment we measured the recognition rate by training and testing the model with different speech sets. Although this study reveals that Speech Emotion Recognition (SER) is mostly language-independent, there is some disparity while recognizing emotional states like disgust and fear in these two languages. Moreover, our investigations inferred that non-native speakers convey emotions through speech, much like expressing themselves in their native tongue.
Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence-optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.
Effects of Graph Network Connections on The Efficiency of Quantum Annealing

Graph structure of quantum spin networks plays an essential role in applying quantum annealing (QA). The Ising model is the typical choice to describe the interactions between the spins in the networks. Here, we explore the interplay of different interaction types between the spins and the structure of the underlying network on the performance of QA. Specifically, we consider the Heisenberg XY and antisymmetric anisotropic exchange interaction models, in addition to the usual Ising Model, in graph structures ranging from linear chains to complete graphs with different connectivities. We find that the QA performance of the Ising model shows less variation with the change of graph structure among different interaction types. The Heisenberg XY model suffers from significant performance losses with increasing connections beyond linear chains, which we attribute to increasing entropy built-up. Anisotropic exchange interactions turn out to be an impractical choice for complete graphs due to the diminishing energy gap during annealing. Furthermore, we investigate the effect of inhomogeneous couplings and reveal a trade-off between coupling strength and performance, especially significant for the case of the anisotropic exchange model.
A self-consistent hybrid model of kinetic striations in low-current argon discharges

A self-consistent hybrid model of standing and moving striations was developed for low-current DC discharges in noble gases. We introduced the concept of surface diffusion in phase space (r, u) (where u denotes the electron kinetic energy) described by a tensor diffusion in the nonlocal Fokker–Planck kinetic equation for electrons in the collisional plasma. Electrons diffuse along surfaces of constant total energy ɛ = u − eφ(r) between energy jumps in inelastic collisions with atoms. Numerical solutions of the 1d1u kinetic equation for electrons were obtained by two methods and coupled to ion transport and Poisson solver. We studied the dynamics of striation formation in Townsend and glow discharges in argon gas at low discharge currents using a two-level excitation-ionization model and a ‘full-chemistry’ model, which includes stepwise and Penning ionization. Standing striations appeared in Townsend and glow discharges at low currents, and moving striations were obtained for the discharge currents exceeding a critical value. These waves originate at the anode and propagate towards the cathode. We have seen two types of moving striations with the two-level and full-chemistry models, which resemble the s and p striations previously observed in the experiments. Simulations indicate that processes in the anode region could control moving striations in the positive column plasma. The developed model helps clarify the nature of standing and moving striations in DC discharges of noble gases at low discharge currents and low gas pressures.
Observation of re-entrant correlated insulators and interaction driven Fermi surface reconstructions at one magnetic flux quantum per moir\'e unit cell in magic-angle twisted bilayer graphene

1. ICFO Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, 08860, Spain 2. Department of Physics, Princeton University, Princeton, New Jersey 08544, USA 3. Laboratoire National des Champs Magnétiques Intenses, Univ. Grenoble Alpes, UPS-INSAEMFL-CNRS-LNCMI, 25 avenue des Martyrs, 38042 Grenoble, France 4. Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan 5. International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
Urine Microscopic Image Dataset

Urinalysis is a standard diagnostic test to detect urinary system related problems. The automation of urinalysis will reduce the overall diagnostic time. Recent studies used urine microscopic datasets for designing deep learning based algorithms to classify and detect urine cells. But these datasets are not publicly available for further research. To alleviate the need for urine datsets, we prepare our urine sediment microscopic image (UMID) dataset comprising of around 3700 cell annotations and 3 categories of cells namely RBC, pus and epithelial cells. We discuss the several challenges involved in preparing the dataset and the annotations. We make the dataset publicly available.
Weak Approximation for del Pezzo Surfaces of Low Degree

We prove, via an “arithmetic surjectivity” approach inspired by work of Denef, that weak weak approximation holds for surfaces with two conic fibrations satisfying a general assumption. In particular, weak weak approximation holds for general del Pezzo surfaces of degrees $1$ or $2$ with a conic fibration.
STATUS REVIEW ON SPENT DESALINATION MEMBRANE MANAGEMENT

Abstract Abstract Abstract Abstract With growing use and market of membrane technologies in desalination and water purification, disposal of used and spent membrane elements is going to be a serious issue for water industry in future. Keeping this in mind, various schemes/protocols can be planned for spent membrane management. Advanced oxidation processes for aqueous organic waste handling are known and are being practiced in industries. Exploratory studies on these lines for handling spent membrane management are planned and primary exploratory experiments based on hydro thermal processes have been initiated. The processes for waste treatment and conditioning practiced in industry need to be revisited in the light of technical developments.
Effects of context, complexity, and clustering on evaluation for math formula retrieval

There are now several test collections for the formula retrieval task, in which a system's goal is to identify useful mathematical formulae to show in response to a query posed as a formula. These test collections differ in query format, query complexity, number of queries, content source, and relevance definition. Comparisons among six formula retrieval test collections illustrate that defining relevance based on query and/or document context can be consequential, that system results vary markedly with formula complexity, and that judging relevance after clustering formulas with identical symbol layouts (i.e., Symbol Layout Trees) can affect system preference ordering.